Multistage Manufacturing Processes: Innovations in Statistical Modeling and Inference

2018 ◽  
pp. 67-81
Author(s):  
Hsiang-Ling Hsu ◽  
Ching-Kang Ing ◽  
Tze Leung Lai ◽  
Shu-Hui Yu
Keyword(s):  
Statistical Modeling ◽  
Manufacturing Processes ◽  
Multistage Manufacturing

Process-Oriented Tolerance Synthesis for Multistage Manufacturing Systems

2000 ◽  
Author(s):  
Yu Ding ◽  
Jionghua Jin ◽  
Dariusz Ceglarek ◽  
Jianjun Shi
Keyword(s):  
Product Design ◽  
Process Design ◽  
Manufacturing Systems ◽  
Minimum Cost ◽  
Manufacturing Processes ◽  
Industrial Case Study ◽  
Multistage Manufacturing ◽  
Process Oriented ◽  
Design Characteristics

Abstract In multistage manufacturing systems, quality of final products is strongly affected not only by product design characteristics but also by key process design characteristics. However, historically, tolerance research has primarily focused on allocating tolerances based on product design characteristics for each component. Currently, there is no analytical approach for multistage manufacturing processes to optimally allocate tolerances to integrate product and process characteristics at minimum cost. One of the major obstacles is that the relationship between tolerances of process and product characteristics is not well understood and modeled. Under this motivation, this paper aims at presenting a framework addressing the process-oriented (rather than product-oriented) tolerancing technique for multistage manufacturing processes. Based on a developed state space model, tolerances of process design characteristics at each fabrication stage are related to the quality of final product. All key elements in the framework are described and then derived for a multistage assembly process. An industrial case study is used to illustrate the proposed approach.

Diagnosing Multistage Manufacturing Processes With Engineering-Driven Factor Analysis Considering Sampling Uncertainty

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Jian Liu ◽  
Jionghua Jin
Keyword(s):  
Factor Analysis ◽  
Spatial Variation ◽  
Identification Problem ◽  
Factor Loading ◽  
Manufacturing Processes ◽  
Multistage Manufacturing ◽  
Factor Rotation ◽  
Variation Patterns ◽  
True Values

A new engineering-driven factor analysis (EDFA) method has been developed to assist the variation source identification for multistage manufacturing processes (MMPs). The proposed method investigated how to fully utilize qualitative engineering knowledge of the spatial variation patterns to guide the factor rotation. It is shown that ideal identification can be achieved by matching the rotated factor loading vectors with the qualitative indicator vectors (IV) that are defined according to spatial variation patterns based on the design constraints. However, the random sampling variability may significantly affect the estimation of the rotated factor loading vectors, leading to the deviations from their true values. These deviations may change the matching results and cause misidentification of the actual variation sources. By using implicit differentiation approach, this paper derives the asymptotic distribution and the associated variance-covariance matrix of the rotated factor loading vectors. Therefore, by considering the effect of sample estimation variability, the variation sources identification problem is reformulated as an asymptotic statistical test of the hypothesized match between the rotated factor loading vectors and the indicator vectors. A real-world case study is provided to demonstrate the effectiveness of the proposed matching method and its robustness to the sample uncertainty.

scholarly journals Variation propagation modelling in multistage machining processes using dual quaternions

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2987-2998
Author(s):  
Filmon Yacob ◽  
Daniel Semere
Keyword(s):  
Motion Vector ◽  
Manufacturing Processes ◽  
Machining Processes ◽  
Dual Quaternions ◽  
Multistage Manufacturing ◽  
Differential Motion ◽  
Variation Propagation ◽  
Stream Of Variation ◽  
Matrix Differential ◽  
Propagation Modelling

Abstract Variation propagation models play an important role in part quality prediction, variation source identification, and variation compensation in multistage manufacturing processes. These models often use homogenous transformation matrix, differential motion vector, and/or Jacobian matrix to represent and transform the part, tool and fixture coordinate systems and associated variations. However, the models end up with large matrices as the number features and functional element pairs increase. This work proposes a novel strategy for modelling of variation propagation in multistage machining processes using dual quaternions. The strategy includes representation of the fixture, part, and toolpath by dual quaternions, followed by projection locator points onto the features, which leads to a simplified model of a part-fixture assembly and machining. The proposed approach was validated against stream of variation models and experimental results reported in the literature. This paper aims to provide a new direction of research on variation propagation modelling of multistage manufacturing processes.

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