Multi objective optimization model of CNC turning for minimizing processing time and carbon emission with real machining application

Rosyidi, Cucuk Nur; Widhiarso, Wahyu; Pujiyanto, Eko Article Multi objective optimization model of CNC turning for minimizing processing time and carbon emission with real machining application Journal of Industrial Engineering and Management (JIEM) Provided in Cooperation with: The School of Industrial, Aerospace and Audiovisual Engineering of Terrassa (ESEIAAT), Universitat Politècnica de Catalunya (UPC) Suggested Citation: Rosyidi, Cucuk Nur; Widhiarso, Wahyu; Pujiyanto, Eko (2021) : Multi objective optimization model of CNC turning for minimizing processing time and carbon emission with real machining application, Journal of Industrial Engineering and Management (JIEM), ISSN 2013-0953, OmniaScience, Barcelona, Vol. 14, Iss. 2, pp. 376-390, https://doi.org/10.3926/jiem.3269 This Version is available at: https://hdl.handle.net/10419/261758 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Journal of Industrial Engineering and Management JIEM, 2021 – 14(2): 376-390 – Online ISSN: 2013-0953 – Print ISSN: 2013-8423 https://doi.org/10.3926/jiem.3269 Multi Objective Optimization Model of CNC Turning for Minimizing Processing Time and Carbon Emission with Real Machining Application Cucuk Nur Rosyidi1 , Wahyu Widhiarso2 , Eko Pujiyanto1 1Industrial Engineering Department, Universitas Sebelas Maret (Indonesia) 2Industrial Engineering, Universitas Jenderal Ahmad Yani Yogyakarta (Indonesia) [email protected], [email protected], [email protected] Received: July 2020 Accepted: February 2021 Abstract: Purpose: The purpose of this research is to develop an optimization model of CNC turning process. The objective function of the model is to minimize processing time and carbon emission. We implemented the results of optimization with real machining application using a certain workpiece. Design/methodology/approach: The model in this research used multi objective optimization involving two objective functions, namely processing time which includes cutting time and auxiliary time and carbon emissions resulted from the electricity energy consumptions, cutting tool, cutting fluid or coolant, raw materials production, and chip removal. Findings: The results of multi objective optimization indicate that the model can be used to minimize the processing time and carbon emissions with the optimal cutting speed and feed rate are 193.7 m/minute and 0.405 mm/rev. The results of sensitivity analysis showed that the higher weights of processing time will decrease the cutting speed, while the higher carbon emissions weight will result in faster cutting speed. The weight has no effects on feed rate. Originality/value: This paper gives a real machining application to show the applicability of the optimization model Keywords: multi objective optimization, prcess parameters, processing time, carbon emission, CNC turning, real machining application To cite this article: Rosyidi, C.N., Widhiarso, W., & Pujiyanto, E. (2021). Multi objective optimization model of CNC turning for minimizing processing time and carbon emission with real machining application. Journal of Industrial Engineering and Management, 14(2), 376-390. https://doi.org/10.3926/jiem.3269 1. Introduction In tight competition, efficiency is important for manufacturing companies to produce competitive product in the market (Berk, 2010). One way to achieve high efficiency is by the use of modern manufacturing technology such as CNC machines. CNC machining is a process method used in many manufacturing systems (Yi, Li, Tang & Chen, 2015). Two important CNC machining processes are milling and turning processes. According to Rochim (2007), -376- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.3269 turning process is a process forming of the workpiece by material removal a linearly (longitudinal, horizontal or angular). CNC turning machine is very effectively used to produce a workpiece in large quantities and in the same time some savings are obtained in the form of cutting tool, processing time, and machining costs (Subagio, 2012). In machining process, the cutting parameters, namely cutting speed, feed rate, and depth of cut will directly affect the workpiece surface quality, production efficiency, production cost, energy consumption, and carbon emissions (Rajemi, Mativenga & Aramcharoen, 2010; Yi et al., 2015; Liu, Sun, Lin, Zhao & Yang, 2016). In