A Parametric Product Design Framework for the Development of Mass Customized Head/Face (Eyewear) Products

applied sciences

Article A Parametric Product Design Framework for the Development of Mass Customized Head/Face (Eyewear) Products

Xiaobo Bai 1,*, Omar Huerta 2 , Ertu Unver 2 , James Allen 2 and Jane E. Clayton 2

1 School of Art and Design, Xi’an University of Technology, Xi’an 710048, China 2 Department of Architecture and 3D Design, School of Art, Design and Architecture, University of Huddersﬁeld, Huddersﬁeld HD1 3HB, UK; [email protected] (O.H.); [email protected] (E.U.); [email protected] (J.A.); [email protected] (J.E.C.) * Correspondence: [email protected]

Abstract: This study led to the development of a parametric design method for mass-customised head/face products. A systematic review of different approaches for mass customization was conducted, identifying advantages and limitations for their application to new product development. A parametric modelling algorithm of a 3D human face was developed using selected scanned 3D head models. The algorithm was developed from a set of measurable and adjustable parameter points related to the facial geometry. These parameters were deﬁned using planimetry. Using the assigned parameter values as input, the parametric model generated 3D models of a human face that served as a reference for the design of customized eyewear. The current challenges and opportunities

of mass customized head/face products are described, along with the possibilities for new parametric product design approaches to enable rapid manufacturing and mass customization. This study also

Citation: Bai, X.; Huerta, O.; Unver, explored whether a new parametric design framework for mass customization could be effectively E.; Allen, J.; Clayton, J.E. A implemented as an early-stage new product development strategy for head/face products. Parametric Product Design Framework for the Development of Keywords: mass customization; product design; parametric design Mass Customized Head/Face (Eyewear) Products. Appl. Sci. 2021, 11, 5382. https://doi.org/10.3390/ app11125382 1. Introduction Science and technology continue to rapidly develop, with businesses moving faster Academic Editor: toward smart manufacturing during the so-named 4th Industrial Revolution. There is a Nikolaos Papakostas constant demand for specialized products, where customization is becoming prevalent, and where standard available tools are applied in a ﬂexible but effective way within the Received: 27 April 2021 product design process. Market demand is constantly changing, leaving the industry and Accepted: 7 June 2021 Published: 10 June 2021 designers with the task to develop new and ﬂexible approaches to meet users’ needs. For example, wearable products and automotive components are particularly a good exemplar

Publisher’s Note: MDPI stays neutral of how small-batch and customized production has been used as a strategy to quickly with regard to jurisdictional claims in deliver personalized products to gradually replace mass-produced products [1,2]. Usually, published maps and institutional afﬁl- customization is applied during the manufacturing of a product by adjusting certain iations. product elements rather than restarting the entire production to deliver a new product variation. In this context, customization allows enterprises to adapt to the always-changing customer needs and act accordingly, hence offering a key indicator to measure sustainable economic development. Product personalization and design for Mass Customization (MC) have been used by Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. product designers as strategies to adapt to the always changing users’ needs. MC calls for This article is an open access article ﬂexibility and quick responsiveness in a context with ever-changing factors (i.e., people’s distributed under the terms and needs, market, and processes and technology) to provide customer satisfaction through conditions of the Creative Commons an increased product variety with a limited effect in cost and lead time, contrary to the Attribution (CC BY) license (https:// old paradigm of mass production (i.e., mass-produced standardized products). MC has creativecommons.org/licenses/by/ had a great appeal to industry and academia during recent years, and some frameworks 4.0/). have been proposed to effectively achieve MC through design. The implementation of MC

Appl. Sci. 2021, 11, 5382. https://doi.org/10.3390/app11125382 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 5382 2 of 20

frameworks during the conceptual design and preliminary development stages has led to the adaptive concept of Design for Mass Customization (DFMC) [3]. Design as discipline and thinking process has an important role at the fuzzy front end (FFE) of innovation and at new product development [4]. Whether or not MC is implemented as a key element in the innovativeness of a product or service, it is perhaps at the FFE of innovation when it can be considered as part of the product strategy [3]. Therefore, this work approaches the MC framework from the Product Design perspective, and at an early stage of the New Product Development (NPD) process [5]. In product design, customization has played an important role in the good fit and comfortability of wearable products, hence its use as a way to improve product ergonomics. Some studies have shown that MC capability can be developed using modular systems and innovative product designs and processes [6–8]. Additionally, MC has allowed large companies to integrate customers into the design process [9]. However, this process relies on the exploration and visualization of the different existing eyewear models to assist customers in the collaborative process. On the other hand, smaller eyewear companies approach customization through the implementation and continuous optimization of flexible tools within the manufacturing process [10]. This study proposes an MC design framework based on a comparative study of current 3D data acquisition methods and a parametrized design approach that can be used at the early stages of eyewear design to provide a more attainable MC and co-design experience. In this work, a typical eyewear design is used as a case study to explore the development of the aforementioned MC framework. This framework is developed from a product design perspective, trying to implement such workflow at the FFE stage of NPD, where flexibility in the creative process is a key factor for the product designer, as well, as comfortability, good fit, and customization are Unique Selling Points (USPs) and decision-making factors for the customer.

2. Related Works 2.1. Mass Customisation, Personalisation and Design Within the context of this study, MC is referred as the use of flexible computer-aided design and manufacturing systems to produce customized products for mass production purposes. One of the main objectives of an MC strategy is to obtain the low unit costs that benefit from a mass production process and the flexibility of customization. MC gives customers and industry the chance to interact across the different stages of the NPD process, allowing the manufacturer to satisfy the customer’s specific needs [11]. On the other hand, personalization as an industrial strategy approaches each customer satisfaction as an individual entity. Therefore, product differentiation occurs at the same individual level, whereas customization differentiates products for market segments. Making profit and gaining competitiveness through product differentiation is not new; companies are continuously using market segmentation or customer-centred personalization strategies to remain competitive. Different MC methodologies and strategies have been developed, and different levels of customization have been proposed [12]. The combination of these frameworks has led to eight generic levels of MC, ranging from pure customization (individually designed products) to pure standardization, where design (from a project point of view) appears at the top of the levels [11]. Furthermore, MC can be implemented at different stages along the value chain and product development process, from the total customization of a product’s design, manufacturing, assembly, delivery and even sale, up to the simplest interventions/adaptations of products by customers. According to the four levels of customization proposed by Gilmore and Pine, a collaborative and transparent customization should foster designers’ dialogue with customers, thereby products can be easily adapted to individual user needs [13]. Moreover, Design Customization (DC) refers to a collaborative project (i.e., design), manufacturing and Appl. Sci. 2021, 11, 5382 3 of 20

delivery of products according to individual user needs and preferences (i.e., intrinsic and extrinsic). In terms of design, customization classifies customers into market segments based on the needs identified by market analysis. Consequently, customers within a designated seg- ment receive a similar or parameter-based customized product. This customized product keeps the pre-configured elements stable (i.e., configuration mechanism, product archi- tectures, basic modules). Therefore, the principle of Design for Mass Customization is to be able to create many product variations through the configuration of modular elements using the commonality embedded within the designed product platform so that these modular elements can be reused among the different product families [5,11]. On the contrary, in Design for Mass Personalization (DFMP), industry works along with customers to create products that may be completely new to fulfil customer needs within a given budget and time constraints. In this case, personalization enables product differentiation beyond the original set of product offerings. Nevertheless, it remains a difficult task to design a product intended to be mass-customized. Customization implies the use of fixed and predefined product elements, whereas personalization allows changes to the basic design and product features. The flexibility of a product design in terms of its adaptability to changes is essential for personalization and customization as the main objective of product design and is the meet-up of customer needs [14]. Customization allows users to select from a predetermined set of already proven elements that will logically configure a product. Designers define in advance the product elements that could potentially be used to create a different range of products based on user/customer needs. This set of elements is based on qualitative and quantitative requirements carefully obtained from users or other stakeholders [15–17]. Therefore, the product design process plays a fundamental role in determining the elements that will be common between the building blocks that customers will use to create product predictably. On the other hand, personalization is less predictable, as it gives users the chance to foresee the product result within the company. Hence, the importance of an innovative design framework that considers the user experience of designers to carry out with efficiency the DFMC or DFMP process.

