Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites |
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| Affiliation: | 1. Department of Mechanical Engineering, St. Mary’s Integrated Campus, Hyderabad, India;2. Department of Mechanical Engineering, TKR College of Engineering, Hyderabad, India;3. Department of Mechanical Engineering, JNTUH, Hyderabad, India;4. Department of Mechanical Engineering, RISE Krishna Sai Prakasam Group of Institutions, Ongole, India;1. CSIR-Central Mechanical Engineering Research Institute, Durgapur, WB 713209, India;2. Indian Institute of Technology Kharagpur, WB 721302, India;1. Department of Automatic Manufacturing Engineering, École de Technologie Supérieure, University of Quebec, 1100 Notre-Dame St W, Montreal, Québec H3C 1K3, Canada;2. Department of Mechanical Engineering, Polytechnique Montréal, University of Montreal, C.P. 6079, Succ. Centre-Ville, Montréal, Québec H3C 3A7, Canada;1. M.E.D, SVNIT, Surat, 395007, India;2. M.E.D, IIT Bombay, Powai, 400076, India;1. State Key Laboratory in Ultra-precision Machining Technology, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China;2. Shenzhen Research Institute of The Hong Kong Polytechnic University, Shenzhen, PR China |
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| Abstract: | The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r2 and root mean square error (RMSE), performed on training as well as checking datasets. |
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| Keywords: | ANFIS modelling Tool wear Machining Statistical performance Grid partitioning Subtractive clustering GFRP composites |
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