cutting process, the metal raw material, such as iron, steel, or aluminum has the highest energy consumption. The machining process such as milling, turning, and other metal cutting consumed the energy of about 66-82 MJ/kg which is 50% higher than the energy consumption of the forging and casting processes (Sun & Zhang, 2012). Carbons are emitted from the use of energy in those processes. Hence, reducing the carbon emissions in such processes is served as a strategy to create green production (Hassine, Barkallah, Bellacicco, Louati, Riviere & Haddar, 2015). Many studies have been conducted in CNC machining optimization model development to minimize several objectives, such as processing time, carbon emissions or combination of both objective functions. Rajemi et al. (2010) developed a total energy consumption model on turning process by optimizing tool life to minimize the energy consumption. Deepak (2012) developed an optimization model to minimize production time on turning process to determine the optimal value of cutting speed and feed rate. Sai, Charyulu and Nayak (2012) developed a multi-objective optimization model in the CNC turning to minimize the production time and production cost to find optimal cutting parameters, namely cutting speed and feed rate. The model was solved using Weighted Sum Genetic Algorithm. Li, Tang, Cui and Yi (2013) developed an analytical model to reduce the carbon emissions from various CNC machining processes. They considered several sources of carbon emissions such as electricity, cutting tool production, cutting fluid production, raw material production, and chip removal. In that research, the carbon emissions came from CNC machining systems are evaluated using various cutting speed. The research results indicated that faster cutting speed will increase the total carbon emissions. Jabri, Barkany and Khalfi (2013) developed a multi-objective optimization model of the turning multi-pass process to minimize cutting cost and tool life. The research considered cutting speed, feed rate, and depth of cut as the decision variables. Yi et al. (2015) developed a multi-objective optimization model to minimize production time and carbon emissions in the CNC machining. They considered cutting speed and feed rate as the decision variables. In that research, the production time consists of the cutting time, tool changing time, and auxiliary time. The auxiliary time are related with the approach and escape time of cutting tool. The carbon emissions are obtained from the electricity consumption, cutting tool, and cutting fluid. The carbon emissions of raw material and removal chip did not include in the model due to their insignificant effects on the cutting parameter. The model was then solved using Non-dominated Sorting Genetic Algorithm (NSGA II) method. Liu et al. (2016) developed a multi-objective optimization model in the cutting process to minimize processing time and carbon emissions to find the optimal value of cutting speed and feed rate. In that research, the machining costs included in the carbon emissions as the utility function to select and evaluate the optimal solution of the cutting parameters. The carbon emissions are resulted from the electricity energy consumption, cutting tool, and cutting fluid or coolant. The processing time consists of starting time, tool setting time, tool changing time, idle running time, and cutting time. The model was solved using Non-dominated Sorting GA (NSGA II) method. Hamada, Rosyidi and Jauhari (2017) developed a multi-objective optimization model of the CNC machining to minimize processing time and environmental impact with cutting speed and feed rate as the decision variables. In that research, the processing time comprises of cutting time, tool changing time, and auxiliary time. The environmental impact was obtained by converting the environmental burden into eco-indicator 99 unit using LCA (Life Cycle Assessment) approach as the basis of analysis. Several aspects of the environmental impacts were included in the model, namely energy (electricity and compressed air), water uses, and by-product (CO2, landfillable and hazardous waste, recyclable materials). Based on Hamada et al. (2017), Widhiarso and Rosyidi (2018) developed a multi-objective optimization model by modifying the objective function from production time into production cost for CNC turning process. Those two researches were solved using Oracle Crystal Ball software. The summary