Multi-operation blank localization with hybrid point cloud and feature-based representation

Abstract

Sustainability objectives, including the endeavor to reduce waste, energy consumption and machining effort gave rise to the near net shape (NNS) machining concept, which requires the initial rough blank to be as close to the final machined product as possible. Nevertheless, the opportunity of savings in material, energy and effort come with a risk of manufacturing scrap even in case of a very small geometrical error of the blank. This issue is addressed by blank localization, i.e., the act of placing the final machined product in the geometry of the rough blank. Multi-operation blank localization was proposed recently to exploit tolerances in the product design to compensate potential geometrical errors of the blank. It places each feature group, machined together in the same operation, separately in the blank. When tolerances connecting different feature groups allow, these feature groups can be moved slightly according to the measured actual blank geometry. This paper proposes a novel multi-operation blank localization approach that models the rough blank as a free-form geometry, capturing all possible geometrical errors, whereas represents the final product using a feature-based model. The problem of blank localization for minimizing tolerance errors while leaving sufficient allowance is formulated and solved as a convex quadratically constrained quadratic program (QCQP). In a case study from the automotive industry, it is shown that the proposed multi-operation approach outperforms earlier methods that handle the product as a single solid geometry.