Abstract

Variation propagation modeling of multistage machining processes enables variation reduction by making an accurate prediction on the quality of a part. Part quality prediction through variation propagation models, such as stream of variation and Jacobian-Torsor models, often focus on a 3-2-1 fixture layout and do not consider form errors. This paper derives a mathematical model based on dual quaternion for part quality prediction given parts with form errors and fixtures with N-2-1 (N>3) layout. The method uses techniques of Skin Model Shapes and dual quaternions for a virtual assembling of a part on a fixture, as well as conducting machining and measurement. To validate the method, a part with form errors produced in a two-stationed machining process with a 12-2-1 fixture layout was considered. The prediction made following the proposed method was within 0.4% of the prediction made using a CAD/CAM simulation when form errors were not considered. These results validate the method when form errors are neglected and partially validated when considered.

Highlights

  • Variation propagation modeling in multistage machining processes (MMP) considering punctual locators has been studied for more than two decades

  • The importance lies in the ability of the models to help in reducing variation by enabling a better understanding of the multistage machining processes, thereby making informed decisions in process planning, fault diagnosis, variation compensation, process-oriented tolerancing, and costquality optimization [1]

  • Four main operations play a key role in the manipulation of Skin Model Shapes as defined in ISO 17450-1: (1) partitioning, (2) extraction, (3) filtration, and (4) association [40]

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Summary

Introduction

Variation propagation modeling in multistage machining processes (MMP) considering punctual locators has been studied for more than two decades. The model treats the partfixture interaction as a mechanism and often focuses on tolerance analysis Both approaches consider three major sources of variation: machining-induced variation that is caused by cutting tool deviation from its nominal toolpath, datuminduced variation due to deviations induced in upstream, and fixture-induced variation caused by deviation of the locators. The matrices and the expression of these models tend to become tedious, especially when N-2-1 (N>3) fixture layout, locating surfaces, or generic cases are considered [8, 10, 13, 19,20,21] These expressions require three Euler angles to obtain the 16 parameters of homogenous transformation matrices (HTM).

Skin Model Shapes
Projection of points
Dual quaternions
Representation of parts with form errors
Setting up a part on an N-2-1 fixture layout
Assembling to primary locators
Computing difference surface
Selecting a stable contact
Transforming a part by dual quaternions
Assembling to the secondary locators
Assembling to the tertiary locators
Virtual machining using SMS
Multistage machining processes
Case study
Findings
Discussion
10 Conclusion

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