Smart Manufacturing and Metrology

SMART MANUFACTURING AND METROLOGY

How can metrology enable smart manufacturing?

Authors: Supervisors: Eric Tell Andreas Archenti Alexander Ökvist Bo Karlsson

Bachelor Thesis in Product Realization and Industrial Engineering Abstract To create the possibilities needed for more precise simulations and calculations regarding manufacturing changes in the equipment and new technology has to be implemented. This work investigates possible solutions for the gathering of information in manufacturing companies. To get a wider understanding of the current situation in manufacturing we have also researched some possible solutions and applications that can be applied in manufacturing. The work consists of a literature study regarding the possible solutions and technologies of smart manufacturing complemented by a survey and a follow-up interview with scientist and employees’ at large corporations to get their view of the business today and possibilities for the future. The benefits from a successful implementation of metrology can help companies toward success in the transformation toward smart manufacturing. This report also investigates what is needed for implementing smart manufacturing and the transformation in manufacturing companies to get economic advantages with a technological adaption. It also covers the possible difficulties and problems that may occur when this implementation is performed. Sammanfattning Vid skapandet av grunder för att möjliggöra beräkningar och simulering för produktion så finns det krav på att nya verktyg och ny teknik implementeras. Detta arbete undersöker möjliga lösningar för att samla in information i industriella företag samt hur dessa företag ska gå tillväga för att möjliggöra denna omställning. För att få en bättre förståelse för området har vi även undersökt några möjliga applikationer som kan implementeras inom industrin. Arbetet består av en litteraturstudie där vi undersökte området smart manufacturing samt möjliga lösningar och tekniker som krävs för att uppnå detta. Som komplement till detta skapades även en enkät som baserades på området, svaren från enkäten följdes upp av intervjuer med deltagarna. Deltagarna var särskilt utvalda personer på större industriföretag eller institutioner vilka hade erfarenheter inom området metrologi. Detta användes som utökad grund för att få både en uppfattning av dagsläget samt idéer inför framtiden. Fördelarna med en lyckad implementering av metrologi kan hjälpa företag att ta steget mot att applicera smart manufacturing i deras produktion. Detta kan möjliggöra enklare produktion för operatörer men även ekonomiska fördelar för företaget i helhet. Arbetet tar även upp möjliga problem eller svårigheter som kan ske under denna implementation.

i Content 1 The development of the industry 1 1.1 Complexity in modern manufacturing 2 1.2 Smart manufacturing 3 1.2.1 Metrology and smart manufacturing 4 2 Purpose, research question and limitations 5 3 Method 6 3.1 Literature study 6 3.2 Survey 6 3.3 Interviews 7 4 Theory 8 4.1 Cyber-Physical Productions Systems and cloud-based manufacturing 8 4.2 Manufacturing prognosis 10 4.3 Smart sensors 11 5 Applications 14 6 Results 16 6.1 Survey 16 6.2 Interviews 21 6.2.1 Present situation 21 6.2.2 Metrology 21 6.2.3 Data collection and analysis 23 6.2.4 Implementation 24 6.2.5 Examples 26 6.2.6 Challenges and the future 26 6.2.7 Effects 27 7 Discussion 28 8 Conclusion 30 9 Acknowledgment 31 10 References 32 11 Appendix 34

ii 1 The development of the industry To grasp the current state of manufacturing some historical information about the field is essential. During the 18th century the first real change in production began. Before this time most goods were produced by independent craftsmen without collaboration except for the few orders that could be placed by the military or the nobles. These workers created for their local market, which often was limited by city borders. Nations often imposed limitations on the establishment of new producers on positions already occupied to restrict competition, therefore resulting in less competition in existing industries. In extension, they also made sure that none of the producers gained a monopoly. This all changed with the first industrial revolution that started in 1740 in England (Encyclopedia 2017). The challenges of today's industries are rooting from the changes that have influenced both customers and companies during the last century. The customers want cheap wares of good quality that at the same time can fit their specific needs. We are entering what the Germans call the fourth industrial revolution, the digitalization off the production industry. There exist difficulties to adapt these new technologies for a business that has a clear paradigm and a way of work regarding incremental innovation compared to the more radical changes of digitalization. According to PwC, 33 % of the industrial companies today see themselves at an advanced level of digitalization. By the year of 2020 at least 72 % of all industrial companies want to reach this grade of digitalization. In today’s industries only half of the companies are collecting data and big data analytics as a base for their decisions. These numbers are expected to grow to 8 out of 10 of all companies will use big data for their decisions in the next five years. (R. Geissbaur 2016). The benefits of becoming a digital industry may seem far off but from the nine researched industries the amount invested would come close to 900 b.n $ for the coming five years. This number may seem daunting but they expected an annual return on at least 490 b.n $. The economic advantages to motivate a change in the industry and to integrate digital solutions in their production exists. The differences that exist compared to the IT industry is that in a completely digital environment the costs regarding the collecting and storing of all the data are minimal. In production, the need for sensors and connectivity to gather all the different production variables is great. The problems lie in the change from our current production lines that dates back to the start of the automation era which are based further back in the non- complexity of Henry Ford assembly lines. It might be possible to implement smart sensors in current machines to gather all the data required for better decisions. There are challenges ahead with both opportunities of greater revenue and the problems of changing the way industry act and think in a global competitive environment. (R. Geissbaur 2016) (Kunzmann et al. 2005) During the last thirty years the changes in automation has created an environment in manufacturing where the operators’ roles have been vastly modified. From working as the actual producer that is transforming the raw material into wares, they are in increasing extent monitoring machines that are executing the actual physical work. To measure and analyze the factors from the production to evaluate problems and errors has become the reality for most operators in larger factories. How the operators can help working towards digitalization of their workplace is a pressing mater.

