Mechanical Machining Process Optimization

Manufacturing process optimization, a key of intelligent manufacturing, seeks to identify process parameters that maximum the profitability of manufacturing processes. This chapter focuses on the analysis, modeling and solution of machining process optimization. First, typical objective functions and constraints encountered in machining operations are discussed. Next, optimization of static machining operations (i.e., machining operations where the process parameters are fixed) is discussed. In many cases the optimal solution can be obtained analytically. For complicated processes, artificial intelligence techniques are typically employed. The genetic algorithm technique is introduced and applied to a case study involving parallel drilling. Then, optimization of dynamic machining operations (i.e., machining operations where the process parameters vary during the process) is discussed. In this case, changes in the machining operation are detected in real time through sensors and adjustments are made during the operation. Adaptive Control with Constraints, where process parameters are adjusted to maintain a critical constraint, and Adaptive Control Optimization, where process parameters are adjusted to maximize (or minimize) and objective function, are discussed and examples are given Dynamic optimization was then illustrated by implementing machining process control in the wire sawing machining process for SiC single crystal.