Fluctuation Analysis of Process Flow Based on Error Propagation Network

Liu, Daoyu

doi:10.3901/jme.2010.02.014

Cited by 11 publications

(9 citation statements)

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“…A weighted network is introduced, and the variation propagation stability of MMPs is analyzed by the virus spreading model based on complex networks [38][39]. Similarly, complex networks are introduced to describe these interactions and the error propagation in MMPs [40][41][42].…”

Section: Production Processmentioning

confidence: 99%

“…Finding: In the 12 papers on this topic (8% of the selected papers) [38][39][40][41][42][43][44][45][46][47][48][49], the network models of multistage machining processes are proposed by combining the real network characteristics with complex networks theory. The variation propagation and quality control for multistage machining processes based on a number of measurement indicators are analyzed.…”

Section: Production Processmentioning

confidence: 99%

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Complex networks in advanced manufacturing systems

Tao

Cheng

et al. 2017

Journal of Manufacturing Systems

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Section: Production Processmentioning

confidence: 99%

Section: Production Processmentioning

confidence: 99%

Complex networks in advanced manufacturing systems

Tao

Cheng

et al. 2017

Journal of Manufacturing Systems

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“…And the defects may be propagated, accumulated and amplified with WIP (work-in-process) to downstream process. So the assembly quality of products may have a large fluctuation [3,4]. The defect of the assembly process will seriously affect the quality and cost of the final product.…”

Section: Introductionmentioning

confidence: 99%

Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty

Xiao-qiao

Liu

et al. 2015

Computers in Industry

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“…In the assembly sequence evaluation [7], they are used to evaluate the influence of cumulative deviation. Besides, in the fluctuation evaluation and identification [8], they are used to identify the fluctuation source of the process of the workpiece.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Methodology to Improve Tolerance Allocation Using Product Usage Data

Gao

Zheng

et al. 2021

Journal of Mechanical Design

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Industry 4.0 puts forward new requirements for the sustainable service of products. With the recent advances in measurement technologies, global and local deformations in inaccessible areas can be monitored. Product usage data such as geometric deviation, position deviation and angular deviation that lead to product functional performance degradation can be continuously collected during the product usage stage. These technologies provide opportunities to improve tolerance design by optimizing tolerance allocation using product usage data. The challenge lies in how to assess these deviations for identifying relevant field factors and reallocate the tolerance value. In this paper, a data-driven methodology based on the deviation for tolerance analysis is proposed to optimize the tolerance allocation. A feature graph of a mechanical assembly is established based on the assembly relationship. The node representation in the feature graph is defined based on the unified Jacobian-torsor model and the node label is calculated by a synthetic evaluation method. A novel algorithm based on the graph neural networks (GNNs) is proposed to investigate hidden relations between nodes in the feature graph and calculate labels of all nodes. A modification necessity index (MNI) is defined for each tolerance between two nodes based on their labels. A deviation difference matrix is constructed to calculate the MNI of each tolerance for identifying the to-be-modified tolerance value with high priorities for product improvement. The effectiveness of the proposed methodology is demonstrated through a case study for the optimal tolerance allocation of a press machine.

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Fluctuation Analysis of Process Flow Based on Error Propagation Network

Cited by 11 publications

References 0 publications

Complex networks in advanced manufacturing systems

Complex networks in advanced manufacturing systems

Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty

A Data-Driven Methodology to Improve Tolerance Allocation Using Product Usage Data

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