Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling
Sustainability plays an important role in manufacturing industries through economically-sound processes that able to minimize negative environmental impacts while having the social benefits. In this present study, the modeling of wire electrical discharge machining (WEDM) cutting process using an artificial neural network (ANN) for prediction has been carried out with a focus on sustainable production. The objective was to develop an ANN model for prediction of two sustainable measures which were material removal rate (as an economic aspect) and surface roughness (as a social aspect) of titanium alloy with ten input parameters. By concerning environmental pollution due to its intrinsic characteristics such as liquid wastes, the water-based dielectric fluid has been used in this study which represents an environmental aspect in sustainability. For this purpose, a feed-forward backpropagation ANN was developed and trained using the minimal experimental data. The other empirical modelling techniques (statistics based) are less in flexibility and prediction accuracy. The minimal, vague data and nonlinear complex input-output relationship make this ANN model simple and perfects method in the manufacturing environment as well as in this study. The results showed good agreement with the experimental data confirming the effectiveness of the ANN approach in the modeling of material removal rate and surface roughness of this cutting process.
Atzeni E, Bassoli E, Gatto A, Iuliano L, Minetola P, Salmi A, Surface And Sub Surface Evaluation In Coated-Wire Electrical Discharge Machining (WEDM) Of INCONELÂ® Alloy 718. Procedia CIRP, Vol. 33, pp. 388â€“393, 2015.
Saedon JB, Jaafar N, Jaafar R, Saad NH, Kasim MS, Modeling And Multi-Response Optimization On WEDM Ti6Al4V. Applied Mechanics and Materials, Vol. 510, pp. 123â€“129, 2014.
Rahim EA, Sasahara H, Surface Integrity In MQL Drilling Nickel-Based Superalloy. Key Engineering Materials, Vols 447â€“448, No. 1, pp. 811â€“815, 2010.
Gamage JR, De Silva AKM, Assessment Of Research Needs For Sustainability Of Unconventional Machining Processes. Procedia CIRP, Vol. 26, pp. 385â€“390, 2015.
Mahalil K, Rahim EA, Mohid Z, Performance Evaluation Of Sustainable Coolant Techniques On Burnishing Process. IOP Conference Series: Materials Science and Engineering, Vol. 494, pp. 1-9, 2019.
Kellens K, Renaldi, Dewulf W, Duflou JR, Preliminary Environmental Assessment Of Electrical Discharge Machining. Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Berlin, pp. 377â€“382, 2011.
Kou Z, Han F, On Sustainable Manufacturing Titanium Alloy By High-Speed EDM Milling With Moving Electric Arcs While Using Water-Based Dielectric. Journal of Cleaner Production, Vol. 189, pp. 78â€“87, 2018.
Ariffin MKAM, Che Hussain HB, Mohamed SB, Sulaiman S, Determining Optimum Electro Discharge Machining Parameters For Drilling Of A Small Hole By Utilizing Taguchi Method. Applied Mechanics and Materials, Vol. 564, No. 1, pp. 481â€“487, 2014.
Singh NK, Pandey PM, Singh KK, Sharma MK, Steps Towards Green Manufacturing Through EDM Process: A Review. Cogent Engineering, Vol. 3, No. 1, pp. 1â€“13, 2016.
Izamshah R, Akmal M, Kasim MS, Mohamed SB, Experimental Analysis On Parameters Affecting The Material Removal Rate In Wire Electrical Discharge Turning Using The Taguchi Method. International Journal of Mechanical & Mechatronics Engineering, Vol. 18, No. 5, pp. 75â€“82, 2018.
Ming W, Zhang Z, Zhang G, Huang Y, Guo J, Chen Y, Multi-Objective Optimization Of 3D-Surface Topography Of Machining YG15 In WEDM. Materials and Manufacturing Processes, Vol. 29, No. 5, pp. 514â€“525, 2014.
Saedon JB, Jaafar N, Yahaya MA, Characteristics Of Machining Parameters On WEDM Titanium Alloy. Materials Science Forum, Vol. 872, pp. 23â€“27, 2016.
Sharma N, Raj T, Jangra KK, Parameter Optimization And Experimental Study On Wire Electrical Discharge Machining Of Porous Ni 40 Ti 60 Alloy. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 231, No. 6, pp. 956â€“970, 2017.
Rao RV, Kalyankar VD, Optimization Of Modern Machining Processes Using Advanced Optimization Techniques: A Review. International Journal of Advanced Manufacturing Technology, Vol. 73, Nos. 5â€“8, pp. 1159â€“1188, 2014.
Izamshah R, Akmal M, Kasim MS, Sundi SA, Hadzley M, A Statistical Comparison In Selection Of Wire-EDM Process Parameters For Machining Titanium Alloy. Journal of Advanced Manufacturing Technology (JAMT), Vol. 10, No. 2, pp. 45â€“55, 2016.
Siswoyo A, Arief Z, Sulistijono IA, Application Of Artificial Neural Networks In Modeling Direction Wheelchairs Using Neurosky Mindset Mobile ( EEG ) Device. EMITTER International Journal of Engineering Technology, Vol. 5, No. 1, pp. 170â€“191, 2017.
Chiroma H, Abdul-Kareem S, Shukri Mohd Noor A, Abubakar AI, Sohrabi Safa N, Shuib L, Fatihu Hamza M, Yaâ€™u Gital A, Herawan T, A Review On Artificial Intelligence Methodologies For The Forecasting Of Crude Oil Price. Intelligent Automation and Soft Computing, Vol. 22, No. 3, pp. 449â€“462, 2016.
Shandilya P, Jain PK, Jain NK, RSM And ANN Modeling Approaches For Predicting Average Cutting Speed During WEDM Of SiCp/6061 Al MMC. Procedia Engineering, Vol. 64, pp. 767â€“774, 2013.
Devarasiddappa D, George J, Chandrasekaran M, Teyi N, Application Of Artificial Intelligence Approach In Modeling Surface Quality Of Aerospace Alloys In WEDM Process. Procedia Technology, Vol. 25, No. Raerest, pp. 1199â€“1208, 2016.
Hasan MS, Ebrahim Z, Wan Mahmood WH, Ab Rahman MN, Sustainable -ERP System: A Preliminary Study On Sustainability Indicators. Journal of Advanced Manufacturing Technology, Vol. 1, pp. 61â€“74, 2017.
Muthukrishnan N, Davim JP, Optimization Of Machining Parameters Of Al / SiC-MMC With ANOVA And ANN Analysis. Journal of Materials Processing Technology, Vol. 209, No. 1, pp. 225â€“232, 2009.
Izamshah R, Akmal M, Kasim MS, Sued MK, Sundi SA, Amran M, Parametric Study On Parameter Effects In Hybrid Micro Wire Electrical Discharge Turning. Journal of Advanced Manufacturing Technology (JAMT), Vol. 12, No. 1, pp. 1â€“12, 2018.
Abu NH, Jaya ASM, Muhamad MR, Performance Analysis Of Neural Network Models For Sustainable Manufacturing Practices (SMP) And Economy Performances. Proceedings of Mechanical Engineering Research Day 2016, Vol. 2016, pp. 46â€“47, 2016.
Boukezzi F, Noureddine R, Benamar A, Noureddine F, Modelling, Prediction And Analysis Of Surface Roughness In Turning Process With Carbide Tool When Cutting Steel C38 Using Artificial Neural Network. International Journal of Industrial and Systems Engineering, Vol. 26, No. 4, pp. 567-583, 2017.
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