of the related literature review is shown in Table 1. -377- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.3269 Model Components Rajemi et al. (2010) Deepak (2012) Sai et al. (2012) Jabri et al. (2013) Li et al. (2013) Yi et al. (2015) Liu et al. (2016) Hamada et al. (2017) Widhiarso and Rosyidi (2018) This Research Objective Function Processing time       Carbon emission      Environmental impact   Production cost     Minimize tool life  Decision Variable Cutting speed           Feed rate           Depth of cut   Problem Solving NSGA II   Genetic Algorithm   Geometric Programming  Oracle Crystal Ball    Other Opti. Software   Processing Time Starting time     Tool setting time    Tool changing time         Idle running time   Cutting time           Tool quick return time  Carbon Emission Electricity energy consumption       Cutting tool production      Cutting fluid production     Raw material production    Material waste removal      Tool changing  Idle machine  Table 1. Summary of related research -378- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.3269 In this research, we extend the research of Widhiarso and Rosyidi (2018) with two extensions. First, we involve the carbon emission as one of the objective functions replacing for the environmental impact objective function. In their research, the environmental impact was measured using Eco Indicator 99 which not specifically measure carbon emission in detail. In this research we calculate a more detailed carbon emissions from electrical energy, the production of cutting tool, cutting fluid, and raw material, as well as chip removal. Second, we add a real machining to validate the optimization results. 2. Research Methodology 2.1. Multi Objective Optimization In this research, we employ multi objective optimization to model the machining process of a certain work piece. The multi objective optimizations are found in many fields of research. Emmerich and Deutz (2018) explained the basic mathematical foundation of multi objective optimization and the fundamentals and applications of several evolutionary algorithm in such optimization. According to Al-Jamimi, BinMakhashen, Deb and Saleh (2021), multi objective optimization allows for finding the optimal solutions directly and simultaneously to achieve the desired objectives by satisfying a set of constraints. Further, Audet, Bigeon and Cartier (2020) found 63 performance indicators in their review on multi objective optimization papers. Those indicators are then culstered into four groups according to their properties, cardinality, convergence, distribution and spread. They also presented the application of those indicators. In literature, a multi objective optimization problem may be solved using several methods and the most robust method is by using transformation function (Marler & Arora, 2004). The transformation is commonly used to aggregate the different units in the objective functions to become dimensionless. In this research, the objective functions has two of different units which should be transformed as can be expressed in Equation (33) (Koski, 1984; Koski & Silvennoinen, 1987; Rao & Freiheit, 1991). (33) In Equation (33), Fitrans is the transformed objective function which has the value between zero and one, Fi(x) is the original objective function, Fio and Fimax are the minimum and maximum value of the objective function respectively. Each objective function in multi objective optimization has different weight. The most common approach to multi objective optimization is the weighted sum method (Marler & Arora, 2010). The weighted sum (U) is the product of the objective function and weight of each objective function based on the value given by the decision maker (wi). The weights are determined based on the decision maker preference which shows the relative importance among the objective functions. The weighted sum with two objective functions can be expressed in Equation (34) as follow: (34) 2.2. Real Machining Application In this research, after the optimal solution was found, a real machining application is performed. The real machining application is used to show the applicability of the proposed model and validate the results of optimization. The cutting speed dan feed rate as the solution of the optimization model will be implemented in a real machining using Gedee Weiler Leanturn CNC lathe machine with workpiece material made from cylindrical Mild Steel (ST 370) as shown in