1 In present day, manufacturers are following the large amount of IT that is becoming an integrated part of production and measurement. The previous challenges to only maximize output are moving towards an interest in producing the greatest quantities of wares with the least amount of raw materials. Problems such as constant machine uptime, zero vision of defects and customizable products to specific need are all parts of the future challenges in manufacturing. Following these requirements, the problems of the future becomes clearer. 1.1 Complexity in modern manufacturing

Henry Ford’s breakthrough with zero complexity production has ended. Since the production of the T-Ford many manufacturers have competed by reducing complexity in their production, which have been successful. But those methods are insufficient to deal with the challenges of the future (ElMaraghy et al. 2012). Global competition and higher demands from customers have driven up the product and manufacturing complexity. The challenges today are characterized by design complexity that must be matched with flexible and complex manufacturing systems. Complexity is driven by customer demands and expectations as well as global competition. The customers’ expectations of new and better products as well as services compels manufacturers to develop advanced products that is harder to produce and has higher demands on tolerances. To manage this the production methods will get more complex. In addition, global competition drives down the prices. Therefore, the manufacturing process must be cheaper, faster and strive to produce zero defect products. Manufacturing companies operates in an uncertain and changing environment driven by changes in customer demands, product design and processing technologies. This increases the complexity in the manufacturing systems and is one of the main challenges for future production. The worlds private and public sector leaders believe that a rapid escalation of complexity is the biggest challenge confronting them. Their enterprises today are not ready to cope effectively with this complexity (Palmsiano 2010). Due to the rising complexity in manufacturing there is a greater need of real-time data and knowledge of the processes. This data and knowledge can be used to anticipate and prevent problems in the process. According to Davis and Edgar smart manufacturing will lead to that production goes from response to prevention (Davis and Edgar 2008). On page 14 they wrote: “…Response to Prevention addresses how sensors and knowledge-enabled capabilities will be organized and oriented. Every component of the enterprise will operate in a dynamic, proactive environment enabled by intelligent, model-based systems that are vigilant in monitoring plant and asset status. Any deviations from expected norms will be noted and if adverse trends are detected, the intelligent control systems will gather needed information and autonomously take preventive actions and in so doing exhibit high a high degree of fault tolerance.” Intelligent and connected systems will change production and the way value is created. It will also change the competitive environment; the increased productivity and higher quality can be a game changer. Connected devices and systems can increase productivity with 2.5 – 5 % and 60 % of manufacturers think that smart manufacturing can increase their revenues (Monostori et al. 2016).

2 1.2 Smart manufacturing

National Institute of Standards and Technology NIST in United States of America defines smart manufacturing as: “...fully integrated and collaborative manufacturing systems that responds in real time to meet the changing demands and conditions in the factory, supply networks and customer needs.” (NIST 2014) The field of smart manufacturing consist of multiple different branches that all come together to form a new basis for production. It takes on the aspects in collection of big data, industrial connectivity, and the use of advanced robotics to further increase production and competitivity. The possibility to connect a whole factory and instantaneous transmission of data from one machine to the rest of the production. The usage of this data could also be applied beyond the factory itself and send data to either suppliers or customers to further integrate the process from raw material to finalized product (Jung et al. 2015). Kang et al have defined some key technologies that are needed to implement smart manufacturing, they are cyber-physical systems (CPS), cloud manufacturing (CM), big data analytics, internet of things (IoT) and smart sensors. They also mentioned that these technologies affect each other in the implementation and usage of smart manufacturing (Kang et al. 2016). IoT is one enabler for improving manufacturing, it can enhance automation, supply chain management and remote maintenance. It has potential to automate processes by connecting systems with machines, processes, and humans. It can also give direct access to design and manufacturing related data and information (Wu et al. 2015). One other enabler for smart manufacturing is artificial intelligence (AI). AI might meet the challenges with complexity in modern and future manufacturing, it can enable a more flexible, responsive, and intelligent manufacturing. Kruger et al writes that software agent and holonic manufacturing provides one promising computing methodology for the development of intelligent systems (Kruger et al. 2011). A combination of these two systems can increase the robustness, scalability, and flexibility of the industrial control system. Kruger et al show in their report that an IA architecture for an industrial process could learn from and optimize a high- volume manufacturing process.

3 1.2.1 Metrology and smart manufacturing Metrology is the science of measurement and its applications (BIPM 2008), the area has three sub-fields, scientific or fundamental metrology, applied, technical or industrial metrology and legal metrology. Figure 1 and Figure 2 presents how smart manufacturing sees metrology respectively how metrology sees smart manufacturing.

Figure 1:Smart manufacturing from the perspective of metrology (Szipka and Archenti 2017).

Figure 2: Metrology from the perspective of smart manufacturing (Szipka and Archenti 2017) With IoT and smart sensors a data collection framework can be built, combine that with a CPS and real-time performance and prognostics can be done. The perspectives in the two figures are connected to each other. Smart manufacturing needs a data collection framework, which can be realized by connected measurements. Real-time performance evaluation can be realized with data analysis. Prognostics and health management is also realized with connected sensors that measure components in the machine. As Kang et al identified, the technologies are depended of each other when smart manufacturing is implemented. 4 2 Purpose, research question and limitations To know what to measure is one of the challenges of today's competitive industries. When these industries implement smarter systems and smart manufacturing this challenge might be the one that decides if the implementation will be successful or not. The factors that will have an impact on the production needs to be differentiated from measurements that will only waste money. If the companies do not know what to measure and how it can be measured they will have a hard time to stay competitive. To stay competitive companies will have to focus on what aspects they want to measure. Measuring right parameters have been in focus since the implementation of quality control. In 1990 Guillot and Chryssolouris presented the advantages of using neural networks and the group method of data handling (GMDH) as tools when deciding what measurements to use in machine control (Chryssolouris and Guillot 1990). The purpose of this report is to identify some aspects of how data should be gathered and how this can enable smart manufacturing, the report also compiles a present day view on how smart manufacturing can be implemented by using metrology. The main research question is: • How can metrology support the development of smart manufacturing? Other relevant questions are: • How smart manufacturing affects production? • What systems will companies use and how will the systems collect and gain access to relevant data? • What data is relevant to improve the processes and make them more efficient? The report will only look at implementation in manufacturing industry that uses stand-alone machines and the report will only look at some aspects of smart manufacturing that can be implemented in assosiated production processes. Regarding the area of metrology, the report will look at the applied area of metrology, e.g. the application of measurement to manufacturing and its processes.

