A Novel Approach for Minimization of Tool Vibration and Surface Roughness in Orthogonal Turn Milling of Silicon Bronze Alloy

The advanced manufacturing system is aimed to produce components at right quantity, quality and cost. Turnmilling is one of the advanced machining techniques that combines turning and milling processes for high metal removal rate. In orthogonal turn milling, bottom surface of the end mill cutter removes material from the surface of a rotating workpiece. Optimization of process parameters plays an important role in machining to improve quality and productivity and reduce production cost. In the present work, an advanced teaching learning based optimization (TLBO) technique was introduced to optimize process parameters in orthogonal turn milling of Silicon Bronze. Experiments were conducted at five levels of cutting speed, feed and depth of cuts. Experimental results of surface roughness and amplitude of cutter vibration were analysed using analysis of variance. The experimental results were also used to optimize process parameters through TLBO. Experiments were also conducted using TLBO optimized process parameters and the results were compared with TLBO results. The TLBO results were found to be in good agreement with target values of the responses. Artificial neural networks were developed for the surface roughness and amplitude of cutter vibration to verify optimization.