Quality-driven Optimization of Assembly Line Configuration for Multi-Station Assembly Systems with Compliant Non-ideal Sheet Metal Parts

Shahi, Vahid Jandaghi; Masoumi, Abolfazl; Franciosa, Pasquale; Ceglarek, Darek

doi:10.1016/j.procir.2018.02.022

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…Ideal part Liu and Hu, 15 Chang and Gossard, 17 Merkley, 18 Camelio et al, 19 Liao and Wang, 20 Xie et al, 21 Dahlströ m and Lindkvist, 22 Ungemach and Mantwill 23 Camelio et al, 16 Gerbino et al 24 Non-Ideal part Franciosa and colleagues [26][27][28] Jandaghi Shahi et al 29,30…”

Section: Compliantmentioning

confidence: 99%

“…The mean and standard deviation of all KPCs are shown in Figure 10. These analytic values are compared with the pure rigid 12 and compliant variation models 29 corresponding to the similar initial deviations. Overall, the result of the comparison shows that mean deviation and standard deviation of the KPCs obtained from the proposed model are higher than the pure rigid and compliant variation models.…”

Section: Industrial Case Studymentioning

confidence: 99%

“…25 An efficient method capable to simulate the PCFR cycle considering non-ideal parts called variation response methodology (VRM) was proposed by Franciosa and colleagues, [26][27][28] but it was only related to single-station processes. In order to support assembly sequence planning, this method has since been extended to MSA systems by Jandaghi Shahi et al 29,30 The state space representation is used to model stationto-station repositioning errors throughout assembly line.…”

Section: Introductionmentioning

confidence: 99%

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Integration of in-plane and out-of-plane dimensional variation in multi-station assembly process for automotive body assembly

Shahi

Masoumi

2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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Automotive body assembly systems contain multiple operations in multi-station processes. One of the most critical challenges for such manufacturing systems is dimensional quality, which is affected by the accumulation and propagation of variation caused by manufacturing imperfections. However, sheet metal part compliancy behavior makes the variation modeling method extremely intricate when both rigid (in-plane) and compliant (out-of-plane) variations are considered. This paper develops a more accurate variation propagation model to describe dimensional variation of sheet metal assembly in multi-station assembly system through involving both the variation types simultaneously as well as the impacts of assembly operations on each other. In this methodology, three sources of deviations—non-ideal parts, fixture errors, and assembly operations effects—are taken into account. The variation generated in every assembly operation (placing, clamping, fastening, and releasing steps) and the variation propagation through station-to-station interaction (repositioning) are analyzed by the transfer function mechanism. In the in-plane direction, the stream of variation analysis is adopted to obtain the rigid transfer function to describe the position and orientation relationships between part and assembly element errors. For the simulation of part deformation during the assembly process in the out-of-plane direction, the compliant transfer functions are extracted by variation response methodology. A nested state space model is used to integrate the overall assembly variation by updating part geometry after each assembly operation. The capability of proposed method is illustrated through a case study on an automotive body side assembly process.

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Section: Compliantmentioning

confidence: 99%

Section: Industrial Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integration of in-plane and out-of-plane dimensional variation in multi-station assembly process for automotive body assembly

Shahi

Masoumi

2019

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

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“…In recent years, distinct methods, such as, Monte Carlo Simulation [7][8][9], design of experiments [3,8] and metamodels [1,10] have been used to model the influence of uncertainty. Sensitivity analyses are used to quantify the influence of each uncertainty source in the variability of the forming results [3,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of the of the square cup stamping process using a polynomial chaos expansion

Pereira

2023

Materials Research Proceedings

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Abstract. The stochastic modelling and quantification of the various sources of uncertainty associated with sheet metal forming processes, usually requires a large computational cost to obtain accurate results. In this work, a polynomial chaos expansion metamodel is used in order to reduce the computational cost of the uncertainty quantification (through Sobol’s indices). The metamodel allows to establish mathematical relationships between the square cup forming results and the uncertainty sources associated with the material behaviour and process conditions. Then, sensitivity indices are estimated with the trained metamodel, without resorting to additional numerical simulations. The indices obtained with the metamodel were compared to those obtained with the traditional approach based on a quasi-Monte Carlo method. The metamodel allowed to reduce the computational cost in about 90% when compared to the traditional approach, without compromising the accuracy of the results.

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“…Design friendless in this paper is defined as the ability to model and simulate non-ideal parts with a small number of model parameters. This is vital because optimising a large assembly involving many parts by considering the non-ideal behaviour requires a large number of computationally expensive Finite Element Method (FEM) simulations [3,26,27]. The number of FEM simulations required increases exponentially with the number of model parameters whose effects on product quality has to be determined.…”

Section: Introductionmentioning

confidence: 99%

Object shape error modelling and simulation during early design stage by morphing Gaussian Random Fields

Babu,

Franciosa,

Shekar

et al. 2020

Preprint

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3D free-form surfaces with high aesthetical and functional requirements are widely used in automotive and aerospace industry. Geometric and dimensional variations of these free-form surfaces caused by inevitable uncertainties in the manufacturing process often leads to product quality issues. Failing to model the effect of non-ideal parts, i.e., parts with geometric and dimensional errors, during design inhibits the ability to predict such quality issues. Consequently leading to expensive design changes after freezing of design. Therefore, it is vital to model and simulate non-ideal parts that are as similar as possible to true manufactured parts during early design stage and prevent unnecessary design changes during later stages. A major challenge for accurate modelling of non-ideal parts during early design phase is the limited availability of data and the ability to effectively utilise the historical data from similar parts.Overcoming this challenge a novel morphing Gaussian Random Field (mGRF) methodology for shape error modelling of 3D free-form surfaces during the early design stage is presented in this paper. The mGRF methodology works under the constraint of limited data availability and can utilise historical measurement data of similar parts to generate non-ideal parts that exhibit spatial deviation patterns as similar as possible to the true manufactured part and conform to specified form tolerance requirements for the profile of a surface. Additionally, the mGRF methodology provides designers with an intuitive way to explore several 'What if?' scenarios relating to part geometric variations during early design phase. This is achieved by first, modelling the spatial correlation in the deviations of the part from its design nominal using Gaussian processes and then, utilising the modelled spatial correlations to generate non-ideal parts by conditional simulations. The aforementioned capabilities enable to accurately model and predict the effect of part shape variations on product quality during the early design phase. The developed mGRF methodology is demonstrated, compared with state-of-art methodologies, and validated using a sport-utility-vehicle door part.

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Quality-driven Optimization of Assembly Line Configuration for Multi-Station Assembly Systems with Compliant Non-ideal Sheet Metal Parts

Cited by 8 publications

References 25 publications

Integration of in-plane and out-of-plane dimensional variation in multi-station assembly process for automotive body assembly

Integration of in-plane and out-of-plane dimensional variation in multi-station assembly process for automotive body assembly

Sensitivity analysis of the of the square cup stamping process using a polynomial chaos expansion

Object shape error modelling and simulation during early design stage by morphing Gaussian Random Fields

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