5 3 Method Data for this study were collected by reading articles, conducting a survey, and doing interviews with those who participated in the survey. The articles were both summarizations of current findings (secondary data) and articles with primary data. This literature study was done in the start of the project to get an overview of the subject and to find areas which could be studied further to answer the research question. Based on the data collected in the literature study the survey was designed and sent to three companies and two institutions. The questions in these interviews were based on the answers the interviewees responded with in the survey. 3.1 Literature study

The literature study was done to investigate the development in the smart manufacturing area during the 21st century and to identify what technology that could be used to help solve the research question. The data from the literature study were collected continuously during the research, in the beginning the literature focus was on summarizing articles and after hand more articles about specific subjects were studied. All sources are published articles from different scientific engineering journals except for two, which is white papers from a reputable consulting company and from a science foundation. Google scholar and KTHs database search system primo were used to find scientific publication databases, the articles were found by searching directly in a database or by searching on google scholar and then go to a database. The search words were: “smart manufacturing”, “Industry 4.0”, “Cyber-physical systems”, “CPS”, “Metrology”, “predictive manufacturing systems”, “predictive maintenance systems” and “industrial revolution”. 3.2 Survey

The goal with the survey was to get insight in what production companies, metrology companies and institutions view as important when developing a more efficient industry and how it can be done by combining metrology and smart manufacturing. Since the research area is not well explored the survey was designed as a qualitative study. The survey was based on the data found in the literature study and the questions were supposed to fill the knowledge gaps about implementation, what should be measured and how is can be measured. The survey was reviewed twice by a control group which have appropriate knowledge and experience in the field before it was sent out to the companies and institutions. Every answer was expected to be a paragraph to avoid too much or too insufficient data the length of the survey was limited to ten questions. Each individual in the focus group for this survey was handpicked since the subject is complex and unknown by many, therefore it is necessary to make sure every participant know the subject smart manufacturing and/or metrology to a certain extent. One other reason for the focus group to be handpicked were that every participant in the survey would also participate in an interview. One other reason to have a handpicked group was to make sure that the group members had different backgrounds and positions. Since the survey is explorative, as many different views of the subject is needed. A problem with a handpicked focus group is that the conclusion drawn from the result only represent the views of the participants and not the whole industry and science community.

6 3.3 Interviews

Semi-structured interviews were conducted with the participants in the survey, a total of four persons were interviewed. The interviews were conducted using a web based meeting service. All the answers were recorded and listened to later and summarized, notes were also taken during the meeting. The goal with the interviews was to be able to follow up the answers in the survey and get a deeper understanding of what the participants reasoning was when they answered. Therefore, the interviews were semi-structured to be able to openly discuss the subject. There is the same risk in the interview as in the survey regarding the conclusions drawn from the results, they only represent the views of the few subjects participating in the interview.

7 4 Theory 4.1 Cyber-Physical Productions Systems and cloud-based manufacturing

A cyber-physical production system (CPPS) is a system of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes. It provides and use data accessing and data processing simultaneously and can consist of several subsystems that are connected across all levels of production (Monostori et al. 2016). Three main characteristics of CPPS are intelligence, connectedness, and responsiveness. A CPPS needs to acquire information from its surroundings and act autonomously on that information. Therefore, it needs to be connected to sensors that gathers this data, but a CPPS must also have the ability to connect to other elements of the systems (including humans) for cooperation and collaboration. Monostori et al. makes the general assumption that a CPPS consists of two main functional components. One is responsible for the advanced connectivity that ensures real-time data acquisition from the physical world and the information feedback from cyber-space. The other one incorporates intelligent data management, analytics and computational capabilities that constructs the cyber-space.

Figure 3: The data flow in a CPPS (Lee et al. 2015) CPPS can be seen as one type of cloud manufacturing (CM) that uses cloud-enabled prognosis, the CPPS monitor the machine conditions during the process, based on the information remote data analysis and prognosis are done in the CPPS and the possible measures to improve the production are done (Gao et al. 2015). Cloud-enabled prognosis can improve accessibility and robustness in the CPPS, it also improves the computational efficiency and the data storage. The cloud also enables collaboration between machines and data distribution, which is critical for a CPPS.

8 Further, Gao et al identified some characteristics and benefits of CM, they are diversity, dynamic, virtualization, elastic, broad access, fault tolerance and cost effective. The three first that are mentioned can be key components to handle the new challenges with modern and complex manufacturing. Both CPPS and CM are methods for data-driven decision-making and process control. In a conference paper researchers from NIST suggested that a control system can include actuators, sensors, and a controller (Helu et al. 2016). In Figure 4 the system is illustrated.

Figure 4: Industrial control systems (Helu et al. 2016). The controlled process is disturbed and the sensors sends the data to the applications that analyzes the data and detects that the process must be corrected either by changing input parameters or by maintenance. The altering of input parameters can be done automatically or manually by the operator in the human machine interface. The controller receives the altered parameters and the alterations are realized by the actuators. Wu et al expect that Cyber-physical systems (CPS) will help to integrate design and manufacturing knowledge, connect physical and cyber components to each other, enhance interaction and communication in complex manufacturing methods. CPS will be able to improve autonomy, functionality, reliability, adaptability, flexibility, and scalability in cloud based manufacturing (Wu et al. 2015).

9 4.2 Manufacturing prognosis

Based on monitored conditions tool, machine or system lifetime can be estimated, this prognosis can provide a more scientific method to plan maintenance and create a more reliable production system design (Gao et al. 2015). For an example one efficient maintenance method, intelligent preventive maintenance (IPM), uses prognosis based on real time data to minimize down time and costs. IPM can increase system safety, extend machine life time, increase maintenance effectiveness, and reduce maintenance cost by preventing unnecessary replacements and machine failures that cause damage to surrounding machine components. Figure 5 describes how the architecture for CM can be designed. The machine conditions are monitored by sensors and sent to the cloud, the data from the conditions are analyzed by applications in the cloud or by a collaborative engineering team. The acquired results can form a base for preventive maintenance.

Figure 5: Architecture of cloud-enabled prognosis (Gao et al. 2015). In their research, Gao et al mentions that studies have shown that preventive maintenance can reduce maintenance costs by 30 % and avoid up to 75 % of the breakdowns compared with using the regular norm with scheduled maintenance. Gao et al divided prognosis techniques into three categories, physics-based, data-driven, and model-based. Physics-based methods uses empirical formulas to describe a system, data-driven methods use collected data to numerically create a prognosis regarding lifetime of the machine. Both AI and statistical methods can be used in the data-driven methods. The model-based techniques are a combination of the other two categories, this can improve the predictions even further.

