A control method with considering error coupling in milling multi-feature thin-walled parts

Zuo, Xiaoyan; Yin, Zhihua; Xiong, Tao; Zhang, Chi; Chen, Feng; Zhou, Xiangyang

doi:10.1007/s00170-022-09959-7

Cited by 1 publication

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References 26 publications

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“…To address this issue, Wang et al [30] established a variation management framework for KCCs in MMPs. The error control and error compensation methods have been studied in order to improve the machining quality [31,32]. The models developed using DMVs are limited to an orthogonal 3-2-1 fixture layout case.…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Variation Propagation for Complex-Shaped Workpieces in Multi-Stage Machining Processes

Yang

Zhang²,

Zhang

et al. 2023

Machines

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Variation prediction and quality control for complex-shaped workpieces in automotive and aerospace fields with multi-stage machining processes have drawn significant attention because of the widespread application and increasing diversity of these kinds of workpieces. To finish the final workpieces with complex shapes, multiple setups and operations are often applied in machining processes. However, sources of geometric error, such as fixture error, datum error, machine tool path error, and the dimensional quality of the product, interact complicatedly at different stages. These complex interactions pose significant challenges to final product error prediction and reduction. Manufacturing error prediction based on stream of variation is an effective way to control the machining quality. However, there are few integrated models that can describe the interactions among types of geometric error sources from different stages for different kinds of complex workpieces. This paper proposes a modified error prediction model to systematically capture the interactions of different error sources among different operations for complex-shaped workpieces in multi-stage machining processes. Using differential motion vectors, the connection of all key variations from machine, fixture, and workpiece is established. This modified model can not only handle general fixture layouts for complex workpieces, but also introduce machining-induced variations. Based on this model, the main error sources identification method and error compensation method are proposed. In order to evaluate the effectiveness of the proposed method, engine blocks are used to be machined as an example. Compared with a machining process without a compensating strategy, the average machining error of the key feature is reduced by 80.5% after compensating for the main error sources.

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Section: Introductionmentioning

confidence: 99%