Feature model and case retrieval for body-in-white part

Chao, Yongsheng; Liu, Haijiang

doi:10.1007/s00170-010-2910-1

Cited by 5 publications

(2 citation statements)

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“…(b) Experience-based design [7,18]: The designer acts freely based on their experience without explicitly consulting previous designs or knowledge. For example, Andersson et al [125] observed welding in production and found that the same repeated weld designs would often fail.…”

Section: State Of the Artmentioning

confidence: 99%

“…These areas require extensive amounts of expert knowledge and research to find feasible solutions [97]. Moreover, these areas have complex environmental conditions with ever-changing specifications, such as shortening product development cycles and increasing market competition [7]. Furthermore, most applications require enormous datasets, expertise, and resources for process optimization, monitoring, and control of applications at an unparalleled level of accuracy [98].…”

Section: Introductionmentioning

confidence: 99%

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Automated joining element design for high product variety in the manufacturing industry

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Product variety and the manufacturing complexity that it induces are continuously increasing. This poses a challenge in the product development process and, consequently, the design of joints. Joining elements define the manner in which a permanent joint is created between parts. Joining element design is an ambiguous manual task with limited automation solutions. Thus, it can lead to long, iterative, error-prone development trajectories that may result in costly rework. Hence, automation solutions for joining element design must be intelligent. However, simply ensuring the intelligent automation of joining element design is insufficient. Modular design, through the approaches of modularization and commonalization, enables manufacturers to cope with the complexity induced by product variety. Unfortunately, modular design approaches have not yet considered joining elements. Hence, this dissertation study sought to answer the following research question: "How can joining element design be automated for high-variety products?"This dissertation presents a framework for automating joining element design. The framework is structured into smaller design problems, which will guide designers through the process of designing joining elements. This structuring will also enable designers to evaluate and assess artificial intelligence (AI) for each design problem. Moreover, the design problems will enable designers to identify unused AI techniques, such as machine learning.These techniques were conceptualized and several were implemented for validation in this study. A market-validated database with automotive Body-in-White structures was used. The validation included the use of decision trees to predict joining technologies and the number of joining elements. Both prediction tasks seem promising to implement in early design phases due to their simplicity.Next, this study validated two approaches for predicting joining locations. The first was a straight-forward evolutionary algorithm for distributing spot welds over contact regions. However, this algorithm's performance fell off quickly for nontrivial design problems with high solution spaces.The second approach was to use convolutional neural networks to predict spot weld locations, which provided robust and promising results. This study first explored a voxel-based regression and classification task by drawing locations in a grid-like structure. Classification with a segmentation approach produced more robust results due to the spatial dependencies of classes. Supervised machine learning enabled the consideration of knowledge of successful designs in new design problems. This concept was subsequently enriched with nongeometric data using a branding approach. However, these multimodal machine learning models did not improve the joining locations. Furthermore, the models could not extract the i ii

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Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%