Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining

End milling is one of the most common metal removal operations encountered in industrial processes. Product quality is a critical issue as it plays a vital role in how products perform and is also a factor with great influence on manufacturing cost. Surface roughness usually serves as an indicator of product quality. During cutting, surface roughness measurement is impossible as the cutting tool is engaged with the workpiece, chip and cutting fluid. However, cutting force measurement is easier and could be used as an indirect parameter to predict surface roughness. In this research work, a correlation analysis was initially performed to determine the degree of association between cutting parameters (speed, feed rate, and depth of cut) and cutting force and surface roughness using adaptive neuro-fuzzy inference system (ANFIS) modeling. Furthermore, the cutting force values were employed to develop an ANFIS model for accurate surface roughness prediction in CNC end milling. This model provided good prediction accuracy (96.65 % average accuracy) of surface roughness, indicating that the ANFIS model can accurately predict surface roughness during cutting using the cutting force signal in the intelligent machining process to achieve the required product quality and productivity.

[1]  Yoshio Saito,et al.  Cutting force reduction and surface quality improvement in machining of aerospace duralumin AL-2017-T4 using carbon onion nanolubrication system , 2012, The International Journal of Advanced Manufacturing Technology.

[2]  Aitzol Lamikiz,et al.  Machine Tools for High Performance Machining , 2009 .

[3]  John S. Agapiou,et al.  Metal Cutting Theory and Practice , 1996 .

[4]  P. K. Philip,et al.  Built-up edge phenomenon in machining steel with carbide , 1971 .

[5]  A. Sarhan,et al.  A fuzzy logic based model to predict surface hardness of thin film TiN coating on aerospace AL7075-T6 alloy , 2013 .

[6]  Joseph C. Chen,et al.  Development of a fuzzy-nets-based in-process surface roughness adaptive control system in turning operations , 2006, Expert Syst. Appl..

[7]  J. Paulo Davim,et al.  Machining : fundamentals and recent advances , 2008 .

[8]  Tuğrul Özel,et al.  Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks , 2005 .

[9]  Ahmed A. D. Sarhan,et al.  An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish , 2012 .

[10]  Thomas Childs,et al.  Metal Machining: Theory and Applications , 2000 .

[11]  Ahmed A. D. Sarhan,et al.  Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling , 2014 .

[12]  İlhan Asiltürk.  Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression , 2012 .

[13]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[14]  Jose Vicente Abellan-Nebot,et al.  A review of machining monitoring systems based on artificial intelligence process models , 2010 .

[15]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[16]  S. Kumanan,et al.  Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness , 2008 .

[17]  Joseph C. Chen,et al.  The development of an in-process surface roughness adaptive control system in end milling operations , 2007 .

[18]  Lieh-Dai Yang,et al.  Fuzzy-nets-based in-process surface roughness adaptive control system in end-milling operations , 2006 .

[19]  I. M. Soltan,et al.  Surface roughness prediction in end milling using multiple regression and adaptive neuro-fuzzy inference system , 2015 .

[20]  Ching-Kao Chang,et al.  Study on the prediction model of surface roughness for side milling operations , 2006 .

[21]  P. Srinivasa Pai,et al.  The effect of built-up edge on the cutting vibrations in machining 2024-T351 aluminum alloy , 2010 .

[22]  Ahmed A. D. Sarhan,et al.  Enhancement and verification of a machined surface quality for glass milling operation using CBN grinding tool—Taguchi approach , 2012 .

[23]  Tamas Szecsi,et al.  Cutting force modeling using artificial neural networks , 1999 .

[24]  R. Ramesh,et al.  Automated intelligent manufacturing system for surface finish control in CNC milling using support vector machines , 2009 .

[25]  Patricia Muñoz-Escalona,et al.  Built-up edge effect on tool wear when turning steels at low cutting speed , 2004 .

[26]  Xiaowen Wang,et al.  Development of Empirical Models for Surface Roughness Prediction in Finish Turning , 2002 .

[27]  Pramod Kumar Jain,et al.  In-process prediction of surface roughness in turning of Ti–6Al–4V alloy using cutting parameters and vibration signals , 2013 .

[28]  Uday S. Dixit,et al.  Application of soft computing techniques in machining performance prediction and optimization: a literature review , 2010 .

[29]  Robert Fullér,et al.  Neural Fuzzy Systems , 1995 .

[30]  José Antonio Sánchez,et al.  Wire Electrical Discharge Machines , 2009 .