Figure 1 and Figure 2 respectively. The diameter of the workpiece (D) is 20 mm and the turning length (Lj) is 15.7 mm (0.25πD). The surface roughness (Rmax) is required to be less than 6.4  m with the cutting depth (ajp) of 0.3 mm and the maximum cutting force (Fmax) of 9000 N. The specification of the machine is shown in Table 2. -379- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.3269 Parameters Specifications Spindle motor power, P (kW) 3.7 Spindle speed, nmin - nmax (rev/min) 50 - 5000 Feed velocity maximum, vfmax (mm/min) 6000 Rapid moving speed (m/min) X axis= 20 Z axis= 25 Machine Efficiency, η0.8 Table 2. Specification of Leanturn CNC Lathe Figure 1. Leanturn CNC lathe The cutting fluid or coolants are required during the machining process to cooling the heat resulting from the cutting tool and workpiece, and minimizing chip on cutting tool edges. The type of cutting fluid or coolants used during the machining process is Emulkat 500 mixed with water. The machine will process a radius cutting and done feeding one time as depicted in Figure 2. Figure 2. The workpiece -380- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.3269 3. Model Development In this section we present the model development both objective functions and constraints. Hence, the optimization model has two objectives and will be solved using multi objective optimization method. 3.1. Processing Time The processing time (Tp) used in this paper refers to Liu et al. (2016) in which expressed as the sum of auxiliary time (tot) and cutting time (tm). (1) The auxiliary time (tot) in the machining process consists of starting time, tool setting time, tool changing time, and idle running time which can be expressed as in Equation (2). (2) The tool life (Tt) is determined based on Taylor’s equation formula and can be expressed in Equation (3) (Kalpakjian & Schmid, 2003). (3) In Equation (3), CT is coefficient of machining parameters, x, y, z are exponent of cutting speed, feed rate, and cutting depth. The idle running time (t4) is the temporary idle operation during the turning process and can be expressed in Equation (4). In the processing time, the feed time of cutting tool in the idle operation considered as the auxiliary time (Liu et al., 2016). (4) The cutting time related with the length of turning process and machining parameter (Yi et al., 2015) and obtained from the sum of every turning process. The turning process time (tjm) to process a radius can be expressed in Equation (5) and the cutting time (tm) is shown in Equation (6). (5) (6) Therefore, the proposed model of processing time (Tp) can be expressed as follows: (7) -381- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.3269 3.2. Carbon Emissions The carbon emissions of a CNC machining process are generated from the emissions of electricity, the production of cutting tool, cutting fluid, and raw material, and the chip removal (Li et al., 2013). In this research, the carbon emissions refers to Liu et al. (2016) for emissions from the electricity energy, cutting tool, and cutting fluid or coolant, while raw material production and chip removal refers to Li et al. (2013). The proposed carbon emissions (CE) model can be calculated by Equation (8) as follows: (8) 3.2.1. Carbon Emissions of Electricity Energy The carbon emissions from the electricity energy (CEe) can be calculated by Equation (9) (Jeswiet & Kara, 2008). (9) The total electricity energy (E) in machining process consists of the energy consumption in the auxiliary machining and cutting process which can be expressed in Equation (10). (10) The energy consumption of the auxiliary machining (E1) can be calculated by Equation (11) (Liu et al., 2016). The energy consumption of machine starting process (E00) can be shown in Equation (12), while the idle power of machine (P01) related to spindle speed and its specification as expressed in Equation (13) (Liu, Hu, He & Hu, 2012). (11) (12) (13) The energy consumption in the cutting process (E2) can be calculated using Equation (14) (Liu et al., 2016). Xu, Wang, Teng, Zhong and Teng (2015) explained that the power of machine can produce the actual cutting power (Pc) and the additional load power (Pa) during machining process. The additional load power (Pa) can be expressed in Equation (15). The actual cutting power (Pc) is expressed as the function of cutting force (Fc) and cutting speed (vc) as shown in Equation (16), while the cutting force (Fc) can be calculated using Equation (17). (14) (15) (16) (17) -382- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.3269 In Equation (14) and (17), xFC, yFC, nFC are coefficients of depth of cut, feed rate, and cutting speed, CFC is cutting force coefficient of workpiece material, KFC is factor influence coefficient of cutting force. 