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A quality-driven assembly sequence planning and line configuration selection for non-ideal compliant structures assemblies

  • Vahid Jandaghi Shahi
  • Abolfazl MasoumiEmail author
  • Pasquale Franciosa
  • Dariusz Ceglarek
ORIGINAL ARTICLE
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Abstract

In automotive body assembly systems, an optimum assembly sequence planning (ASP) not only increases production efficiency and product quality, but also decreases cost and process cycle time. Typically, ASP evaluation approaches are focused on design for assembly criteria, and very few studies have considered the impact of ASP on dimensional accuracy. The major challenges involving quality-driven ASP evaluation can be enumerated into three categories: (1) batch of compliant non-ideal parts to consider real part defects; (2) variation propagation modeling in multi-station assembly (MSA) system in the presence of stochastic manufacturing errors both at product and process levels; and, (3) the development of dimensional quality criteria for quantitative ASP comparisons. This paper proposes a methodology based on the modeling of dimensional errors propagation in MSA with a batch of compliant non-ideal parts to improve product dimensional quality through optimizing ASP and assembly line configuration. It entails three main steps: (i) assembly sequence generation by k-ary assembly operation method for a predetermined assembly line configuration; (ii) variation propagation simulation taking into account a batch of non-ideal parts, station-to-station repositioning errors, and spring-back phenomenon in MSA system; and, (iii) robust optimization of ASP based on developed quality criteria which contains two quantitative indices. The potential benefits of the proposed methodology are successfully demonstrated on automotive front-rail assembly process.

Keywords

Assembly line configuration Assembly sequence planning Batch of compliant non-ideal parts Dimensional quality Multi-station assembly 

Nomenclature

A

Set of ordered parts

C

ki-piece mixed graph

E

Set of unordered parts

Fi

Transfer function

\(\mathbf {f}_{{k}_{i}}\)

Part deviation generator function

Im

KPC tolerance interval [LSL,USL]

i

Station index

j

Stochastic sample of KCCs

ki

Assembled part(s) at station ith

KH

Kernel smoothing function

KL

Proportionality constant

L

Taguchi’s loss function

m

KPC index

M

Mixed graph

n

ASP index

NASP

Number of ASP options

NKPC

Number of KPCs

NMC

Number of Monte-Carlo iterations

NS

Number of stations

ΔP (i)

Non-ideal part

Qj (i,r)

Displacement field for FE simulations

r

FE simulation index

S (i)

Assembly station input

T

KPC target vector

U (i)

State vector of displacement field

V

Set of total parts

X

Stochastic KPC variation

Xj (i)

State vector for KPC variation

Γ

ASP option

Ψ

Conformity rate

Υ

Target index

Ω

Quality criteria

σ

Standard deviation

μ

Mean value

λ

KPCs variation vector

Δ

Deviation operator

FLP (i)

Fixture location point

JLP (i)

Joining location point

PDF

Probability density function

VRM

Variation response methodology

Notes

Acknowledgments

The authors wish to thank School of Mechanical Engineering at University of Tehran (Iran) and Digital Lifecycle Management group at University of Warwick (UK) for their invaluable cooperation during this research.

Funding information

This study was partially supported by the UK EPSRC project EP/K019368/1: “Self-Resilient Reconfigurable Assembly Systems with In-process Quality Improvement.”

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Vahid Jandaghi Shahi
    • 1
  • Abolfazl Masoumi
    • 1
    Email author
  • Pasquale Franciosa
    • 2
  • Dariusz Ceglarek
    • 2
  1. 1.School of Mechanical Engineering, College of EngineeringUniversity of TehranTehranIran
  2. 2.Digital Lifecycle Management (DLM), WMGUniversity of WarwickCoventryUK

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