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Optimization of Cutting Parameters to Minimize the Surface Roughness In PP Periodica Polytechnica Optimization of Cutting Parameters Mechanical Engineering to Minimize the Surface Roughness in the End Milling Process Using the 61(1), pp. 30-35, 2017 Taguchi Method DOI: 10.3311/PPme.9114 Creative Commons Attribution b João Ribeiro1*, Hernâni Lopes2, Luis Queijo1, Daniel Figueiredo3 research article Received 23 February 2016; accepted after revision 21 September 2016 Abstract 1 Introduction This paper presents a study of the Taguchi design application The machining is one of the most important manufacturing to optimize surface quality in a CNC end milling operation. processes in the industry today. This is usually defined as the The present study includes feed per tooth, cutting speed and process of removing material from a workpiece or part in the radial depth of cut as control factors. An orthogonal array form of chips. A mechanical part could be machined using dif- of L9 was used and the ANOVA analyses were carried out to ferent techniques without major differences in the final results. identify the significant factors affecting the surface roughness. However, the efficiency is not the same for all techniques. In The optimal cutting combination was determined by seeking other words, to obtain the same part, there is a machining tech- the best surface roughness (response) and signal-to-noise nique most appropriate that gives the best quality for the lower ratio. The study was carried-out by machining a hardened machining time and energy consumption. The choice of the tech- steel block (steel 1.2738) with tungsten carbide coated tools. nique depends on the goal to be achieved and, for machining, is The results led to the minimum of arithmetic mean surface related to the part material and its geometry, as well as, machines roughness of 1.662 µm, being the radial depth of cut the most and tools available. According to the machining goal and the influent parameter, with 64% of contribution for the workpiece choice of cutting tool, there are different combinations of param- surface finishing. eters, like feed rate, spindle speed, axial or radial depth of cut to obtain different results in terms of quality of machined surface Keywords and tool wear [1-3]. Each cutting parameters combination will milling, cutting parameters, optimization, surface roughness, result in a different surface roughness and tool life. However, for Taguchi method many different parameters to control with several levels for each one is very difficult to define the best combination that provides the lower surface roughness and maximum tool life. The most important features in the manufacturing industry is to predict the surface quality, tool life and the manufacture costs for a particu- lar combination of machining parameters. The quality of machined surface is evaluated by the surface roughness of the machined part, being the most critical qual- ity characteristic. Some researchers tried to predict the surface roughness of machining process through mathematical algo- rithms [4-6], but those studies are very slow and expensive since they required a large number of experimental tests, using several parameters combinations. Nevertheless, an interesting 1 Department of Mechanical Technology solution to minimize the combinations number of experimental Polytechnic Institute of Bragança, ESTIG/IPB Campus Sta. Apolónia, 5301-857 Bragança, Portugal tests could be the implementation of optimization techniques. 2 Department of Mechanical Engineering In the last two decades were instigated many numerical tech- Polytechnic Institute of Porto, DEM/ISEP, niques applied to the optimization of machining parameters [7] R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal being the most frequently used the fuzzy logic [8], the genetic 3 Development Department, Palbit, Hardmetals Tools Solutions, algorithms [9] and the Taguchi method [10-12]. In this work is R. das Tílias, 3854-908 Branca Alb.-a-Velha, Portugal implemented the Taguchi method for surface roughness opti- * Corresponding author, e-mail: [email protected] mization in an end-milling operation. 30 Period. Polytech. Mech. Eng. J. Ribeiro, H. Lopes, L. Queijo, D. Figueiredo The Taguchi approach [13, 14] is based in statistical analysis of this method is to produce high quality product at low cost to of tests that can economically satisfy the product or process for the manufacturer. a defined optimal design. One advantage of this method is that The key idea behind this method is the quality of a product multiple factors are considered at once, including noise factors. required by the society is same how related to the loss caused Taguchi method when combined with other statistical tools, during its life cycle. A high quality product will cause less loss like analysis of variance (ANOVA), principal component anal- to the society. The loss can be measured in different parameters ysis (PCA) [15] or grey