The volume variation of multiple chambers of a workpiece is one of the most important factors that can directly influence the performance of the final product. This paper presents a novel systematic approach for online minimizing the volume difference of multiple chambers of a workpiece based on high-definition metrology (HDM). First, the datum of high-density points is transformed by a random sample consensus (RANSAC) algorithm due to its good robustness in fitting. Second, a procedure containing reconstruction of interior curved surfaces of chambers, boundary extraction, and projection is developed to calculate the accurate volumes of the multiple chambers. Third, a model for obtaining an optimized machining parameter for depth of chambers is explored to minimize the volume difference of any two ones of all the chambers. The model is formulated as a multi-objective optimization (MOO) problem, and a new procedure of multi-objective particle swarm optimization (MOPSO) algorithm is developed to solve this problem. Finally, a milling depth is output as the optimal milling parameter for controlling the volume variation of multiple chambers. The results of a case study show that the proposed approach can minimize the volume difference of four combustion chambers of a cylinder head and it can be well applied online in volume variation control of multiple chambers in machining processes.

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