Improving the process performance of magnetic abrasive finishing of SS304 material using multi-objective artificial bee colony algorithm

Magnetic abrasive finishing is a super finishing process in which the magnetic field is applied in the finishing area and the material is removed from the workpiece by magnetic abrasive particles in the form of microchips. The performance of this process is decided by its two important quality characteristics, material removal rate and surface roughness. Significant process variables affecting these two characteristics are rotational speed of tool, working gap, weight of abrasive, and feed rate. However, material removal rate and surface roughness being conflicting in nature, a compromise has to be made between these two objective to improve the overall performance of the process. Hence, a multi-objective optimization using an artificial bee colony algorithm coupled with response surface methodology for mathematical modeling is attempted in this work. The set of Pareto-optimal solutions obtained by multi-objective optimization offers a ready reference to process planners to decide appropriate process parameters for a particular scenario

Optimal sequence of hole-making operations using particle swarm optimization and modified shuffled frog leaping algorithm

Tool travel and tool switch scheduling are two major issues in hole-making operations. It is necessary to find the optimal sequence of operations to reduce the total processing cost of hole-making operations. In this work therefore, an attempt is made to use both a recently developed particle swarm optimisation algorithm and a shuffled frog leaping algorithm demonstrating in this way an example of plastic injection mould. The exact value of the minimum total processing cost is obtained by considering all possible combinations of sequences. The results obtained using particle swarm optimisation and shuffled frog leaping algorithm are compared with the minimum total processing cost results obtained by considering all possible combinations of sequences. It is observed that the results obtained using particle swarm optimisation and shuffled frog leaping algorithm are closer to the results of the minimum total processing cost obtained by considering all possible combinations of sequences presented in this work. This clearly shows that particle swarm optimisation and shuffled frog leaping algorithm can be effectively used in optimisation of large scale injection mould hole-making operations

Process parameter optimization based on principal components analysis during machining of hardened steel

The optimum selection of process parameters has played an important role for improving the surface finish, minimizing tool wear, increasing material removal rate and reducing machining time of any machining process. In this paper, optimum parameters while machining AISI D2 hardened steel using solid carbide TiAlN coated end mill has been investigated. For optimization of process parameters along with multiple quality characteristics, principal components analysis method has been adopted in this work. The confirmation experiments have revealed that to improve performance of cutting; principal components analysis method would be a useful tool