Parametric optimization of advanced fine-finishing processes

Quality and performance of the products requiring higher precision and involving moving-parts mainly depends on the surface finish and dimensional accuracy. Generally, finishing operations use fine abrasive particles in different forms along with some carrier or binding medium. Finishing operations are crucial, expensive, uncontrollable, and a labor-intensive phase in the overall production, and contribute significantly to the total production time and cost. As surface finish requirement increases, the cost of finishing operations increases exponentially. Though progress has been made in automating the finishing operations to reduce the production time to some extent, it increases the initial investment and operating costs significantly. Quality, cost, time, and efficiency of finishing operations can be improved significantly by choosing the optimum values of the process parameters. This paper presents the details of process parameters optimization of two advanced fine-finishing processes namely abrasive flow machining (AFM) and magnetic abrasive finishing (MAF), which are capable of giving nano-level surface finish, using real-coded genetic algorithms (GA). It also describes the development of a surface roughness model that was developed to form the objective function for the optimization of AFM process.