Mitigated cutting force and surface roughness in titanium Alloy-Multiple effective guided chaotic multi objective Teaching learning based optimization

Abstract

Titanium alloys has significance in engineering applications owing to their enhanced properties and its ability to retain the shape at elevated temperatures. A new Teaching Learning Based Optimization (TLBO) variant was developed with multiple search features to mitigate cutting force and surface irregularities in Titanium samples. This assists to achieve the best quality of the product at minimal cutting energy. Experiments were conducted and the significance of machining parameters on cutting force and surface finish were analyzed. It is ascertained that the best surface is attained at a lower tool feed rate with higher cutting speed. The increase of nose radius has more influence on the surface quality. Chaotic multiobjective TLBO with multiple effective guidance was applied in both single objective and multiobjective optimization, where useful information of other non-fittest learners is leveraged for effective more population search. The performance of the new algorithm was evaluated and comprehensively discussed. The minimum cutting force Fz=65.06N and Ra=1.41μm can be achieved with v =130m/min, f=0.051mm/rev, nr=0.4mm and ap=0.5mm. The predicted results were validated experimentally and verified with other existing optimizers. It is concluded that this new algorithm can be applied in machining and production wastage can be greatly minimized.

Keywords

• Machining
• Coated tools
• Cutting force
• Surface roughness
• RSM
• Chaotic