2.2. Customer-Driven Design and Manufacture at the Core of MC Systems Customers’ preferences are critical to product development [18]. To enable and fully support the development of a successful MC system, the main elements of a manufacturing strategy along with the user/customer preferences need to be considered [11]. These are included within the following: • Agile manufacturing is the ability to thrive and prosper in a competitive environment of continuous and unanticipated change in order to respond quickly to rapidly changing markets driven by customer-based valuing of products [19]. Here, it is important to truly capture user preferences and requirements based on attitudes and emotions, which may imply further challenges [17]. • Supply-chain management concerns the coordination of resources and the optimisa- tion of activities across the value chain to obtain competitive advantages [20,21] • Customer-driven design and manufacture at the core of MC systems [22] “actively considers the market trends and individual customer requirements during the design, manufacturing and delivery of the products”. Some authors call this practice “One- of-a-Kind Production” (OKP). The application of customer-driven practices in MC systems aims at providing the conditions for the customer to take part in the design process, and for the company to build an infrastructure to develop such customer- driven products, hence the relevance of defining customer preferences though a robust design requirement specification [15]. • Lean manufacturing is an efficient way to satisfy customer needs while giving pro- ducers a competitive edge [23]. MC production addresses four elements of lean Appl. Sci. 2021, 11, 5382 4 of 20

production: product development, the chain of supply, shop floor management, and after-sales services [24]. The main enabling technologies supporting MC can include additive manufacturing (AM) [25], computer numeric control (CNC) [11], flexible manufacturing systems (FMS) [26], computer-aided design (CAD) [27], computer-aided manufacturing (CAM), computer-integrated manufacturing (CIM), and electronic data interchange (EDI) [28]. The use of these technologies is justified by their inherent flexibility, and their capability to alter the economies of manufacturing and remove barriers to product variety. Additionally, these technologies allow full exploitation of the fundamental MC attributes such as agility and flexibility.

2.3. The Ergonomic-Anthropometric-Good Fit Relationship Most of the eyewear products that can be found in the market are mass-produced products. Their design follows current trends and “standard” anthropometric dimensions to make the final product suitable to user needs. A good fit–comfortability relation is an important decision-making factor in the purchase process for this type of product. However, age, head asymmetry, and evolutive body size change are usually not considered in the design of these mass-produced products. Some studies have already made clear the head and face size differences between race, gender, and age. For example, the head size changes at different ages have been analyzed through a long-term observation [29]. Face morphology and size differences between genders, races and populations have also been described based on statistical principles [30] and comparison studies [31]. Ball et al., on the other hand, used the data from the North American and European Caucasians (CAESAR) and the SizeChina human body databases, to analyze and compare the head size differences between Caucasian and Chinese people [32]. Hanson et al. found many body size changes on workers after collecting 3D data of 367 volunteers’ bodies and comparing results with original ergonomic data [33]. This ergonomic–anthropometric good fit relationship is a field that has intrigued many scholars. Some have taken an approach of statistically analyzing anthropometric data [34–36] to generate tools that designers could use to establish product size systems [37]. On the other hand, a parametric-based system has also been used to cluster anthropometric head-size data sets into four major groups, providing with this the basis for a personalized bicycle helmet design [38]. Nevertheless, due to the complexity and diversity of human head morphology, it remains difficult to design personalized and customized products within a flexible design framework.

2.4. 3D Scanning Technology New design and manufacturing technologies such as generative design, 3D scanning, and additive manufacturing have expanded the possibilities for companies that are dealing with MC or personalized products [11,39,40]. 3D Scanning technologies have offered a more reliable and efficient method for obtaining accurate 3D measurements and morphological data to design more appropriate products, where a good fit is of paramount importance [41]. For example, Luximon et al. used collected 3D data of men’s and women’s heads from different regions of China to provide an accurate representation of a 3D head and face shape with corresponding anatomical features for product design and evaluation [42,43]. Zheng et al. also used 3D scanning to develop a new measuring system [36]. However, due to the complexity the human body presents, it is difficult to accurately represent its morphology with the current diversity of parameters [33,35,44].

2.5. Wearable Product Design The appropriate definition of human body parameters for product design is particularly important for products that require a good fit and comfort such as helmets or earphones [45,46]. Design methods have linked these parameters to the human body’s 3D models to achieve a good product fit [47,48]. CAD software packages have allowed designers to create products based on parametric systems. These systems can refer directly to a 3D Appl. Sci. 2021, 11, 5382 5 of 20

measured human body to explore the design of the product following real-time ergonomic and anthropometric parameters being used [49]. For example, Rhinoceros 3D software is a commercial 3D modelling software developed by Robert McNeel & Associates that is widely used. The software offers a tool to approach the whole design process, from design sketch to actual product. Rhinoceros 3D geometry uses Non-Uniform Rational B-Splines (NURBS) producing mathematically precise curves and freeform surfaces. The software can accurately generate 3D models for design, development rendering, animation, engineering drawing, analysis and evaluation, prototyping, and production. Moreover, Rhinoceros’ plug-in Grasshopper allows the software to generate parametric models based on visual programming language, therefore offering a flexible parametric design application based on visual programming and data flow [50]. Other NURBS-based concept modelling packages include Alias, ICEM surf from Dassault Systèmes, and many other polygonal-based tools such as Blender, Seamless3d and Maya. Some of these 3D software packages offer visual programming tools (e.g., Grasshopper and Dynamo). These visual programming tools are popular among designers, architects, engineers, artists, and manufacturers, due to the quick iterative processes used with minimal knowledge of programming. As a typical wearable product, eyewear has also attracted the attention of some researchers. Some have used 3D scanning models and the principal-component analysis method to reduce the complexity of 3D data, as well as the definition of key partial feature points that correlate between the different parameters using a Kringing nonlinear regression method, consequently generating a new 3D face model that was used to create personalized eyewear designs [51]. On the other hand, virtual reality and semantic parameters have also been used to bring real-time design interactivity between users and designers [52]. In addition, Weihua Lu has used the emotion expressed in the voice as a 3D modelling or design parameter that could be used to personalize eyewear [53]. Some of the work reviewed focuses on the virtual trial/wearing of eyewear and the apparent relation between product size and the human face, limiting the work to the visual perception of good fit or to the design of eyewear based on the acquisition of statistical data from face size, consequently focusing on the predetermined appearance or aesthetic features of eyewear, but not on the comfort and the meeting of individual consumer preferences. Eyewear products may need to be worn every day, and comfortability is a concern for consumers. However, the design and manufacturing of eyewear is a long and complex process, and the comfort of this large-scale manufactured product does not always meet the needs of some consumers. Therefore, research on personalized design of eyewear becomes valuable for both consumer and industry, potentially improving many related aspects of the product such as aesthetics and ergonomics, whilst taking advantage of current manufacturing technologies, materials, and methods for eyewear products. In this paper the authors propose a new framework to approach the creation of eyewear, based on algorithmic and parametric CAD software to provide an effective design tool that allows the customization or personalization of this type of product. This method attempts to obtain the user’s head size through two orthogonal photos to then establish a 3D parametric head model to build a fully customizable eyewear 3D model. This work covers a preliminary evaluation of three scanning methods used to capture head/face features (i.e., photogrammetry, somatosensory and laser scanning) and the development of a parametric-based strategy for the design of personalized eyewear.

