On the Characterization and Optimization of Machining Chip Washing System Using Multi-Variable Response Surface and Gradient Descent Method Abstract

The final step in manufacturing high-precision parts for internal combustion engines, such as cylinder heads and blocks, is the removal of machining chips from the finished parts. This step is crucial because the machining chips and cutting oil left on the surface after machining can cause quality issues in the downstream engine assembly and affect the cooling system’s performance during engine operation. This chip removal step is especially critical for parts with internal cavities, such as the water jackets in cylinder heads, due to the difficulty of removing chips lodged in the narrow passages of these internal channels. To effectively remove chips from the water jacket, machining chip washing systems typically utilize multiple high-velocity water jets directed into the water jacket, creating flows with substantial kinetic energy to dislodge and evacuate the machining chips. For machining chip washing systems equipped with dozens of water nozzles, optimizing washing efficiency presents a significant challenge due to the large number of variables involved. Additionally, the optimization objectives and procedures can vary depending on the specific constraints of the process and the chosen criteria of the performance. For a complex system with many input factors and strong interactions among the factors, a trial-based experimental approach is no longer a viable option for designing effective machining chip washing systems. The goal of this paper is to develop a model-based engineering methodology utilizing computer simulations, with two objectives. The first objective is to create simulation models that employ a systematic procedure for determining the characteristics of the machining chip washing systems. These characteristics are defined by two performance criteria: local performance, which focuses on individual nozzle effectiveness, and global performance, which assesses overall chip washing efficiency. The second objective is to apply optimization algorithms to enhance system performance based on these characteristics. While local nozzle effectiveness will be optimized using the response surface method, the global washing efficiency will be optimized using the gradient descent method. By utilizing this approach, the complex high-dimensional optimization problem can be broken down into smaller, more manageable sub-problems, which can then be solved using conventional optimization algorithms.