Compensating Misalignment Using Dynamic Random-Effect Control System: A Case of High-Mixed Wafer Fabrication

Khakifirooz, Marzieh; Chien, Chen–Fu; Fathi, Mahdi

doi:10.1109/tase.2019.2894668

Cited by 16 publications

(4 citation statements)

References 33 publications

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“…The efficiency of the machining delay, denoted by f d (X ij3 ) is affected by the machining shape ij3 , the machining size S ij3 , and the material plasticity δ ij3 . The calculation of the efficiency associated with the machining delay is given by (6).…”

Section: E Model Quantificationmentioning

confidence: 99%

“…The machining efficiency f d (x 123 ) is evaluated according to formula (6). The plastic deformation of the material is depended on its own fluidity, and fetches δ = 100%.…”

Section: B Weight Calculationmentioning

confidence: 99%

“…(3, 1), (1,2), (2,4); (5,6), (6,15), (8,7), (9,8); (10,11), (10,13), (11,12), (12,16), (13,14), (14,15), (16,7).…”

Section: Optimization Solutionmentioning

confidence: 99%

“…According to studies in [3]- [5], most of the manufacturing processes of modern equipment belong to the discrete manufacturing (any two machining actions can be combined probably arbitrarily and processed in an adjacent order). Thus, for a task involving many machining actions, a large number of manufacturing schemes need to be arranged [6]- [8]. Different schemes incur different costs.…”

Section: Introductionmentioning

confidence: 99%

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A Graph Theory-Based Optimization Design for Complex Manufacturing Processes

et al. 2020

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The manufacturing process of modern equipment becomes very complex due to features such as mass units, multiple machining, and complicated coupling-relationships, posing a big challenge for determining the manufacturing scheme. This paper addresses the challenge by proposing a graph theory-based optimization design for the complex manufacturing process. A detailed analysis of a serial of graph models built according to the manufacturing process features reveals that the Hamilton graph is suitable for modeling the manufacturing process system. Some model weight assignment functions are extracted for the quantitative study. Further the optimal scheme for an optimization design of the complex manufacturing process is solved using the full link graph feature algorithm -a search optimization algorithm. A manufacturing model matrix is constructed, and penalty number and divisor are formulated to simplify the matrix and improve the algorithm efficiency in the process of algorithm design. An example is provided to demonstrate feasibility and effectiveness of the proposed method.INDEX TERMS Graph model, graph theory, manufacturing process, model weight, optimization design.ZHONG HAN received the B.S. degree in computer application technology and the M.S. degree in computer science and technology from the University of Electronical Science and Technology, Chengdu, China, respectively, and the Ph.D. degree in mechanical engineering and automation from Xi'an Jiaotong University, Xi'an, China.He was a Postdoctoral Researcher of intelligent manufacturing technology with the Xi'an Jiaotong University of China. He is currently an Associate

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Section: E Model Quantificationmentioning

confidence: 99%

“…The machining efficiency f d (x 123 ) is evaluated according to formula (6). The plastic deformation of the material is depended on its own fluidity, and fetches δ = 100%.…”

Section: B Weight Calculationmentioning

confidence: 99%