10 4.3 Smart sensors

A CPPS needs to collect data from the processes, that can be done with sensors or components equipped with sensors. One concept of smart sensors is gentelligent (GI) components, components that can collect, store, and communicate data (Denkena et al. 2010) (Denkena et al. 2014). The components contain manufacturing and quality data, stored as the “genetic information”. The intelligence of a genetic component is its ability to collect real-time data, process the data and then store and communicate this data. Denkena have divided real-time data into two types, configuration data and runtime data. Configuration data is generated from the engineering process to describe physical part and the machine. Runtime data is generated during the manufacturing process and describes the real-time status of the process. The date that it stored as well as recorded in the components can be useful to plan and control the manufacturing, determine the cause of breakdowns, estimate when there is need of service based on the usage of components and aid in the planning to set up a dynamic service interval (Denkena et al. 2010). This possibility can reduce downtimes in manufacturing equipment and maximize the use of a component before it’s replaced. As illustrated in Figure 6 a closed information loop from design to maintenance can be obtained with gentelligent components.

Figure 6: Application of gentelligent component over their life cycle (Denkena et al. 2014).

11 In Figure 7 an illustration of how an gentelligent component can be manufactured. The production process can be more dynamic if the machines can read production data from the part they are producing. As written before can maintenance be more efficient with gentelligent components, for example can IPM be implemented.

Figure 7: Manufacturing planning and control with gentelligent components (Denkena et al. 2014). Figure 8 illustrates how the control cycle of maintenance can be done with gentelligent components.

Figure 8: Control cycle of component status-driven maintenance (Denkena et al. 2014). An gentelligent component can be a machine tool which is able to sense and measure the conditions for the cutting process, for example the forces between the tool and the workpiece. The machining process can be monitored and optimized during the manufacturing and be optimized for the next workpiece as well (Denkena et al. 2014). If the clamping system is gentelligent the force flux in the machine can be measured as well, this data can enhance the

12 optimization of the process. Gentelligent machine tools and a monitoring system can make it easier to identify best practices for production sequences for every individual product in a batch. The best practice can change depending on the state of the whole production system, thus can gentelligent components make the production more flexible to change and disturbances. Denkena writes that an gentelligent driven production system can reduce lead times and interruptions in the manufacturing by 22 %. Kang et al defines smart sensors the most important technology to realize smart manufacturing via CPS, IoT and CM (Kang et al. 2016), since it is the most basic component/technology for gathering of real time data.

13 5 Applications Vogl (Vogl et al. 2016) tested a method for online monitoring of degradation in linear axes by using an inertial measurement unit (IMU). The method uses data from both accelerometers and rate gyroscopes to find changes and error motions in the axes. The method is said to be robust and appropriate analyzes based on the collected data can be done. The maintenance personnel can get information of the seriousness and location of the wear, further violations of performance tolerances can be estimated and an assessment if the axis needs to be replaced can be done. Vogl et al continued with suggesting that this method can be developed and supported self-diagnosing systems there among other useful information remaining useful life of axis can be estimated. In Figure 9 the concept of a mounted IMU and the data processing is illustrated.

Figure 9: IMU-based method for diagnostics of machine tool performance degradation (Vogl et al. 2016). Okwudire et al (Okwudire et al. 2016) minimized vibration in a computer numerical control (CNC) machine by implementing a regular P/PI feedback controller augmented with velocity and acceleration data from accelerometers. This method can minimize tracking errors and thus increase process stability. Aguade et al (Aguade et al. 2016) identified that direct measurement will be replaced by technology based on indirect measurement which requires shorter verification time. One of the measurement system suggested is laser tracker. In their article Aguade et al describes a method to improve a milling machines accuracy with indirect measurement. This can increase both process stability and product quality. A tool condition monitoring system has four purposes according to Dimla and Dimla (Dimla and Dimla 2000), they are: • “Advanced fault detection system for cutting and machine tool, • check and safeguard machining process stability, • means by which machining tolerance is maintained on the workpiece to acceptable limits by providing a compensatory mechanism for tool wear offset and • machine tool damage avoidance system.” Dimla and Dimla continued to describe how operators uses a combination of sight and sound to monitor process and tool conditions. Sensors cannot be as flexible as a human but they are instead much more accurate. Advanced sensor design can enable adequate data collection of the process, acoustic emissions, tool tip/edge temperature, cutting forces, and vibrations is suggested as good data points to collect. Among these cutting forces and vibrations are

14 considered the most applicable parameters. Dimla and Dimla also suggested to measure these four characteristics with optical methods, by measuring workpiece dimensions and surface qualities, spindle motor power consumption, magnetism, and finally ultrasonic methods. Dilma and Dilma’s suggested methods of monitoring tool wear in combination of a smart online system could automate the tool monitoring process, Ghosh et al (Ghosh et al. 2007) suggest a neural network to fulfill the same work. As seen in Figure 10 the artificial neural network (ANN) learn from manually obtained observation, after the system is trained the ANN can estimate tool wear in real time.

Figure 10: A system architecture for estimating of tool wear (Ghosh et al. 2007).

15 6 Results 6.1 Survey

The results from the survey are presented by identifying key words and present the number of occurrences of those keywords in the answers. If the answer to a question is accurate and describing the whole answer will be cited. 1. What do you think will be the main benefit of smart manufacturing in collaboration with metrology? Citation: “Metrology plays a more important role in smart manufacturing compared with traditional manufacturing in terms of providing accurate, qualitative and traceable data/information to make a reliable foundation for the "smartness" of manufacturing.”

16 2. To enable smart manufacturing, characteristics of products, systems, environment etc. needs to be captured and fed upstream. From this point of view which characteristics need to be captured by measurement? Citation: “The same characteristics should be captured by measurement in smart manufacturing as those in traditional manufacturing. However, the measurement needs to be made in a higher speed and also in a way of network, instead of individually, in smart manufacturing.”

3. Should these characteristics be measured before, after or during the process?

17 4. How can smart manufacturing and metrology assist industrial practitioners in their work?

5. In what areas do you see easy/easier implementation and high yields for metrology in manufacturing?

18 6. What can be the role of smartness in metrology to support application (in your relevant working field)? Citation: “As a provider of qualitative and traceable data/information that are necessary for the smartness of manufacturing.”

