A multilayer shallow learning approach to variation prediction and variation source identification in multistage machini
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A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes Filmon Yacob1
· Daniel Semere1
Received: 26 October 2019 / Accepted: 18 August 2020 © The Author(s) 2020
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. Keywords Variation propagation · Skin Model Shapes · Virtual machining
Introduction All manufacturing processes are inherently imprecise, thus producing parts with variation (Srinivasan and Heights 1999). The ability of assessing the effect of those variations by virtual models provides a competitive advantage for manufacturing and product development organizations (e.g. Schleich et al. 2017). 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). The variation propagation analysis is based on the mechanical assembly of virtual parts (Schleich and Wartzack
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Filmon Yacob [email protected] Daniel Semere [email protected]
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Department of Production Engineering, School of Industrial Engineering and Management, Royal Institute of Technology KTH, Brinellvägen 68, 114 28 Stockholm, Sweden
2015), mainly for the purposes of performing tolerance synthesis and analysis (Nabil Anwer et al. 2014; Schleich and Wartzack 2014). In multistage machining, the part variations propagate at each stage in process, resulting a complex relationship between the sources of variation and the machined parts. Such relationship is often studied under the notion of stream of variation. This mainly includes a
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