Integrated energy-efficient machining of rotary impellers and multi-objective optimization

Abstract

Reduction in energy consumption without compromising flexibility and quality during manufacturing processes is a continuous challenge. Due to the increasing demand for high-quality machine parts having complex features and intricate shapes, machine-tool manufacturers are producing advanced designs, such as turn-mill machine tools that are capable of turning, milling, drilling, and surface machining and can obtain high precision and surface quality. Use of such advanced systems increases the energy requirement of machining processes, hindering efforts to reduce energy consumption. Rotary impellers are the most critical components of turbomachine systems and are manufactured using difficult-to-cut materials. In this study, integrated process planning for the manufacturing of a rotary impeller AISI 304 from bar-stock to a finished part using a turn-mill machine tool with a single setup is accomplished, and the energy-intensive operations are identified. To improve the energy efficiency of the machining process without compromising other operational objectives, the most energy-intensive operations are investigated. By employing the design of the experimental method, data are collected for the features of concern, and the results are analyzed via the analysis of variance method. Furthermore, artificial neural network models are developed, and multi-objective optimization for the finish-cut milling operation is performed using the genetic algorithm.