7. What are the biggest obstacles in the concept of smart manufacturing where metrology can offer solutions?

8. Can you describe the timeline for today’s factories to integrate metrology to the smart manufacturing concept? This question did not yield any data.

19 9. What problems do you see for companies that will not adapt smart manufacturing? Both now and in the future.

10. How will smart manufacturing enable sustainability (both from an economic and environmental perspective)?

20 6.2 Interviews

Since the interviews had different questions based on the interviewees answers in the survey the answers are not directly comparable. Instead the interviews have been summarized and different subjects was identified, all that the interviewees said about the subjects is presented below. 6.2.1 Present situation That improvement in today’s factories is possible was something that all the interviewees had in common with each other. Interviewee A identified the problems with manufacturing in present day was a reactive approach instead of working with the problems in a predictive and proactive way to hinder these occurrences before they even happened. Interviewee D spoke of the problems with both lead time and manufacturing time for products. These had to be improved in the factories to minimize cost and waste as well to maximize output. Interviewee A and B had different situations regarding machine connectivity, interviewee A:s machines have been connected for some time and are transmitting some production data. Interviewee B on the other hand said that the earliest machine they could connect to the cloud was from the mid-2010s. Which in comparison is a newer machine park than interviewee A:s machine park. These differences might speak of possible complications with implementation in different kinds of manufacturing machines. Interviewee B confirmed that there exists software to create digital machines and factories that can be fed production parameters. Interviewee A said that they already had digitized machines where simulation of production can be done. Both interviewee A and B said that the next step is to feed this digitized model with real time data from the production. Interviewee A explained how advanced simulation with production data can help the engineers introduce new products in the production. The simulation can tell the engineers how fast the process will be in the real world, but the hard part is to predict the capability since it depends on many factors. When a new product is produced it must be tested in a machine to evaluate its capability because the capability in the simulation is not good enough. E. g. it is not known if the processing of the part is efficient enough to have a high yield. If this could be determined before a test in the real world, resources and time could be saved and the ordinary production would not be disturbed unnecessarily. If production data could be gathered and use that data in the simulation program a better estimate of the capability can be done. Interviewee B explained how AI can be used to control complex processes that depends on thousands of parameters, for example casting. Today's AI could not manage to do the job but the implementation area is attractive. Interviewee B also said that industries are still far behind in the field of instructions regarding maintenance, for example concerning full scale halts of factories and regular problems. 6.2.2 Metrology All interviewees said that vibrations and temperature in the machine are important parameters, interviewee A also added product dimensions to the list and Interview B said that lateral playroom also is important to measure. Interviewee D also explained that vibrations and temperature differences can tell if there is something wrong with the machine. Interviewee B thought that to measure and control the process in real time will improve both process and product quality in manufacturing.

21 Interviewee A said that if in process measurement of the product dimensions is realized the production may become more efficient, if the dimensions can be corrected the machine will produce a correct product and if that's not the case any unnecessary machining is avoided. Regarding in process measurement of product dimensions interviewee D said that it is possible to some extent today, more complicated geometries will be hard to measure and it will be difficult to get the same accuracy as with a 3D-measurement machine. Interviewee A talked about one drawback of measuring the products dimensions directly in the machine. The machine compensate itself and if the compensation is wrong the machine might say that a product is correct even if it has an error. When the part is measured after the process in a separate 3D-measurement machine that specific error is avoided. But if the dimensions are incorrect, compensations must be done and a new part must be produced and measured to know if the process is correct. If the operator can be sure that the process is correct there is no need to measure the part after it is done, that would save money and increase the productivity. Interviewee A also explained how information about the machine temperature and vibrations can be used to improve product and process quality. During the process compensation for the temperature in the machines is done, these compensations can be retrieved and compared with the final result of the product. After this comparison, an operator or engineer can see how the compensations affected the dimensions. In a milling or turning machine the temperature can be measured in the cooling water, on the machine parts and on the tool, if it is a smart tool it can measure its own internal temperature. With a smart tool the possibility exists to measure vibrations and compensate in real time for them. One other way to compensate for vibrations is to monitor the tool during the process and after the process the machine can calculate what parameters it should use in the next process to avoid vibrations. This method only works well when the same process runs every time, if different articles are produced with the same tools real time compensating is necessary. Both temperature and vibrations affects the dimensions of the created product, these dimensions can be measured either during or after the process. Interviewee C said that the measuring equipment needed for implementing smart manufacturing exists today but different industries has different needs due to the different circumstances in the production. For example, are metal cutting industries tougher for the sensors than electronic industries. The sensor that exists today are good enough to use for the most cases, but to implement smart manufacturing it might be necessary to combine the sensors in new ways and connect the sensor to the cloud. Interviewee A added that one drawback with sensors are the fact that every sensor is an error source, this needs to be considered before filling the machine with them.

22 6.2.3 Data collection and analysis Interviewee C believes it is possible that all the parts of the user chain can benefit from sharing data. E.g. the machine suppliers get user data from the producer, the producers get user data from the end user. This can help both the machine producer and the manufacturer to improve their products to better suit the user. Interviewee A suggested two different data collections strategies, one based on big data analysis and one based on selective data collection for specified problems. The strategies are presented in table 1.

Method Approach Advantages Disadvantages

Big data Collect everything that can All the data will be It demands a lot of resources analytics be measured, analyze it and available after the process and knowledge about the then try to connect the is done and it can be tied process. results from the analysis to to every problem that what occurred during the occurred during the The operator and process. process. maintenance personnel will not be included.

Selective The engineers, operators Is not as resource You must know what you data and maintenance personnel demanding compared to need to measure before you analytics defines X numbers of issues big data analytics. start the analysis, if you measure wrong parameters with the process that they you have to redo the process. want to resolve, they also The operator and the need to find out what caused maintenance personnel these issues and how to will be included and they measure these might understand how phenomenon’s. this solution helps them in their work. They could By doing this the X issues also provide their can be prevented. experience about the process and machines.

Table 1. Data collection strategies.