3.2.2. Carbon Emissions of Cutting Tool Production The carbon emissions generated from the cutting tool production (CEt) comes from tool material extraction and tool manufacturing which can be calculated in Equation (18) (Liu et al., 2016): (18) 3.2.3. Carbon Emissions of Cutting Fluid Production The carbon emissions generated from the cutting fluid production (CEc) consists of the production process, waste fluid removal, and electricity energy consumed to supply pump. The cutting fluid supply time assumed as the total processing time comprises of the auxiliary time and cutting time (Liu et al., 2016): (19) 3.2.4. Carbon Emissions of Raw Material Production The CNC machine is an automatic machine to process material to become semi-finished or finished products. The carbon emissions of raw material production (CEm) removed is calculated by Equation (20) (Li et al., 2013). (20) The embodied material energy is then converted into the standard coal with the content of coal carbon which can be determined as in Equation (21) (Li et al., 2013): (21) The removed material (Mchip) is measured by calculating the difference between mass of the raw material and semifinished or finished products which can be expressed in Equations (22) and (23) respectively (Li et al., 2013). In Equation (22) and (23), Q is removal rate. (22) (23) 3.2.5. Carbon Emissions of Chip Removal The chip recycle is often used to recover the raw material. The carbon emissions generated from the electricity to recycling process can be expressed in Equations (24) and (25) respectively (Li et al., 2013). (24) (25) The proposed carbon emissions (CE) model can be expressed as follows: -383- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.3269 Rosyidi, C.N., Fatmawati, A., & Jauhari, W.A. (2016). An Integrated Optimization Model for Product Design and Production Allocation in A Make to Order Manufacturing System. International Journal of Technology, 7(5), 819-830. https://doi.org/10.14716/ijtech.v7i5.1173 Sai, N.K., Charyulu, T.N., & Nayak, M.S. (2012). Multi Objective Optimization of Machining Parameters by Using Weighted Sum Genetic Algorithm Approaches. International Journal of Engineering Research & Technology (IJERT), 1, 1-7. Subagio, D.G. (2012). Application and CNC Programming in Industrial World. Bandung: Informatika (in Bahasa). Sun, Q., & Zhang, W. (2012). Carbon Footprint Analysis in Metal Cutting Process. Proceedings of International Conference on Mechanical Engineering and Material Science (MEMS 2012) (619-622). Technical Data Formula for Cutting Power. Specific Cutting Force, G015. Available at: http://www.teraskonttori.fi/easydata/customers/teraskonttori/files/lastuavat/Mitsu_14_TECHNICAL_DATA.pdf (Accessed: March 2018.) Walsh, R.A., & Cormier, D.R. (2006). McGraw-Hill Machining and Metalworking Handbook (3rd ed.). McGraw-Hill Companies, Inc. Widhiarso, W, & Rosyidi, C.N. (2018). Multi Objective Optimization Model for Minimizing Production Cost and Environmental Impact in CNC Turning Process. 3th International Conference on Industrial, Mechanical, Electrical, and Chemical Engineering, AIP Conference Proceedings (1931, 1-7). https://doi.org/10.1063/1.5024083 Xu, Z.Z., Wang, Y.S., Teng, Z.R., Zhong, C.Q., & Teng, H.F. (2015). Low-carbon Product Multi-objective Optimization Design for Meeting Requirements of Enterprise, User, and Government. Journal Cleaner Production, 103, 747-758. https://doi.org/10.1016/j.jclepro.2014.07.067 Yi, Q., Li, C., Tang, Y., & Chen, X. (2015). Multi-Objective Parameter Optimization of CNC Machining for Low Carbon Manufacturing. Journal of Cleaner Production, 95, 256-264. https://doi.org/10.1016/j.jclepro.2015.02.076 Journal of Industrial Engineering and Management, 2021 (www.jiem.org) Article’s contents are provided on an Attribution-Non Commercial 4.0 Creative commons International License. Readers are allowed to copy, distribute and communicate article’s contents, provided the author’s and Journal of Industrial Engineering and Management’s names are included. It must not be used for commercial purposes. To see the complete license contents, please visit https://creativecommons.org/licenses/by-nc/4.0/. -390-