Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)

• Published in: Journal of intelligent manufacturing Vol. 28; no. 8; pp. 1769 - 1785
• Main Authors: Abhishek, Kumar, Rakesh Kumar, V., Datta, Saurav, Mahapatra, Siba Sankar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2017; Springer Nature B.V
• Subjects: Algorithms; Business and Management; Carbon fiber reinforced plastics; Control; Cutting force; Cutting parameters; Experimental research; Feed rate; Fiber orientation; Functions (mathematics); Genetic algorithms; Machine learning; Machine shops; Machines; Machining; Manufacturing; Material removal rate (machining); Mathematical models; Mechatronics; Optimization; Optimization algorithms; Polymer matrix composites; Process parameters; Processes; Production; Robotics; Surface roughness; Turning (machining); Carbon fibre reinforced polymer (CFRP); Teaching–learning based optimization (TLBO); Machining
• ISSN: 0956-5515; 1572-8145
• DOI: 10.1007/s10845-015-1050-8

The present paper focuses on machining (turning) aspects of CFRP (epoxy) composites by using single point HSS cutting tool. The optimal setting i.e. the most favourable combination of process parameters (such as spindle speed, feed rate, depth of cut and fibre orientation angle) has been derived in view of multiple and conflicting requirements of machining performance yields viz. material removal rate, surface roughness, SR ( R a ) (of the turned product) and cutting force. This study initially derives mathematical models (objective functions) by using statistics of nonlinear regression for correlating various process parameters with respect to the output responses. In the next phase, the study utilizes a recently developed advanced optimization algorithm teaching–learning based optimization (TLBO) in order to determine the optimal machining condition for achieving satisfactory machining performances. Application potential of TLBO algorithm has been compared to that of genetic algorithm (GA). It has been observed that exploration of TLBO appears more fruitful in contrast to GA in the context of this case experimental research focused on machining of CFRP composites.