Assembly Sequencing using Sparse Batch Variation Data: A Process-Oriented Tolerancing Approach
Freitag S, Gökce MC, Stockinger A, Götz S, Wartzack S (2026)
Publication Language: English
Publication Type: Journal article
Publication year: 2026
Journal
Original Authors: Stephan Freitag, Mert Can Gökce, Andreas Stockinger, Stefan Goetz, Sandro Wartzack
Book Volume: 145
Pages Range: 230-235
DOI: 10.1016/j.procir.2026.03.142
Open Access Link: https://doi.org/10.1016/j.procir.2026.03.142
Abstract
Variations in individual parts and the assembly process itself have a significant impact on the resulting assembly quality. Selective assembly approaches have been established to address these variations while maintaining high quality by matching parts based on their variations. However, such approaches require extensive variation information for every individual part, which can lead to excessive measurement and data handling effort and therefore higher costs.
In this article, a method is proposed that does not rely on measuring and matching 100% of the parts. Instead, batch-level variation probabilities are derived from sparse measurement data and incorporated into an assembly simulation to improve the resulting quality outcomes. This is achieved by optimizing assembly parameters and selecting the most suitable positioning sequence for each batch. Thus, the best assembly concept can be defined while accounting for batch-specific variations and compensating for effects such as continuous mean shifts. A case study is conducted to investigate whether adjusting the assembly parameters using the proposed method can improve the assembly quality (gap parallelism and gap width). The assembly simulation is based on a fixtureless assembly process without mechanical reference elements, as commonly used in automotive engineering. Four assembly concepts are investigated, using multiple batches with different variation distributions. The method is shown to be effective in selecting both the optimal assembly concept and the corresponding assembly parameters. Furthermore, the results can be used to identify systematically increasing or shifting variations and therefore allows to adjust manufacturing processes before quality deteriorates excessively.
Authors with CRIS profile
Stephan Freitag
Chair of Engineering Design (KTmfk)
Stefan Götz
Chair of Engineering Design (KTmfk)
Sandro Wartzack
Chair of Engineering Design (KTmfk)
Related research project(s)
Involved external institutions
Germany (DE)How to cite
APA:
Freitag, S., Gökce, M.C., Stockinger, A., Götz, S., & Wartzack, S. (2026). Assembly Sequencing using Sparse Batch Variation Data: A Process-Oriented Tolerancing Approach. Procedia CIRP, 145, 230-235. https://doi.org/10.1016/j.procir.2026.03.142
MLA:
Freitag, Stephan, et al. "Assembly Sequencing using Sparse Batch Variation Data: A Process-Oriented Tolerancing Approach." Procedia CIRP 145 (2026): 230-235.
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