23 6.2.4 Implementation “When sensors are installed in a machine and the data is analyzed it will be like opening up the box of Pandora.” The citation is taken from interview A and the interviewee point was that when a company starts using sensors and analyzing data so many possibilities opens up and the company cannot be sure what they will be able to do. Interviewee A believes that implementation of smart manufacturing will start with integrating simulation environments to the physical production, not necessarily the whole factory at once, one machine at the time is more sensible. The interviewee also identified maintenance as an easy implementation area with high yields. Interviewee B thought that implementation of smart manufacturing will start by installing machines that are already prepared with sensor for monitoring production process and are connectable to a cloud. Interviewee A, B and C pressed the importance to connect the machines to a cloud. When the machines are connected all collected data can be transmitted to the cloud and be analyzed. Process quality can be evaluated and maintenance can be more dynamic. Interviewee C mentioned that not only machines in the same factory should be connect, different factories could also be connected to each other. Also, the operators and engineers should also have access to the same cloud as the machines. Whenever a decision is made it will be taken based on the information from the machines. Interviewee A explained that not only the machine needs to be digital, human competence must also be digitized. When experience from an operator is put online will not only the analyzing systems and applications get smarter, an unlimited number of other operators and engineers will be able to use that experience to their own gains. This will lead to a more dynamic production according to interviewee A, the machine can analyze the process and if errors are detected the process can be stopped before the machine crash or ruins the product. Interviewee C believed that smart manufacturing will go through different phases, in the first phase will the operator will be engaged in the process. They will receive information from the machine interface and based on that they will take a decision. During the phases, the operator will serve the machine in the same extent as before but the machine will provide more and better information to the operator for every new phase. In the later phases the operator's competence is digitized and the machine can in the end take most of the decisions itself. Interviewee B said that digitalization of a whole factory will be expensive, therefore will factories with processes where downtime is costly (ex. pulp & paper works, steelworks) have a larger initiative to digitize their production to predict maintenance and counteract downtime. Industries with parallel production flows are not as sensitive to production stops will not be able to the same extent motivate investments in that area. It shall also be added that large process industries have specific paradigms that can be hard to change, because the lock-in effect. These makes them slower to adapt to new technology. Because of this, workshops might be faster to adapt to these changes in smart manufacturing before the process industries.

24 Interviewee C identified automobile and electronic industries as two of the main targets of smart manufacturing; there are already a lot of inspections and the industries are large scaled. The parts to the final products are created all over the world and it is important to keep track of the different batches. The implementation will not be easier but since there are already a lot of inspections in the processes of the automobile and electronic manufactures this will come more naturally. If those measurement instruments and sensors can be connected to the cloud and the data sent and analyzed, implementation of smart manufacturing is emerging. Therefore, interviewee C think it’s more motivated to invest in smart manufacturing for those two industries. Interviewee B described the development of smart manufacturing as incremental innovations over time. To succeed with the implementation a clear goal and a strategy is needed. Interviewee C believed that it will depend on the industry. Because a car is built with several thousand part made in different countries, so if the factories all around the world are connected with each other you need a totally different way to solve this manufacturing connection. The problem is it can be at a to large scale even for these big companies. C thinks they have a very established production management system, but that can be totally changed with smart manufacturing, C thinks that for small scale manufacturing there can be incremental innovation but for those industries that has large scale manufacturing, there will be one big revolutionary innovation. Interviewee A concluded that it is time to start working with the different aspect of smart manufacturing. Even if a total implementation throughout the company will be expensive there are ways to harvest the low hanging fruit easily. Interviewee C thought that it can be difficult for companies to make decision when they should implement the different aspects of smart manufacturing. It is not necessary to adopt smart manufacturing early but it might be bad to adopt it late since the late adopters probably will lose competitiveness. There will be a balance of your investment and your gain. The timing is also important in this, C think that the sensors and measure instruments the beginning they can be very expensive so if companies invest early they may need to invest more and the cost will be reduced when the prices are lowered so those who adopt a bit later can invest lesser and get the same yield. That is the kind of difficult decision to make. It is a balance of investment and the benefits. In the examples below easy ways to implement smart manufacturing by measuring production equipment and processes are represented.

25 6.2.5 Examples Interviewee A gave three different examples on how smart manufacturing can be implemented by using sensors and/or measurement: Tool changes are executed about five to six million times per year in a manufacturing company. If the time it takes to change a tool is measured every time the operations is done any anomalies can be detected, when the operation takes slightly longer one or more parts in the machine might be worn and can be exchanged before they break. This is a very simple way to be smart, no extra sensors are needed so therefore it is a cheap way to measure the state of the machine. In the same way, a footprint can be taken every day, the machine does a certain routine and every step of the routine is clocked. The state of the machine can be checked without installing any additional sensors. If the quality in the raw material can be measured the cutting forces in a milling machine can be adjusted to optimize tool life. One example can be attaching sensors and a GPS to a forklift to be able to trace where the raw material is, every time the forklift places a batch of material a footprint is send to the cloud. Then some engineer realized that he/she could analyze the usage of all the forklifts on the plant, the result was that they had two redundant forklifts. The maintenance department also detected that every time the forklift drove into a pothole. Following this an order could be send to the maintenance department to fix the road. Interviewee B and D gave one example each. B suggested that if spindle wreckage can be predicted by installing sensors for vibration and studying power supply to the spindle motor combined with empirical data any breakdowns can be stopped and the spindle can be exchanged before an incident. Interviewee D explained how production can be optimized by predicting the tool life left and when the tools condition is critical the machine can send a demand that the tool is to be replaced. In total, three of five examples were about maintenance, and three of five examples was about process stability (one of interviewee A:s examples had both aspects). 6.2.6 Challenges and the future Both interviewee A and B said that smart manufacturing will not be implemented in a standard package, it must be implemented differently for different companies because the different way of work that exists in the companies. Interviewee B added that it might be hard to motivate the investments needed to connect old equipment. Both interviewee B and C said that future machines need to be ready for smart manufacturing, the machines should already have advanced sensors and be able to connect to the cloud A, B and C also identified that the employees might have a resistance to change in their work. A and C agreed that if the operators are involved in the development and their competence digitized they will probably accept the changes easier. Interviewee B said that the actors also must overcome the economical obstacles and the stiffness in the industry.