3. Methodology 3.1. Parametric Modelling Strategy An overview of the methodology used is presented in Figure 1. In the current work, by collecting three-dimensional scan data of the human head, and based on principal component analysis and the K clustering algorithm, the head model was divided into several regions to generate a three-dimensional model, to then establish a head model database. This method is based on work previously published [43,44,52,54]. According to Appl. Sci. 2021, 11, 5382 the feature points of the head, the 3D scanning model was sliced/divided,6 of 20 and the contour line was then extracted. By using the interpolation method, where 24–26 points were used to obtain an interpolation curve similar to the contour line, the hierarchical inter- line was then extracted. By using the interpolation method, where 24–26 points were used polationto curve obtain anwas interpolation used to curvecreate similar the tohead the contour parametric line, the model. hierarchical Orthogonal interpolation photographs were thencurve used was usedto develop to create a the two head-dimensiona parametric model.l (2D) Orthogonal image-based photographs reading were system to de- terminethen head/face used to develop feature a two-dimensional points. These (2D) 2D image-based data points reading obtained system to determinefrom the orthogonal head/face feature points. These 2D data points obtained from the orthogonal images images were were transformed transformed into 3D into data 3D that data was used that as was an input used to create as an the input corresponding to create the corre- spondinghuman human head parametrichead parametric model. Following model. this, Following the parametric this 3D, the model parametric was used as 3D a model was used as referencinga referencing system system where the where parametric the eyewear parametric was designed. eyewear Finally, was using design 3D printing,ed. Finally, using the designed eyewear product was prototyped to evaluate comfort and ﬁt. 3D printing, the designed eyewear product was prototyped to evaluate comfort and fit.

Figure 1. Parametric modelling strategy. Figure 1. Parametric modelling strategy. 3.2. Head/Face Scanning 3.2. Head/FaceBefore Scanning developing the 3D parametric model of the head, 3D scanning methods were evaluated to determine their suitability for this study. Convenience, speed, and accuracy Beforewere useddeveloping as the main the evaluation 3D parametric parameters. model of the head, 3D scanning methods were

evaluated3.2.1. to Photogrammetry determine their suitability for this study. Convenience, speed, and accuracy were used asPhotogrammetry the main evaluation is based on the parameters. principle of a camera imaging system; it uses multiple photos from different angles to reconstruct a 3D model. This usually includes the following 3.2.1. Photogrammetrysteps: image characteristics extraction, depth restoration, point cloud registration, and deep integration [55]. This method is mainly used by surveyors, architects, engineers, and Phocontractorstogrammetry to create is topographic based on maps, the meshes,principle point of clouds a camera or drawings. imaging For this system; method, it uses multiple photos56 high-resolution from different images wereangles taken to from reconstruct 360◦ whilst thea 3D user model. remained This still in usually front of a includes the followingplain steps: background image and characteristics without any objects extraction, to obstruct the depth view. Therestoration, high-resolution point images cloud registra- were then processed using a photogrammetry application to generate a human head 3D tion, andmodel deep (see integration Figure2). [55]. This method is mainly used by surveyors, architects, engineers, and contractors to create topographic maps, meshes, point clouds or drawings. For this method, 56 high-resolution images were taken from 360° whilst the user remained still in front of a plain background and without any objects to obstruct the view. The high-resolution images were then processed using a photogrammetry application to generate a human head 3D model (see Figure 2). Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 20 Appl. Sci. 2021, 11, 5382 7 of 20

Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 20

Figure 2. Photogrammetry image. FigureFigure 2. Photogr 2. Photogrammetryammetry im image.age. 3.2.2. Somatosensory Camera Scan 3.2.2. Somatosensory Camera Scan 3.2.2. SomatosensoryA somatosensory Camera controller, Scan such as Kinect from Microsoft(Redmond, WA,USA), A somatosensory controller, such as Kinect from Microsoft(Redmond, WA, USA), comprises three lenses (RGB), an infrared transmitter, and an infrared receiver. This A somatosensorycomprises three lenses controller, (RGB), such an infrared as Kinect transmitter, from and Microsoft an infrared(Redmond, receiver. WA,USA) This , comprisessystem three has lenses been used (RGB), as a low-cost lowan - infraredcost (under (under transmitter, $200) $200) scanning scanning and alternative.alt ernative. an infrared The Kinect receiver. can This simultaneously obtain the depth image and RGB image from the object, consequently system hassimultaneously been used obtain as a thelow depth-cost image (under and $200) RGB image scanning from alt theernative. object, consequently The Kinect can making it possible to obtain an accurate 3D model with original colours. In this method, simultaneouslymaking it obtain possible the to obtain depth an accurate image 3D and model RGB with image original from colours. the In object this method,, consequently the thescanning scanning device device was mountedwas mounted on a tripodon a tripod while thewhile subject the su remainedbject remained still on astill platform on a plat- that making itformslowly possible that rotated slowly to 360 obtain rotated◦ during an 360 theaccurate° during capturing. the 3D capturing Scanning model . accuracy withScanning original was accuracy continuously colours. was continuously adjustedIn this method, to the scanningadjustedthe scanning device to the distance was scanning mounted to obtaindistance theon to best aobtain tripod scanning the whilebest results. scanning the su resultsbject remained. still on a platform that slowly rotated 360° during the capturing. Scanning accuracy was continuously 3.2.3. Laser Scanning adjusted3.2.3. to the Laser scanning Scanning distance to obtain the best scanning results. There are a variety of of small small and and por portabletable 3 3DD s scannerscanners used used for for different different applications. applications. TheTheirir accuracy is generally high although not as high as Coordinate Measuring Machines 3.2.3. Laser(CMM). Scanning Due Due to to their their accuracy, accuracy, light light weight, weight, and and portability, portability the Space, the Space Spider Spider scanner scanner (Artec There(Artec3D, are Luxembourg) 3D,a variety Luxembourg) wasof small selected was and selectedfor por evaluationtable for ev 3 (seeaDlua s Figurectionann (seeers3). Aused Fig quickure for 3). scan different A of quick a small scan applications. object of a Their accuracysmallwithenough object is generally with details enough would high details normally although would take normallynot 5–10 as min. high take Several 5as–10 Coordinate min 3D. full Several head/face Measuring 3D full scans head/face were Machines scanscaptured, were and captured resulting, and scans resulting were thenscans validated were then against validated the localizationagainst the locali of keyzation feature of (CMM). Due to their accuracy, light weight, and portability, the Space Spider scanner keypoints feature and measurementspoints and measurements taken from orthogonaltaken from photographs.orthogonal photographs. (Artec 3D, Luxembourg) was selected for evaluation (see Figure 3). A quick scan of a small object with enough details would normally take 5–10 min. Several 3D full head/face scans were captured, and resulting scans were then validated against the localization of key feature points and measurements taken from orthogonal photographs.

Figure 3. Scanning tool.

