An adaptive optimization procedure for spot-welded structures

Bhatti, Q. I.; Ouisse, Morvan; Cogan, Scott

doi:10.1016/j.compstruc.2011.04.009

Cited by 18 publications

(11 citation statements)

References 12 publications

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“…For this reason, the global maximum value is not guaranteed to be reachable. Bhatti et al (2011) found an adaptive procedure which optimizes the distribution, quantity, and performance of spot welds. They also managed to study the influence of missing and/or defective RSW on the performance.…”

Section: Spot Welding Quantity and Performance Optimizationmentioning

confidence: 99%

Managing and improving robot spot welding efficiency: a benchmarking study

El-Khalil

2014

Benchmarking: An International Journal

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Purpose -The paper presents a benchmarking analysis that investigates the efficiency gap in relation to spot welding robots in automotive body shops at foreign and domestic companies in North America. The main purpose of this paper is to determine body shop efficiency improvement opportunities for the domestic companies or the Big Three, therefore reducing the competitive gap and improving business performance. Design/methodology/approach -The following paper is an extension of an earlier dissertation study conducted by EL-Khalil that focused on improving body shop overall efficiency. The Harbour Report was utilized to determine the best in class facilities that must be visited for benchmarking purposes. The data and information presented were obtained from the facilities visited through observations and interviews. The research utilized the corresponding facilities' labs in order to perform measurements and inspect product welding efficiency. The data obtained were a result of a two-year benchmarking study. Findings -The inspection results of spot welds applied on the door flange do not justify the utilization of additional spot welding arm designs and/or robots for the domestic companies. The data presented provide a good opportunity for improving business performance at the body shop Big Three facilities. In order to reduce the current competitive gap, decrease cost, and improve utilization, the Big Three must adopt new strategies (i.e. communization of specific vehicles parts).Research limitations/implications -The benchmarking study was limited to the aperture area.Researchers are encouraged to test the propositions further on different types of vehicles and different areas of the vehicle body. Practical implications -Based on the actual findings, this paper presents a case that impacts the improvements of the body shop overall performance in relation to reducing the number of spot welding arm and robot designs at the automotive industry in North America. Originality/value -The presented gap analysis on body shop spot welding efficiency for automotive companies in North America was not conducted previously. Therefore, the data can be utilized as a benchmark target to drive improvements at the domestic automotive body shops.

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Section: Spot Welding Quantity and Performance Optimizationmentioning

confidence: 99%

Managing and improving robot spot welding efficiency: a benchmarking study

El-Khalil

2014

Benchmarking: An International Journal

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“…The research almost completely refers to spot welds, as they are more commonly used, but the methods can be applied to clinched assemblies. It is known that changing the distribution of the joints influences their loading, which can be described by an intensity criterion [9]. Furthermore, the joining design influences the overall stiffness of a complex joined assembly, as a vehicle body.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of the clinched joint loadings considering the initial pre-strain in the joining area

Martin

Bielak

Bobbert

et al. 2022

Prod. Eng. Res. Devel.

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The components of a body in white consist of many individual thin-walled sheet metal parts, which usually are manufactured in deep-drawing processes. In general, the conditions in a deep-drawing process change due to changing tribology conditions, varying degrees of spring back, or scattering material properties in the sheet blanks, which affects the resulting pre-strain. Mechanical joining processes, especially clinching, are influenced by these process-related pre-strains. The final geometric shape of a clinched joint is affected to a significant level by the prior material deformation when joining with constant process parameters. That leads to a change in the stiffness and force transmission in the clinched joint due to the different geometric dimensions, such as interlock, neck thickness and bottom thickness, which directly affect the load bearing capacity. Here, the influence of the pre-straining in the deep drawing process on the force distribution in clinch points in an automotive assembly is investigated by finite-element models numerically. In further studies, the results are implemented in an optimization tool for designing clinched components. The methodology starts with a pre-straining of metal sheets. This step is followed by 2D rotationally symmetric forming simulations of the joining process. The resulting mesh of each forming simulation is rotated and 3D models are obtained. The clinched joint solid model with pre-strains is used further to determine the joint stiffnesses. With the simulation of the same test set-up with an equivalent point-connector model, the equivalent stiffness for each pre-strain combination is determined. Simulations are performed on a clinched component to assess the influence of pre-strain and sheet thinning on the clinched joint loadings by using the equivalent stiffnesses. The investigations clearly show that for the selected component, the loadings at the clinch points are dependent on the sheet thinning and the stiffnesses due to pre-strain. The magnitude of the influence varies depending on the quantity considered. For example, the shear force is more sensitive to the joint stiffness than to the sheet thinning.

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“…7. The engineer performs numerical analyses and validates designs, often by using finite element analysis (FEA), based on properties such as crash-worthiness [126,127], noisevibration-harshness [47,128], and stiffness [18,129,130]. Simulation engineers consider joining elements in the product as a whole, evaluating joining designs for at least one complete product variant.…”

Section: State Of the Artmentioning

confidence: 99%

“…Finite element-based optimization approaches aim to find joining locations that comply with a set of performance criteria as well as to minimize the number of joining elements. The number and location of joining elements greatly influence performance characteristics, such as static, dynamic, and crash behavior [130]. Additionally, joining elements affect cost and production time, making it necessary to minimize their number [25].…”

Section: Location Optimizationmentioning

confidence: 99%

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

Eggink

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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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An adaptive optimization procedure for spot-welded structures

Cited by 18 publications

References 12 publications

Managing and improving robot spot welding efficiency: a benchmarking study

Managing and improving robot spot welding efficiency: a benchmarking study

Numerical investigation of the clinched joint loadings considering the initial pre-strain in the joining area

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

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