26 6.2.7 Effects All four of the interviewees said that smart manufacturing will make production more efficient. Interviewee A, B and D said one part of this efficiency will come from predictive maintenance and if the machine knows that the process was correct there will be no need to check the quality of the product after the process. Interviewee A described the differences between older manufacturing streamlining and the streamlining from smart manufacturing. In the past process were streamlined by observing, listening as well as feeling and then some ad hoc solution were developed. The development with mereology and smart manufacturing leads to optimizing processes based on statistics derived from data. This development blurs the line between operator and engineer, the operator will do what the engineers have done until today. This will require the operators to have a higher competence than before. When you start to measure processes and machines you can see how they behave and you can make more accurate predictions about how the machine will work tomorrow and how the process will proceed in a near future. Both interviewee A and B said that if the machine can evaluate the process and detect any errors in the product the process can be stopped before any more machining is done on it, this will make the production more efficient. Interviewee B also added that analysis of data can detect deformities in quality in real time but also track the problems that originates with a problem to better find out the reason behind. To gather all the data from the processes and upload this information to a cloud makes it possible to control the production in real time. To save the variables and parameters that a product was exposed to with collaboration with the process data will make it easier to draw conclusions about a possible wreckage. With this information, a company can withdraw specified product from a batch instead of the whole batch. Interviewee A explained how investments can easier be motivated by using a simulation environment. With a simulation environment, a whole factory could be simulated and every change could be tested before it is implemented. Big investments would be easier to discuss and decisions would be easier to take if you know that the investment will work with the rest of the factory. This will also help managers see what investments that are important. The implementation process would also be shorter since all the tests already are done. With this digital model of the factory bottlenecks in the production can be illustrated and what steps in the production that are vulnerable to disturbances. Interviewee B and C both believed that the energy consumption will decrease with smart manufacturing. Interviewee B said that a more efficient controlling of materials and production may create more capacity in production. The company will benefit from a better availability, more efficient use of the raw material and a higher uptime in machines. This makes it beneficial to both the environment and the company's economic situation. Machines that are of no use to the production due to stops or bottlenecks will automatically be turned off to save energy. Interviewee C added that estimations of the ecological impact with the data collected from the processes can enable a more widespread environmental thinking.

27 7 Discussion The result from our research and survey are in line with what have been reported from other sources about smart manufacturing. The field is still in an early phase of implementation in various kinds of business. Actors that do not start to make a change now will most likely face the consequences of being a slow mover. Using metrology to enable this change towards smart manufacturing in an early state can be motivated by the economic benefits that will come from the various applications. The results from the survey are in line with the opportunities of the earlier presented applications. The industry needs better ways of predicting both their production and ways to be predictive with breakdowns and errors. These methods of measurement application rely on high standard of used measurement equipment that can be implemented in machines but we also have the conflict that the older machines in current production lack the opportunity to be fitted with said devices and connected to a cloud. From the interviews, we gained a lot of in depth information about the current state of production among leading companies. We were surprised about how far they had come in the applying the various solutions that we had been studying. The concepts that was theorized in recent scientific articles was already being investigated for implementation in current manufacturing and specifically CPPS had already seen some small scale implementation. The emergence of additional result regarding the profitability of these applications may further increase the speed of which companies will adapt the suggested applications to achieve smart manufacturing. The interviews also yielded examples regarding the problems with adapting workforce with the new technologies and applications. As the changes in workplace situations will probably become of a larger magnitude as the technological shift starts gaining momentum the investments from the companies to keep their employees educated and flexible will become important for a smooth transition. With the results of our report some matter of consideration must be done. We have only interviewed a few selected people and therefore there can exists a deviation from other reported material on the subject. From the limited number of subjects the basis regarding visualization of the result can seem biased as a single answer from one subject can be describes as a factor of 20% in the presented results. A basis for additional research one should gather more subjects to conduct both the survey and the following interview, preferably from companies of different sizes and geographical regions. This will create a better ground for comparison between companies and the possibility to examine trends in different business segments. The focus of our research has also only been on a small amount of applications of smart manufacturing made possible by the use of metrology that are interesting to gain an economical advantage on the competitive market. We also focused on the producing companies, additional research should involve the producers of the machine to get a more extensive analysis of the full chain of creating manufacturing. As our interviews only was from larger corporations and institutes the applications of smart manufacturing and metrology can be obsolete in smaller companies as the invested funds will never result in a proportional yield. Further, it must also be commented that the state of current and future manufacturing is a sensitive issue for the actors on the market. Anything that can be used to get a competitive edge from their competitors will not have been disclosed to us during our research as this report will be available to the public. Therefore, the results of this report must be taken with consideration as they cannot be viewed as a true reflection of the manufacturing market.

28 So: How can metrology support the development of smart manufacturing? From a strict observing point of view companies are producing wares solely to generate profit. Metrology can act as the enabler that combines both the aspect of having a profitable company and working towards succeeding with digitalization in manufacturing environments. The possibilities of acting on problems or opportunities before they occur in modern manufacturing compared to today’s general view of a more reactive production model. With the help of the different presented computerized solutions that are already available companies can start working towards smart manufacturing. The field of metrology is in its own way a great opportunity to create more sustainable solutions for the future in manufacturing. We see the aspect of creating a more efficient production system with less waste in raw material, energy consumption and machine time as future benefits for production with the help of metrology.

29 8 Conclusion The usage of metrology will be essential to get the starting point towards this change in manufacturing. As companies will try to get economic advantages in the early stages of adopting smart manufacturing, metrology will offer the way forward with the presented possible applications. Different solutions can be presented as how both researchers and companies handle the change towards a smart manufacturing business. The objective is to make the transition as lucrative as possible from a business perspective but also make it as soon as possible to make operator's work and managers decision easier, quicker and with more precision than before. The work towards digitalization in the manufacturing will take efforts also from the people involved, as previously mentioned. Changes in equipment may be expensive but are instantaneous. Changes in people and workforce requires more time to accept the transformation that will take place in production. There still exist a substantial need for initiative from the large corporations to further push us towards new technologies and to not adhere to the old philosophy of how to produce wares. They are the ones that can afford the implementation and the ones that will receive the greatest payback from it. To succeed with a higher level of digitalization in production and reap the following profits it is time to start with the implementation.