relational analysis [16], transforms into like time or noise. In general, if the product result is not as the a powerful tool for optimization of the machining parameters. consumer expects, the loss is big since the quality is far from Some authors have studied the turning machining process the user expectations. So, this is not desirable for the industry, associated with Taguchi method [16-19] to optimise the most neither for the society. common controllable parameters, such as: cutting speed, feed Taking into account the quality needed, Taguchi included in rate and depth of cut. The main goal is to reduce the surface his method some loss functions that recognize the customer’s roughness, through the application of Taguchi method based on desire to have products that are more consistent and meets the the highest signal-to-noise ratio for surface roughness. Other producer’s desire to make them low-cost. studies have focused in the milling process [15, 20-22], being Taguchi’s philosophy says that quality should be designed one of the most used metallic chips removal processes in indus- into a product, not inspected into it. This is achieved through try. As in turning process, the main controllable factors are the a system design, parameter design and tolerance design. If a feed rate, depth of cut and cutting speed (or spindle speed) and, producer decides to choose the quality “inspected into” a prod- for the most of this works, the authors used three levels for uct, it means the product is produced at random quality levels each factor through the use of Taguchi L9 array. In many stud- and those are too far from the user desired levels. Quality is ies of milling optimization using Taguchi method referenced more easily achieved by the reduction of deviation from a tar- in the bibliography, the surface roughness measurements are get, through the minimization of the influence of uncontrolla- performed on the surface perpendicular to tool axis, usually ble factors. This means one should have a high signal-to-noise horizontal [15, 20, 21] or on both surfaces, perpendicular and ratio (S/N). parallel to tool axis, and is computed the average value [22]. Taguchi considered three categories of the performance However, the contour operation is very common in metallic characteristic in the analysis of the S/N, namely: nominal is machining and is important to control the lateral surface rough- the best, larger is the better and smaller is the better. The follow ness which can be measured in parallel direction of toll axis. In equations define mathematically the referred categories. this work the authors analysed the influence of each machin- Nominal is the best ing parameters in the surface roughness on parallel direction 2 y SN= 10*log of tool axis for the end milling operation. The main goal is t 2 (1) sy the determination of the optimal combination of milling param- eters to obtain the lower surface roughness value. Larger is the better (maximize) 11n =−10*log 2 Taguchi method for design of experiments and SNL ∑ 2 (2) nyi=1 i experimental details 2.1 Taguchi method Smaller is the better (minimize) Genichi Taguchi (Tokamachi, Japan, 1924–2012) was a statis- 1 n 10*log 2 tician and an engineer. He developed a methodology to improve SNs =− ∑yi (3) n i=1 the quality of manufactured goods by using a fractional-facto- 2 rial approach whenever there are several factors involved and where y is the average of observed data, sy is the variance of is accomplished with the aid of orthogonal arrays. Taguchi has y, n is the number of observations and y is the observed data. made influential contribution to industry. The key elements of The S/Nt is applied when the objective is to reduce variabil- his quality philosophy include the following aspects: ity around a specific target, S/NL is used if the system is opti- • The philosophy of off-line quality control, robust design mized when searching for the large as possible response and of products and processes; S/NS when the response is as small as possible. For each type • Taguchi loss function; of the characteristics, with the above S/N ratio transformation, • Innovations in the statistical design of experimental tests. the higher the S/N ratio the better is the result. The S/NS was selected for this study, since the objective is The Taguchi method involves reducing the variation in a pro- to improve the surface finishing of side milling workpiece, by cess through robust design of experiments. The main objective measuring the surface roughness. Optimization of Cutting Parameters 2017 61 1 31 2.2 Design of experiments Table 2 Taguchi L9 array. The first step for design the experiments is to identify the Test A B C D quality characteristics and to select the process parameters. In Number Cutting Speed Feed rate Radial depth cut Error the machining processes, the most common controlled param- 1 1 1 1 1 eters are the cutting speed (V ), feed rate (F ) and radial depth c z 2 1 2 2 2 of cut (a ).
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