3.3. Determining Determining the the Head Head/Face/Face Required Dimensions To determine the required key feature points for the eyewear design design (Fig (Figureure 44),), dif-dif- ferentferent types of eyewear products were analy analyzedzed and key featurefeature points were correlated toto head/face measurem measurementsents as as shown shown in in Figure Figure 5a,b.a,b. The selectionselection ofof thesethese keykey pointspoints isis based on the feature points suggested in the literature [4343,5555], and they werewere combinedcombined Figure 3. Scanning tool. with points shown in Figure 44 andand TableTable1 1to to determine determine the the relevant relevant feature feature points points for for the the eyewear design design.. 3.3. Determining the Head/Face Required Dimensions To determine the required key feature points for the eyewear design (Figure 4), different types of eyewear products were analyzed and key feature points were correlated to head/face measurements as shown in Figure 5a,b. The selection of these key points is based on the feature points suggested in the literature [43,55], and they were combined with points shown in Figure 4 and Table 1 to determine the relevant feature points for the eyewear design. Appl. Sci. 2021Appl., 11 Sci., x2021 FOR, 11 PEER, 5382 REVIEW 8 of 20 8 of 20 Appl. Sci. 2021, 11, x FOR PEER REVIEW 8 of 20

Figure 4. Dimensioning of eyewear. FigureFigure 4. 4.DimensioningDimensioning of eyewear.of eyewear.

Figure 5. (a) Orthogonal photo; (b) Feature points for head measurement. Figure 5. (a) Orthogonal photo; (b) Feature points for head measurement.

The relationship between eyewear size and the head feature points was stablished Figure throug5. (a) Orthogonalh a simple photo; correlation (b) Feature to facilitate points forthe head control measurement. of parameters using Grasshopper. The following eyewear frame considerations were made: The(1). relationship If the frame betweenis too narrow eyewear and the size distance and the between head featurethe legs pointsis too close, was thestablished lat- througheral orbital a simple tissues correlation will be comp to facilitateressed, causing the control symptoms of parameters such as head using distention Grasshopper. and Theheadache. following Therefore, eyewear efyewearrame considerations frame size and were head made: size should have the following relationship.(1). If the frame is too narrow and the distance between the legs is too close, the lateral orbital tissues will be compressed, causing symptoms such as head distention and a0.5 a  B  B  B (1) headache. Therefore, eyewear frame1 size and 2 head 2 3size 4should have the following relationship.The angle of template should be calculated according to the Formula (2). 1 BBBB   a  tan 2 4 3 1 (2) 7 a1+0.5BB56 a 2  B 2 + B 3 + B 4 (1)

The angle of template should be calculated according to the Formula (2). −1 BBBB+ + − a  tan 2 4 3 1 (2) 7 BB56+ Appl. Sci. 2021, 11, 5382 9 of 20

Table 1. Eyewear feature point for dimensioning.

Head Feature Points Related Eyewear Size Parameters Size Name Explanation of Size to the Eyewear Size (Figure4) (Figure5b)

a1 Eye size Indicates the width of the lens 6,8,9

a2 Bridge width Connection section of lens 4,5,6 Decide where to contact the a Nose angle 4,5,12,13,14 3 eyewear and nose Height of glasses, determined by a Lens height 3,5,8,10,11 4 appearance Height of eyeglasses frame a5 Height of frame section, determined by appearance Distance from eyewear frame a The temple size 7,22 6 to ear The angle between the temple and a Angle of temple 7,22 7 the symmetrical plane of head

a8 Curvature of eyewear frame Curvature of the eyewear frame 3,4,6,7,8,9,22 Thickness of eyewear frame a9 Thickness of frame section, determined by appearance

a10 Back leg angle of eyewear Back leg angle of glasses 22,23

a11 Angle of eyewear frame Angle between frame and temple 4,5,7,11

a12 Thickness of temple Thickness of temple

The relationship between eyewear size and the head feature points was stablished through a simple correlation to facilitate the control of parameters using Grasshopper. The following eyewear frame considerations were made: (1). If the frame is too narrow and the distance between the legs is too close, the lateral orbital tissues will be compressed, causing symptoms such as head distention and headache. Therefore, eyewear frame size and head size should have the following relationship.

a1 + 0.5a2 ≥ B2 + B3 + B4 (1)

The angle of template should be calculated according to the Formula (2).

−1 B2 + B4 + B3 − B1 a7 ≈ tan (2) B5 + B6 Additionally, to ensure comfortable wearing, the a8 radian of the eyewear frame is determined by the curve of the face, where a8 should be at a certain distance from the feature points 3, 4, 6, 7, 8, 9, 22. (2). When the length from the hinge of the frame to the point above the ear is too long, and the bending contour of the frame does not match the outline of the ear base, it is difﬁcult for the frame to remain stable and in place. Therefore, the length of the temples can be calculated according to: a6 ≈ B5 + B6 (3) and the back leg angle of eyewear can be calculated using:

−1 B8 a10 ≈ tan (4) B9 Appl. Sci. 2021, 11, 5382 10 of 20

(3). The height and thickness of the front end of the nasal bone play an important determinant for the stability of the nose pads of the glasses. The shape of the nose pads varies greatly from one individual to another, so the supporting effect of the glasses varies greatly. This was considered through:

−1 B9 − B2 a3 ≈ tan (5) B10

a2 ≈ 2B2 (6) The stability of the glasses also requires a stable triangular relationship between the frame and the bridge of the nose, and this was considered through:

2 2 2 −1 B11 + B13 − B12 a11 ≈ cos (7) 2B11B13 Optical centre and interpupillary distance of the lens is also an important functional consideration. The position of the lens needs to be determined according to the position of the pupil, and the inner and outer corners of the orbit. The eyelid, and the cheekbones are used to determine the size of the lens.

a1 ≈ B3 (8)

a4 ≤ B14 (9)

In Formulas (1)–(9), points a1–a11 correspond to eyewear size parameters as shown in detail in Table1 and Figure4. Dimensions are represented by B1–B14 points as shown in Figure5b. The feature points with serial numbers 1–23 are represented in the coordinate system as (xn, yn, zn), n = 1, 2 ··· 23. Then

B1 = |x7 − x4|, B2 = |x5 − x4|, B3 = |x8 − x9|, B4 = |x5 − x9|, B5 = |y7 − y8|, B6 = |y7 − y22|, B7 = |y22 − y23|, B8 = |x22 − x23|, B9 = |x13 − x12|, B10 = |z4 − z12|, q q 2 2 2 2 (10) B = (z − z ) + (y − y ) ; B = (z − z ) + (y − y ) , 11 4 22 4 22 12 22 10 22 10 q 2 2 B = (z − z ) + (y − y ) , B = |z − z |. 13 4 5 4 5 14 3 11

To correlate the aforementioned points to human head measurements orthogonal pictures were used. Before taking the pictures, small location circles were attached to the face. These markers (Figure5a) were used to calibrate the feature points. Additionally, auxiliary calibration points were positioned at the intersection of horizontal and vertical lines. Pictures were taken with the head parallel to the Frankfurt plane, with the body upright and the shoulders ﬂat. Auxiliary calibration points were used to ensure head posture was kept in position in comparison to the horizontal and vertical axes. Pictures were post-processed (see Figure5a), to sharpen the edges of the image. The edge contour of the head was then extracted, and the photos were gridded according to the calibrated feature points. Then the coordinate origin was determined, and the plane coordinate system was established. According to the plane coordinate system, the dimension of each feature point in the photo was obtained. The outline of each marker was traced, and its real dimension was obtained using:

n ∑i=1 Di n H0 = Hp × (11) D0 Appl. Sci. 2021, 11, 5382 11 of 20

where H0 is the real size measurement, D0 is the measurement of the small circles, and n is the number of small circles measured and the picture. The eyewear feature points and their correlation to the human head are given in Table1. The correlated points obtained from the analyzed eyewear and the relevant human head anatomy (Figure5b) were used to accurately create a parametric 3D model of a human head. Considering the human head morphology, several planes were used to divide the 3D model (see Figure6) in order to transform it into a 3D coordinate system that was used as a foundation for the parametric modelling. This coordinate system was composed of Appl. Sci. 2021, 11, x FOR PEER REVIEW18 planes at points 2~7, 10~15, 17~20 and 22; and another one at the midpoint between11 of 20 3 and 4. Due to the high level of detail around the eye, this area was divided in more detailed sections to facilitate the accuracy of the measurements. The 3D coordinate system has a vertical plane passing through point 4, a horizontal plane passing through point 6 and theanother curve on plane the passing plane above through point point 4 is 22. type The A, intersection the shape of of these the curve three planeson the isplane calculated be- Appl. Sci. 2021, 11, x FORtween PEER REVIEWpoints 4 ~13 is type B, and the curve on the plane between 15~18 is type C. 11 of 20 as the coordinate origin O.

the curve on the plane above point 4 is type A, the shape of the curve on the plane between points 4~13 is type B, and the curve on the plane between 15~18 is type C.