30 9 Acknowledgment We would like to thank all the people that have helped in the creation of this report. The individuals that we had the opportunity to consult both for the survey and the interview. We would also especially thank our supervisor Dr. Andreas Archenti of center for Design and Management of Manufacturing Systems and Ph.D. Candidate Károly Szipka, both at The Royal Institute of Technology in Stockholm.

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33 11 Appendix Appendix 1 - Survey questions 1. What do you think will be the main benefit of smart manufacturing in collaboration with metrology? 2. To enable smart manufacturing, characteristics of products, systems, environment etc. needs to be captured and fead upstream. From this point of view which characteristics need to be captured by measurement? 3. Should these characteristics be measured before, after or during the process? 4. How can smart manufacturing and metrology assist industrial practitioners in their work? 5. In what areas do you see easy/easier implementation and high yields for metrology in manufacturing? 6. What can be the role of smartness in metrology to support application (in your relevant working field)? 7. What are the biggest obstacles in the concept of smart manufacturing where metrology can offer solutions? 8. Can you describe the timeline for today’s factories to integrate metrology to the smart manufacturing concept? 9. What problems do you see for companies that will not adapt smart manufacturing? Both now and in the future. 10. How will smart manufacturing enable sustainability (both from an economic and environmental perspective)?

34 Appendix 2 - Questions Interview A 1. Vad kommer man kunna förutse? 2. Vad kommer man kunna jobba proaktivt med. 3. På vilket sätt blir processerna stabilare? a. Vad gör Predictivity , proactivity och process stability till de främsta fördelarna med smart manufacturing? 4. Vad för information från maskinen behöver du veta? Hur mäter du det? a. Vilken information från verktygen behöver du? Hur mäter man det? b. Product quality är brett, vad är det man ska mäta för att få information om kvaliteten på produkten? Hur mäter man det? Vad mäts i dagsläget? 5. Vad är det man analyserar? a. Vilken statistik är hjälpsam/relevant? b. Ser du några problem/motstånd med dessa typer av hjälpmedel? T. ex. Man måste byta arbetssätt/vanor och lära sig nya saker. Det innebär en omställning i arbetet 6. Varför tror du att smart manufacturing är lättast att nyttja inom underhåll? a. Finns det några andra områden som du tänker på? 7. Hur kan omställningen från analog till digital mätning se ut? 8. Vilka problem inom smart manufacturing kan metrologi lösa? 9. Kan du föreställa dig en tidslinje för implementationen av smart manufacturing? a. Ska man vara tidig med att implementera smart manufacturing eller ska man vänta tills det är beprövat? Vilka skillnader är det för stora och små företag samt är det skillnad på om företaget är världsledande eller ej? b. Är det tillverkande företag eller maskintillverkarna som styr tidsaspekten av implementationen av smart manufacturing? 10. Varför kommer dessa företag inte att vara lönsamma? 11. Hur kan detta locka aktörer till området? a. Vilka ekonomiska fördelar finns det?

35 Appendix 3 - Questions Interview B 1. Vilken fakta/data behöver vi veta för att ta ett beslut. Kan du beskriva en situation som är typisk för dig eller en stor aktör? a. Hur föreslår du att man ska mäta vibrationer, mekanisk förslitning och b. geometriska parametrar? c. Vilka produktionsparametrar är viktiga? 2. I vilket skede tror du det viktigast att mäta dessa egenskaper (products, systems, environment etc), under eller efter processen? 3. Har du några idér om hur en implementation i dessa maskiner kan gå till? (single and bottle-neck machines) 4. Vad finns det för stora hinder för att smart manufacturing kan implementeras och hur kan metrology lösa dessa hinder? 5. Om hur lång tid tror du att det kommer finnas maskiner som är gjorda för att kopplas upp mot IoT och samla in och analysera data? 6. Kommer de första företagen som implementera smart manufacturing gynnas mest eller är det mer gynnsamt att vänta tills det finns välfungerande och testade lösningar?

36 Appendix 4 - Questions Interview C 1. How do you think this foundation can be build? Lots of companies wants smartness in their production but may bypass important technological foundations. 2. How can the change from traditional measurements to digitalized and connected measurement devices be applied in today’s factories? 3. Companies have problems adapting their current machines to suit the needs of smart manufacturing. In today’s environment they instead are phasing out the older machines for newer models that have better modifications to allow smart manufacturing. 4. Can you think of any possible ways to connect the older machines to allow a quicker change towards enabling smart manufacturing? 5. These aspects will change the way employees work in a producing company. What are your ideas to help the people working there to easily implement and adapt to the changes in their working environment? 6. Why do you think it will be easier for automobile and electronics industries to implement metrology in smart manufacturing? 7. What will results in higher yields? (Cost efficiency, low waste, high machine uptime etc.) 8. How do you think an integration of a whole supply chain would work if everyone could share the data? Eg. machine producers get usage data from the production company and the production company gets user data from suppliers and end users. 9. What are the benefits? 10. What are the disadvantages? 11. These measurement instruments and sensors for proper measurement in complicated manufacturing processes, are they available in today’s market or will production companies have to invest to fit their specific needs? 12. How can productions companies then attract the desired employees with the education to create these instruments/sensors? 13. Will the concept of smart manufacturing be a field of many incremental innovations or a few radical changes? Please motivate why. 14. Those industries that adopt smart manufacturing late or not at all, will they lose competitiveness due to higher productions costs? 15. Will smart manufacturing and metrology enable a more widespread environmental thinking when it will be a possibility for companies to easier and in real-time measure their ecological impact?

37 Appendix 5 - Questions Interview D 1. What do you mean when you say through-put? 2. How is through-put one of the biggest issues for smart manufacturing? 3. How can this problem be solved? 4. Why do you think vibration and temperature drift will be important variables. 5. How will operators be assisted by smart manufacturing and metrology? Feel free to give one example. 6. How can metrology help to minimize stops in production? 7. Do you think that the concepts of VR/AR can help enable remote maintenance? 8. Do you think that the threat of security breaches stops companies from adopting smart manufacturing? 9. When do you think a machine (for example a milling or turning machine) will built to be fully connected to a cloud service, be able to measure all process parameters, feed that data to the cloud and receive feedback from the cloud to optimize the production process? 10. Will it be possible to measure a complex part direct in the milling/turning machine with a probe and get the same accuracy as with a 3D-measurement machine?