6+X locating principle based on dynamic mass centers of structural parts machined by responsive fixtures. (February 2018) - Hao, Xiaozhong; Li, Yingguang; Chen, Gengxiang; Liu, Changqing
Abstract: Adaptive machining with responsive fixtures is an effective way to release and eliminate deformation during the machining process of large-scale structural parts. While a proper locating principle is a key challenge for the responsive fixture based machining due to its two contradictory requirements of maintaining the machining datum and deformation releasing sufficiently. For traditional machining methods, locators are placed as far as possible from each other to maintain workpiece stability, which would significantly limit the release of workpiece deformation. In order to address the issue mentioned above, different from traditional locating method, a novel 6+X locating principle is proposed in this paper, where the workpiece is divided into a fixed region and a floating region to facilitate the new locating principle. The fixed region is optimized by considering dynamic mass centers with a Genetic Algorithm to maintain workpiece stability, and the 6° of freedom of the workpiece are restrained by the fixed region to keep machining datum. The floating region is supported by X floating units so as to keep the workpiece stable during machining process, while the floating units can adjust the workpiece posture in accordance with the machining deformation during the machining intervals. Algorithm experiments are applied to verify the convergence of the proposed optimization model. Finally, a case study with real machining experiment is carried out to verify the proposed method. Highlights: A novel 6+X locating principle is proposed for responsive fixture based machining. Fixed region and floating region are defined for location and deformation releasing. The fixed region of workpiece is optimized by using a GA. Machining datum and sufficient deformation releasing are satisfied simultaneously. Machining deformation control is significantly improved by experiment verification.