Figure 6. Plane location according to feature points. Figure 6. Plane location according to feature points. The curves created by the intersection of these 18 planes and the 3D head model are divided into three categories according to their shape as shown in Figure7. The shape of the curve on the plane above point 4 is type A, the shape of the curve on the plane between points 4~13Figure is type 6. Plane B, and location the curveaccording on theto feature plane points. between 15~18 is type C.

Figure 7. Type of curve.

3.4. Algorithm Development, Parametric Design, and Good Fit Test The parametrized head model shown in Figure 8 was developed using the dimen- sionsFigure obtained 7. Type fromof curve. the 3D scannedFigure 7. data.Type of The curve 3D. scanning model was segmented according to the method shown in Figure 6, and the contour lines were then extracted (see Figure3.4. Algorithm8a). 3.4.Feat Algorithmure Development, points Development, (Figure Parametric 8b) correspondParametric Design, and Design, to Goodthose and Fit described TestGood Fit previouslyTest in Figure 7. The originalThe parametrized curveThe p wasarametri head fittedz modeled using head shown a cubicmodel in B Figure-shownspline8 curvewasin Figure developed (see 8Figure was using developed 8b), theand dimensions surfaces using the dimen- wereobtained then generated.sions from obtainedthe 3D When scanned from establishing the data. 3D The scannedthe 3D parameteri scanning data. Thez modeled 3Dhead was scanning model segmented program, model according was a range segmented to ac- of thekey method dimensionscording shown correspondingto the in Figure method6, and shownto the the siin contourze Figure of head lines 6, and were thebiecto thencontourcanthus extracted lines were were (see considered Figurethen extracted8a). (see usingFeature a similar pointsFigure method (Figure8a). Feat presented8ureb) correspond points by (FigureLee et to al. those8b) [3 7correspond]. described to previously those described in Figure previously7. The in Figure original curve was ﬁtted using a cubic B-spline curve (see Figure8b), and surfaces were 7. The original curve was fitted using a cubic B-spline curve (see Figure 8b), and surfaces then generated. When establishing the parameterized head model program, a range of key were then generated. When establishing the parameterized head model program, a range of key dimensions corresponding to the size of head and biectocanthus were considered using a similar method presented by Lee et al. [37].

Figure 8. (a) Curve extraction; (b) curve fitting.

Figure 8. (a) Curve extraction; (b) curve fitting. Appl. Sci. 2021, 11, x FOR PEER REVIEW 11 of 20

the curve on the plane above point 4 is type A, the shape of the curve on the plane between points 4~13 is type B, and the curve on the plane between 15~18 is type C.

Figure 6. Plane location according to feature points.

Figure 7. Type of curve.

3.4. Algorithm Development, Parametric Design, and Good Fit Test The parametrized head model shown in Figure 8 was developed using the dimensions obtained from the 3D scanned data. The 3D scanning model was segmented according to the method shown in Figure 6, and the contour lines were then extracted (see Appl. Sci. 2021, 11, 5382 12 of 20 Figure 8a). Feature points (Figure 8b) correspond to those described previously in Figure 7. The original curve was fitted using a cubic B-spline curve (see Figure 8b), and surfaces were then generated. When establishing the parameterized head model program, a range ofdimensions key dimensions corresponding corresponding to the sizeto the of si headze of and head biectocanthus and biectocanthus were considered were considered using a usingsimilar a similar method method presented presented by Lee by et al.Lee [37 et]. al. [37].

Appl. Sci. 2021, 11, x FOR PEER REVIEW 12 of 20

FigureFigure 8. 8. (a(a) )Curve Curve extraction; extraction; (b (b) )curve curve fitting. ﬁtting.

FigureFigure 99 showsshows the the algorithm algorithm and and parametric parametric design design in in the the 3D 3D m modellingodelling s softwareoftware RhinocerosRhinoceros(McNeel(McNeel Asia, Asia, S Seattle,eattle, WA WA 98103 98103, USA) USA) with with the the visual visual programming programming tool tool Grasshopper.Grasshopper. These These tools tools were were used used to to reference reference native native Rhinoceros Rhinoceros geometries geometries and and other other inputsinputs such such as as images. images.

FigureFigure 9. 9. AlgorithmAlgorithm developed developed in in Grasshopper. Grasshopper.

ToTo crea createte the customized customized 3D 3D head head model, mode twol, two orthogonal orthogonal photos photos were were taken taken as shown as shownin Figure in Figure5a. These 5a. photosThese photos were used were to used create to a create 3D model a 3Dbased model on based frontal on facefrontal features face featuresand nose and profile. nose profile. According According to the relationship to the relationship between between the feature the pointsfeature and points the headand size, the eyewear model was obtained by using Grasshopper directly on the parametric the head size, the eyewear model was obtained by using Grasshopper directly on the model of the user’s head. parametric model of the user’s head. This parametric-based design framework was then used to adjust eyewear size and This parametric-based design framework was then used to adjust eyewear size and appearance in relation to the subject’s head anatomy. Then an iterative design process appearance in relation to the subject’s head anatomy. Then an iterative design process using Rhino and Grasshopper was used to do adjustments for comfort and good fit. The using Rhino and Grasshopper was used to do adjustments for comfort and good fit. The parametric eyewear was designed to fit users’ head size by correlating the dimensions parametric eyewear was designed to fit users’ head size by correlating the dimensions described in Table1 and the parametric 3D head model. Parametric eyewear models described in Table 1 and the parametric 3D head model. Parametric eyewear models were further detailed in Rhino 3D for 3D printing. For eyewear fitting evaluation, the were further detailed in Rhino 3D for 3D printing. For eyewear fitting evaluation, the entire customization process was followed for 10 different users, and the resulting eyewear entire customization process was followed for 10 different users, and the resulting eye- models were manufactured in ABS using an FDM 3D printer. Comfort was assessed in wearterms models of good were fit andmanufactured compared toin theABS current using an products FDM 3D used. printer. Comfort was assessed in terms of good fit and compared to the current products used. 4. Results 4. Results The following results are divided into sections, including the preliminary evaluation of threeThe following scanning methods,results are the divided implementation into sections, of theincluding parametric the preliminary design framework, evaluation and ofeyewear three scanning fit evaluations. methods, the implementation of the parametric design framework, and eyewear fit evaluations.

4.1. 3D Full Head/Face Scanning and Parametric Head Model Three scanning methods were evaluated to assess their suitability as a 3D capturing method for personalized and customized product design. The main aspects evaluated were mobility, availability, and data accuracy.

