Multi Response Optimization Using ANOVA And Desirability Function Analysis: A Case Study In End Milling Of Inconel Alloy.

ABSTRACT Nickel-based super alloys are classified as 'difficult to machine' materials due to its inherent characteristics such as high hardness, and toughness, high strength at elevated temperatures, low thermal conductivity, ability to react with cutting inserts, and ability to weld onto the surface of the cutting insert. The present study investigated the parameter optimization of end milling operation for Inconel 718 super alloy with multi-response criteria based on the Taguchi method and desirability function analysis. Experimental tests were carried out based on an L9 orthogonal array of Taguchi method. The influence of machining factors cutting speed, feed rate and depth of cut were analyzed on the performances of surface roughness and material removal rate. The optimum cutting conditions are obtained by Taguchi method and desirability function. The analysis of variance (ANOVA) is also applied to investigate the effect of influential parameters. A regression model was developed for surface roughness and material removal rate as a function of cutting velocity, feed rate and depth of cut. Finally, the confirmation experiment was conducted for the optimal machining parameters, and the betterment has been proved

Part segregation based on particle swarm optimisation for assembly design in additive manufacturing

Minimising total production time in the additive or layered manufacturing is a critical concern, and in this respect, the idea of balancing assembly time and build time is rapidly gaining research attention. The proposed work intends to provide benefit in terms of reduced lead time to customers in a collaborative environment with simultaneous part printing. This paper formulates a mixed-integer non-linear programming (MINLP) model to evaluate the near optimal threshold area and support material allocation while segregating parts for a single material additive manufacturing set-up. The resulting time minimisation model is finitely bounded with respect to support material volume, total production time and total assembly cost constraints. A novel swarm intelligence-based part segregation procedure is proposed to determine the number of part assemblies and part division scheme that adheres to cross-sectional shape, cross-sectional area, and height restrictions. The proposed approach is illustrated and evaluated for objects with regular as well as free-form surfaces using two different hypothetically simulated real size 3D models. Results indicate that the proposed approach is able to reduce the total amount of manufacturing time in comparison with single part build time for all the tested cases