Meta-Model Based on Artificial Neural Networks for Tooth Root Stress Analysis of Micro-Gears

Haefner, Benjamin; Biehler, Michael; Wagner, Raphael; Lanza, Gisela

doi:10.1016/j.procir.2018.04.031

Cited by 11 publications

(3 citation statements)

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“…Non-geometrical SMs, where a certain geometrical propagation method is already presumed, include tolerance-cost relationships in assemblies [59], optimizations of joining processes such as optimized spot welding sequences [60] and functional relationships such as the use of neural networks for the prediction of tooth root stress of not form-ideal gear wheel assemblies [61].…”

Section: Prior Work For Surrogate Modelling For Joining Processesmentioning

confidence: 99%

On the Development of a Surrogate Modelling Toolbox for Virtual Assembly

2021

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Virtual assembly (VA) is a method to simulate the physical assembly (PA) of scanned parts. Small local part deviations can accumulate to large assembly deviations limiting the product quality. The propagation of geometrical deviations onto the assembly is a crucial step in tolerance management to assess the assembly quality. Current approaches for VA do not sufficiently consider the physical joining process. Therefore, the propagated assembly geometry may deviate strongly from the PA. In the state of the art, only specific and complex methods for particular joining processes are known. In this paper, the concept of Surrogate Models (SMs) is introduced, representing the connection between part and assembly geometries for particular joining processes. A Surrogate Modelling Toolbox (SMT) is developed that is intended to cover the variety of joining processes by the implementation of suitable SMs. A particular SM is created by the composition of suitable Surrogate Operations (SOs). An open list of SOs is presented. The composition of a SM is studied for a laser welding process of two polymer components. The resulting VA is compared to the PA in order to validate the developed model and is quantified by the exploitation ratio R.

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Section: Prior Work For Surrogate Modelling For Joining Processesmentioning

confidence: 99%

On the Development of a Surrogate Modelling Toolbox for Virtual Assembly

2021

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“…The latter is based on neural networking, rule learning, and fuzzy logic [16,18,32,33]. For example, Haefner et al [34] applied machine learning approach based on artificial…”

Section: Introductionmentioning

confidence: 99%

“…The latter is based on neural networking, rule learning, and fuzzy logic [ 16 , 18 , 32 , 33 ]. For example, Haefner et al [ 34 ] applied machine learning approach based on artificial neural network for developing metamodel to be used for tooth root stress analysis of micro-gear. Morin et al [ 35 ] used machine learning to generate metamodels for sawing simulation in wood industry.…”

Section: Introductionmentioning

confidence: 99%

Simulation metamodeling approach to complex design of garment assembly lines

et al. 2020

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The today's competitive advantage of ready-made garment industry depends on the ability to improve the efficiency and effectiveness of resource utilization. Ready-made garment industry has long historically adopted fewer technological and process advancement as compared to automotive, electronics and semiconductor industries. Simulation modeling of garment assembly line has attracted a number of researchers as one way for insightful analysis of the system behaviour and improving its performance. However, most of simulation studies have considered ill-defined experimental design which cannot fully explore the assembly line design alternatives and does not uncover the interaction effects of the input variables. Simulation metamodeling is an approach to assembly line design which has recently been of interest to researchers. However, its application in garment assembly line design has never been well explored. In this paper, simulation metamodeling of trouser assembly line with 72 operations was demonstrated. The linear regression metamodel technique with resolution-V design was used. The effects of five factors: bundle size, job release policy, task assignment pattern, machine number and helper number on throughput of the trouser assembly line were studied. An increase of the production throughput by 28.63% was achieved for the best factors' setting of the metamodel.

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