4.1.1. Photogrammetry Photogrammetry presented quality and consistency issues. The main drawbacks this technology poses are its reliance on the steadiness of the operator, the space limitations for its operation, and the zenithal image capturing. Figure 10 shows the 3D model of the human head where the lack of cranial images leads to a heavily distorted head model that has little detail and use for customized product development. But recent drone-based and automated image capturing systems could help improve these results to allow access to a more reliable and low-cost 3D capturing method. Appl. Sci. 2021, 11, 5382 13 of 20

4.1.1. Photogrammetry Photogrammetry presented quality and consistency issues. The main drawbacks this Appl. Sci. 2021, 11, x FOR PEER REVIEW technology poses are its reliance on the steadiness of the operator, the space13 limitations of 20 for its operation, and the zenithal image capturing. Figure 10 shows the 3D model of the human head where the lack of cranial images leads to a heavily distorted head model that Appl. Sci. 2021, 11, x FOR PEER REVIEW has little detail and use for customized product development. But recent drone-based13 of 20 and

automated image capturing systems could help improve these results to allow access to a more reliable and low-cost 3D capturing method.

Figure 10. The result of photo reconstruction.

4.1.2.Figure Somatosensory 10. The result Camera of photo Scanning reconstruction. Figure 10.This The 3D result scanning of photo method reconstruction. presented better results in comparison to photogramme- 4.1.2. Somatosensory Camera Scanning try. However, the continuous adjustment of the scanning distance to acquire usable 3D 4.1.2.data Somatosensory requireThiss practice 3D scanning Camera and is Scanning method time-consuming. presented Moreover, better results complete in comparison head scans to photogramme-cannot be obtainedThistry. 3D However,during scanning one the methodsingle continuous step; presented the adjustment obtained better scresults ofanned the scanningin patches comparison require distance to a photogram tospecial acquire programme- usable 3D data requires practice and is time-consuming. Moreover, complete head scans cannot be try.(i.e., However, Artec studio)the continuous to stitch adjust the differentment of the sections scanning. In distance general, to this acquire scanning usable method 3D obtained during one single step; the obtained scanned patches require a special program datashowed require littles practice accuracy and, particularly is time-consuming. close to Moreover,the boundary complete of the headobject scans scanned cannot (i.e, be far- (i.e., Artec studio) to stitch the different sections. In general, this scanning method showed obtainedthest region) during. Pointone single clouds step; tend the to obtained be formed scanned in the patches surrounding require area a special of the pr head,ogram re- little accuracy, particularly close to the boundary of the object scanned (i.e, farthest region). (i.e.,sulting Artec in studio)redundant to stitchscanning the data. different Consequently sections. , Inadditional general, work this scanningwas required method to re- Point clouds tend to be formed in the surrounding area of the head, resulting in redundant showedmove little useless accuracy point , cloudsparticularly using closethe 3Dto the scanning boundary software. of the Theobject practical scanned experiment (i.e, far- scanning data. Consequently, additional work was required to remove useless point clouds thestshowed region) that. Point 3D scanned clouds tenddata toobtained be formed throu ingh the this surrounding technology areadid not of the retain head, enough re- using the 3D scanning software. The practical experiment showed that 3D scanned data sultingfacial in features redundant, making scanning it not data. suitable Consequently for the product, additional development work was in required this study. to re-(See obtained through this technology did not retain enough facial features, making it not moveFigure useless 11). point clouds using the 3D scanning software. The practical experiment suitable for the product development in this study. (See Figure 11). showed that 3D scanned data obtained through this technology did not retain enough facial features, making it not suitable for the product development in this study. (See Figure 11).

Figure 11. Somatosensory camera. Figure 11. Somatosensory camera.

4.1.3. Laser Scanning Figure 11.The Somatosensory laser-based scann camera.ing provided better results in terms of resolution, as shown in Figure 12. However, small movements such as blinking eyes can still cause some minor 4.1.3.distortion Laser Scanning in the final 3D model. Additionally, the scanning efficiency was influenced by differentThe laser skin-based colour scann anding type provided of skin better (e.g., resultsoily ski inn). term Ones of resolutionthe main drawbacks, as shown inthis Figuretechnology 12. However, presents small is its m highovement cost,s thesuch large as blinking amount eyes of scanning can still datacause points, some minorand the distortionneed of ain speciali the finalzed 3D software model. forAdditionally, post-processing. the scanning efficiency was influenced by different skin colour and type of skin (e.g., oily skin). One of the main drawbacks this technology presents is its high cost, the large amount of scanning data points, and the need of a specialized software for post-processing. Appl. Sci. 2021, 11, 5382 14 of 20

4.1.3. Laser Scanning The laser-based scanning provided better results in terms of resolution, as shown in Figure 12. However, small movements such as blinking eyes can still cause some minor distortion in the final 3D model. Additionally, the scanning efficiency was influenced by

Appl. Sci. 2021, 11, x FOR PEER REVIEWdifferent skin colour and type of skin (e.g., oily skin). One of the main drawbacks14 of this20 technology presents is its high cost, the large amount of scanning data points, and the need of a specialized software for post-processing.

FigureFigure 12. 12. LaserLaser scan scan image. image. 4.1.4. Scanning Comparison 4.1.4. Scanning Comparison The three scanning methods presented different advantages, as well as some drawbacks.The Although three scanning the cost me ofth photogrammetryods presented different is very low advantages in comparison, as well with as the some other drawbacksmethods,. itAlthough is more timethe cost consuming of photogrammetry and requires is many very pictures low in comparison to be taken in with a studio- the othercontrolled methods, environment. it is more time Additionally, consuming photogrammetry and requires many creates pictures 3D models to be with taken low in scan- a studioning-controlled accuracy. Onenvironment. the other hand, Addi thetionally scanning, photogrammetry process using creates a somatosensory 3D models camerawith lowis easyscanning to set accuracy. up and On low the cost; other however, hand, the the scanning actual scanning process processusing a somatosensory and operation is camerarelatively is easy complex, to set up and and the low accuracy cost; however, is not as the high actual as the scanning laser scanning process method.and operation Finally, is therelatively laser scanning complex, method and the produced accuracy the is not most as accurate high as model,the laser but scanning at a much method. higher Fi- cost nally,than the the laser other scanning two methods method used produced in this the study. most Furthermore, accurate model, all thebut at scanning a much methodshigher costevaluated than the in other this work two may methods not be used widely in this available study. for Furthermore, ordinary end all users. the scanning Therefore, methodsthis preliminary evaluated evaluation in this work showed may thatnot thesebe widely methods available are not for yet ordin suitableary end and users. widely Therefore,available this for thepreliminary intended evaluation eyewear and showed commercial that these application. methods are Consequently, not yet suitable a new andparametric widely available method to for create the accurate intended 3D eyewear head/face and models commercial for eyewear application. design basedConse- on quently,photography a new isparametric proposed. method to create accurate 3D head/face models for eyewear design based on photography is proposed. 4.2. Algorithm Development, Parametric Design, and Good Fit Test 4.2. AlgorithmParametric Development, technology Parametric is a very Design, effective and Good method Fit Test for mass customization, design, andParametric product development technology asis ait veryallows effective for quick method design for iterations mass customi and giveszation, designers design, and andcustomers product todevelopment opportunity as to itexplore allows for several quick design design alternatives iterations and in a gives short designers timeframe. and customersFigure to opportunity13a,b shows to the explore initial parametricseveral design 3D headalternatives model obtainedin a short from timeframe. the extracted curvesFigure from 13a,b scanned shows data the in initial comparison parametric with the3D parametric head model head obtained model from obtained the ex-from tractedcustomer curves pictures. from Thescanned user’s da 3Dta modelin comparison was created with by the substituting parametric the head measured model values ob- tainedof each from feature customer point into pictures. the parametric The user’s program 3D model in Grasshopper. was created by substituting the measuredParameter values of setting each (Figurefeature 14point) allowed into the for par a moreametric ﬂexible program design in process,Grasshopper. where many design iterations could be explored in a real-time situation. For example, the ideal users’ frame needs to meet comfort and good ﬁt requirements that could easily be adjusted using a parametric system. On the other hand, appearance requirements could also be explored through some quick real-time visualizations to offer the customer a preliminary view of the product.

