Abstract
Variation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source identification techniques using Skin Model Shapes is unclear. This paper proposes a multilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.
Highlights
All manufacturing processes are inherently imprecise, producing parts with variation (Srinivasan and Heights 1999)
For the purposes of product development, Skin Model Shapes, models derived from discretized nominal models or point cloud data, provide a better accuracy in variation propagation related analysis compared to the classical methods, such as vector loops and small displacement torsor (Schleich et al 2016; Schleich and Wartzack 2016)
Skin Model Shape has been applied in variation source identification of machined parts. This approach extends the analytical methods applied in variation propagation modelling by introducing how shallow neural network (SNN) can be used in prediction of geometric variation and variation source identification
Summary
All manufacturing processes are inherently imprecise, producing parts with variation (Srinivasan and Heights 1999). The techniques of variation propagation analysis used in product development and manufacturing processes have been developing independently without adapting the advances from each other (Abellán-nebot et al 2013) Towards addressing this need, Wang et al (2019) proposed a model that combines stream of variation for inter-station variation and small displacement torsor for variation in the assembly of workpiece and machine tool components. Skin Model Shape (here after SMS) has been applied in variation source identification of machined parts. This approach extends the analytical methods applied in variation propagation modelling by introducing how SNN can be used in prediction of geometric variation and variation source identification. After presenting a demonstration case in “Demonstration case” section, a discussion and conclusion are presented in the subsequent sections
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