Figure 13. (a) Parametric standard head; (b) User’s parametric head modelling. Appl. Sci. 2021, 11, x FOR PEER REVIEW 14 of 20

Figure 12. Laser scan image.

4.1.4. Scanning Comparison The three scanning methods presented different advantages, as well as some drawbacks. Although the cost of photogrammetry is very low in comparison with the other methods, it is more time consuming and requires many pictures to be taken in a studio-controlled environment. Additionally, photogrammetry creates 3D models with low scanning accuracy. On the other hand, the scanning process using a somatosensory camera is easy to set up and low cost; however, the actual scanning process and operation is relatively complex, and the accuracy is not as high as the laser scanning method. Fi- nally, the laser scanning method produced the most accurate model, but at a much higher cost than the other two methods used in this study. Furthermore, all the scanning methods evaluated in this work may not be widely available for ordinary end users. Therefore, this preliminary evaluation showed that these methods are not yet suitable and widely available for the intended eyewear and commercial application. Conse- quently, a new parametric method to create accurate 3D head/face models for eyewear design based on photography is proposed.

4.2. Algorithm Development, Parametric Design, and Good Fit Test Parametric technology is a very effective method for mass customization, design, and product development as it allows for quick design iterations and gives designers and customers to opportunity to explore several design alternatives in a short timeframe. Figure 13a,b shows the initial parametric 3D head model obtained from the extracted curves from scanned data in comparison with the parametric head model ob- Appl. Sci. 2021, 11, 5382 15 of 20 tained from customer pictures. The user’s 3D model was created by substituting the measured values of each feature point into the parametric program in Grasshopper.

Appl.Appl. Sci. Sci. 20 202121, 11, 11, x, xFOR FOR PEER PEER REVIEW REVIEW 1515 of of 20 20

PParameterarameter setting setting (Figure (Figure 14) 14) allowed allowed for for a a more more flexible flexible design design process, process, whe wherere manymany design design iterations iterations could could be be explored explored in in a areal real-time-time situation. situation. For For example, example, the the ideal ideal users’users’ frame frame needs needs to to meet meet comfort comfort and and good good fi fit trequirements requirements that that could could easily easily be be ad- ad- justedjusted using using a a parametric parametric system. system. On On the the ot otherher hand, hand, appearance appearance requirements requirements co coulduld alsoalso be be explored explored through through some some quick quick real real-time-time visualizations visualizations to to offer offer the the customer customer a a preliminaryFigurepreliminaryFigure 13. 13. (a()a viewParametric) view Parametric of of the the standard standardprodu product. headct. head; ; (b (b) )User’s User’s parametric parametric head head modelling. modelling.

FigureFigure 14. 14. Para ParaParametermetermeter setting setting setting.. .

AAAccordingccordingccording to to to tthe he the relationship relationship relationship established established established between between between the the the feature feature feature points points points and and human and human human head headheadsize, size, a size, corresponding a a corresponding corresponding eyewear eyewear eyewear model model was model designed was was designed designed using Grasshopper using using Grasshopper Grasshopper directly directly from directly the fromparametricfrom the the parametric parametric model of model model the user’s of of the the head. user user’ Thes’ shead. head. parametric The The parametric parametric eyewear eyewear eyewear was designed was was de de tosignedsigned ﬁt users’ to to fitfithead users’ users’ size head head through size size thro thisthroughugh correlation this this correlation correlation previously previously previously stablished. stablished stablished This parametric-based. .This This parametric parametric-based design-based designdesignframework framework framework allowed allowed allowed the size the andthe size appearancesize and and appearance appearance of the eyewear of of the the toeyewear eyewear be changed to to be be as changed changed the dimensions as as the the dimensionsdimensionsof the human of of headthe the human human changed. head head Therefore, changed. changed. each Therefore, Therefore, designed each each eyewear designed designed is parametrically eyewear eyewear is is pa pa linkedramet-ramet- to ricallyricallythe user’s linked linked head to to the size the us asuser’s showner’s head head in size Figuresize as as shown 15showna. Additionally, in in Figure Figure 1 5a.1 more5a. Additionally, Additionally, parameters weremore more addedparam- param- to etersetersfurther were were change added added the to to further appearance further change change of eyewear,the the appearance appearance so that of itof eyewear, ﬁtseyewear, the shape so so that that of it theit fits fits face, the the asshape shape shown of of thethein face, Figure face, as as 15 shown shownb. in in Figure Figure 1 5b.15b.

FigureFigure 15.Figure 15. ( a()a Parametric) 15.Parametric(a) Parametric Eyew Eyewearear Eyewear Des Designign Design1; 1; ( b(b) )Parametric Parametric 1; (b) Parametric Eyewea Eyewea Eyewearr rDesign Design Design2. 2. 2.

AfterAfter completing completing the the parametric parametric design design of of the the eyewear, eyewear, detailed detailed design design work work was was donedone to to further further refine reﬁnerefine the the product product and and to toto prepare prepareprepare it itit for forfor manufacturing manufacturingmanufacturing ( Figure (Figure(Figure 16) 1616)).. .The TheThe eyeweareyewear 3D 3D model model was was then then 3D 3D-printed-printed to to p produceroduce an an early early prototype prototype for for evalu evaluationation purposespurposes as as shown shown in in Figure Figure 1 71.7 . Appl. Sci. 2021, 11, 5382 16 of 20

Appl. Sci. 2021, 11, x FOR PEER REVIEW 16 of 20 Appl. Sci. 2021, 11, x FOR PEER REVIEW 16 of 20

eyewear 3D model was then 3D-printed to produce an early prototype for evaluation purposes as shown in Figure 17.

FigureFigure 16. 16. (a) (Detaila) Detail design design of eyewear of eyewear1;(1;(b) Detailb) Detail design design of eyewear of eyewear 2. 2. Figure 16. (a) Detail design of eyewear1;(b) Detail design of eyewear 2.

(b) (b) FigureFigure 17. ((aa)) 3D 3D printed printed prototype; prototype; ( (b)) evaluation of comfort. Figure 17. (a) 3D printed prototype; (b) evaluation of comfort. AsAs shown in in Figure 17a,17a,b,b, the eye eyewearwear frame was consistent w withith the subject’ssubject’s face shapeshapeAs shown and size.size. in Figure TheThe eyeweareyewear 17a,b, the temples temple eyewears were were frame consistent consistent was consistent with with the the participant’s w participantith the subject head’s head’ anatomy.s face anat- shapeomyThe and eyewear. The size. eyewear templeThe eyewear temple tips were tips temple tightlyweres weretightly shaped consistent shaped around around thewith back the the ofparticipant back the ear,of the describing’s ear head, describing anat- a good omyafit. . goodThe After eyewear fit. 8 hAfter of prolongedtemple 8 h of tips p userolonged were and tightly simulation use shaped and ofsimulation around dynamic the ofactivities back dyna ofmic the such activitiesear as, describing jumping such and as a goodjumpingrunning, fit. andAfter the framerunning 8 h of remained, ptherolonged frame in place, remaineduse and showing simulationin place that, showing the of size dyna andthatmic shapethe activities size of and eyewear such shape as was of jumpingeyewearin good and agreement was running in good, withthe agreement frame the face’s remained with contact the in place surface.face’s, showingcontact In general, surface. that the In size users’ general, and feedback shape the u ofser was s’ eyewearfpositive,eedback was statingwas in goodpositive that agreement the, stat eyewearing that with frame the the waseyewear face perfectly’s contact frame integrated surface.was perf toInectly their general, integrated face anatomy the u toser theirs’ and feedbackfacewas anatomy also was comfortable positive and was, stat to also wear.ing comfortable that the eyewear to wear. frame was perfectly integrated to their face anatomy and was also comfortable to wear. 5. Conclusions 5. Conclusions 5. ConclusionsThe experimental evaluation on three different scanning methods showed that laser- The experimental evaluation on three different scanning methods showed that la- based technology accuracy is still higher and easier to set up and use. However, due serThe-based experimental technology evaluation accuracy ison still three high differenter and easie scanningr to set methods up and useshowed. However, that la- due to the higher cost of this technology, its use may be limited to projects that require a re- sert-obased the higher technology cost of accuracy this technology is still high, itser use and may easie ber limitedto set up to and projects use. However,that require due a re- duced number of scans with high resolution, making data collection by ordinary users to theduced higher number cost ofof thisscans technology with high, itresolution,s use may makingbe limited data to collectionprojects that by requireordinary a re-users less accessible. Although the somatosensory camera-based technology is more accessible, ducedless numberaccessible of. Althoughscans with the high somatosensory resolution, making camera -databased collection technology by isordinary more acces userssible , with a reasonable data accuracy, it requires further post-processing work and is harder lesswith accessible a reasonable. Although data theaccuracy, somatosensory it requires ca furthermera-bas posted- technologyprocessing workis more and acces is hardersible, to to use. Finally, the use of 3D photo reconstruction software is time consuming and re- withuse a .reasonable Finally, the data use accuracy,of 3D photo it requires reconstruction further softwarepost-process is timeing workconsuming and is and harder requires to quires a specific setup and a large amount of data to be transferred, making the accuracy usea. Finally,specific tsetuphe use and of 3D a large photo amoun reconstructiont of data softwareto be transferred, is time consuming making the and accuracy requires set- setup-dependent, hence the need of a new method to capture user head data. The method a specificup-dependent setup and, hence a large the needamoun oft aof new dat amethod to be transferred, to capture usermaking head the data accuracy. The method setup-presenteddependent in, h thisence work the deliveredneed of a anew low-cost method alternative to capture for theuser quick head and data flexible. The method capture of presentedconcise data in appropriatethis work delivered for mass-customized a low-cost alternative eyewear design. for the Thequick data and sets flexible captured, capture and presentedof concise in thisdata work appropriate delivered for a mass low-costcustomi alternativezed eyewear for the design. quick Tandhe dataflexible sets capture captured, of concisethe flexibility data appropriate of the tools fo used,r mass allowed-customi thezed creation eyewear of design. more complex The data designs. sets captured, However, andfurther the work flexibility is needed of the to tools produce used design, allowed files the that creation require less of more post-processing. complex designs. Auto- andHowever, the flexibility further of the work tools is used needed, allowed to produce the creation design of more files complex that require designs. less However, further work is needed to produce design files that require less Appl. Sci. 2021, 11, 5382 17 of 20

mated post-processing algorithm blocks, or even the use of machine learning, could be further integrated. The project produced a usable parametric-based design framework for eyewear design; however, some aspects of its use require specific knowledge from the designer perspective in order to create a seamless MC experience between designer and customer. Other aspects of this project could be further explored to have a larger impact on mass customization and personalized design. For example, the end-user inclusion in the design process needs to be improved, and its impact on the proposed framework and on the design process considered, so it is possible to provide a more inclusive design experience. Despite parametric parameters being pre-set by the designer, it is up to the customer/end-user to make the final decision on the details that give the product a unique character. As none of the eyewear may be the same, they could be described as one-off products, created with the help of software and computer iterations. On the other hand, the variations of similar eyewear forms, the limited production runs using 3D printing, and the involvement of the user in the design process may create a stronger relationship between the product and consumer. These factors along with the user’s perception of a crafted creation are of interest as new emerging technologies such as AI (artificial intelligence) and IoT (internet of things) continue to further blur the boundaries between art, craft, and design. In this work, three main key elements were found to have a strong influence on the development of the parametric-based design framework for MC eyewear: • Human centred-based approach: Within a complex MC system, the uncovering of user and other stakeholders needs (e.g., designer) poses one of the biggest challenges. Therefore, human-centred design methods may bring a better understanding of the user needs in order to ease the implementation of MC strategies. • Technology appropriateness: In the process of personalised and mass customisation design of wearable products, convenient and accurate acquisition of users’ body data has become a key factor for customisation design. The quality of this data relies mostly on technology appropriateness, setup, and user expertise. The user experience of the data acquisition in commercial setups is a factor that requires further research. • Real-time feedback: In the process of personalisation and mass customisation of eyewear, parametric design is an effective and flexible tool to quickly realise product design iterations and explore the meeting of some of the needs of different users by adjusting design parameters and getting users’ real-time feedback. With the maturity of 3D imaging technology and 3D printing technology, the remote mode for design and production of personalized, customized, and rapidly manufactured products is gradually entering people’s day-to-day lives. For the personalized production of eyewear, some of the following factors can be addressed to facilitate this transition: • Clear communication strategy with consumers/end-users: this could facilitate the identification of needs to be met and clearly define the project scope. • Inclusive co-design process: A clear co-design strategy with well-defined roles and requirements could ease the decision-making process that is involved in the personalisation or customisation process. • Technical and aesthetic requirements: personalised and customised products, in particular eyewear, are expected to meet the basic personal needs of consumers in terms of comfort, functionality, and aesthetics. These can be fully defined during the inclusive co-design process. • User experience and human-centred focus: the development of a user-friendly Soft- ware/Visual Programming tool that enables the above tasks should be at the core of this type of work. A human-centred approach for the development of these tools is necessary for a seamless co-design experience. • Agile manufacturing: having the ability to adapt and to respond quickly to rapidly changing markets driven by customer-based needs is important for MC strategies. For eyewear products, current 3D printing technologies and available materials have reached a level of maturity that provides a good alternative. Appl. Sci. 2021, 11, 5382 18 of 20

• Integration of new technologies: having a ﬂexible framework that allows the integration of new technologies or strategies could increase the potential of this type of MC framework. For example, machine learning and artiﬁcial intelligence could be used to collect and analyse users’ data to pre-identify patterns in preferences to help determine the design requirements.

Author Contributions: Conceptualisation, X.B., O.H. and E.U.; methodology, X.B., O.H., and E.U.; software, X.B. and J.A.; validation, J.A. and J.E.C.; formal analysis, X.B., J.A., and J.E.C.; resources, O.H. and E.U.; data curation, X.B.; writing—original draft preparation, X.B., O.H., and E.U.; writing— review and editing, X.B., O.H., E.U., J.A., and J.E.C.; visualization, X.B., O.H., and E.U.; supervision, O.H. and E.U. All authors have read and agreed to the published version of the manuscript. Funding: Please add: This research was funded by THE NATIONAL SOCIAL SCIENCE FUND OF CHINA, grant number 18BG132. Institutional Review Board Statement: Eth-ical review and approval were waived for this study, due to REASON (The main object of this study is wearable products). Informed Consent Statement: Written informed consent has been obtained from the patient(s) to publish this paper. Acknowledgments: We thank the anonymous reviewers for their insightful suggestions and recom- mendations, which led to the improvements of the presentation and content of the paper. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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