Potentiale datengestützter Methoden zur Gestaltung und Optimierung mechanischer Fügeverbindungen

Zirngibl, Christoph; Schleich, Benjamin; Wartzack, Sandro

doi:10.35199/dfx2020.8

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Due to the increasing complexity of multidimensional distributions, experimental studies are only applicable for a limited number of parameter variations. Nevertheless, to enable the identification of optimal joint connections, the application of parametric studies in combination with data-based methods already showed high potentials for the analysis of a wide range of different tool geometries (Zirngibl et al, 2020). However, machine learning methods like artificial neural networks (ANN) often require a sufficiently high amount of data.…”

Section: Introductionmentioning

confidence: 99%

Approach for the Automated and Data-Based Design of Mechanical Joints.

2021

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As a result of the increasing challenges in the field of lightweight constructions, the demand for efficient joining technologies is continuously rising. For this purpose, cold forming processes offer an environmental friendly and fast alternative to established joining methods (e.g. welding). However, to ensure a high reliability, not only the selection of an appropriate procedure, but also the dimensioning of the individual joint is essential. While product designers can rely on a wide range of design principles for thermal processes, the dimensioning and evaluation of mechanical joining processes is mainly based on expert knowledge and a few experimental tests. Although few studies already investigated the numerical analysis of mechanical joints, an approach for the sustainable and consistent optimization of the strength and reliability of joining connections for varying use-cases is not available yet. Motivated by this lack, this paper presents an approach for the automated transfer of information within the process chain and the data-based analysis of mechanical joints by using clinching as an example. Therefore, the CRISP-DM reference model is used for the systematic data mining.

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Section: Introductionmentioning

confidence: 99%

Approach for the Automated and Data-Based Design of Mechanical Joints.

2021

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“…This mainly involves the adjustment and optimization of particular clinching tool and process parameters to maximize quality-relevant geometrical joint characteristics, such as the neck and interlock thickness. For this, the implementation of machine learning methods in combination with a genetic algorithm (GA) showed already a high applicability to determine a solution space involving a wide range of possible design alternatives [3]. Since this mainly requires the definition of a sufficient amount of training data, it is often time-and cost-intensive to setup powerful metamodels and thus to achieve a desired prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Application of reinforcement learning for the optimization of clinch joint characteristics

Zirngibl

Dworschak

Schleich

et al. 2021

Prod. Eng. Res. Devel.

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Due to increasing challenges in the area of lightweight design, the demand for time- and cost-effective joining technologies is steadily rising. For this, cold-forming processes provide a fast and environmentally friendly alternative to common joining methods, such as welding. However, to ensure a sufficient applicability in combination with a high reliability of the joint connection, not only the selection of a best-fitting process, but also the suitable dimensioning of the individual joint is crucial. Therefore, few studies already investigated the systematic analysis of clinched joints usually focusing on the optimization of particular tool geometries against shear and tensile loading. This mainly involved the application of a meta-model assisted genetic algorithm to define a solution space including Pareto optima with all efficient allocations. However, if the investigation of new process configurations (e. g. changing materials) is necessary, the earlier generated meta-models often reach their limits which can lead to a significantly loss of estimation quality. Thus, it is mainly required to repeat the time-consuming and resource-intensive data sampling process in combination with the following identification of best-fitting meta-modeling algorithms. As a solution to this problem, the combination of Deep and Reinforcement Learning provides high potentials for the determination of optimal solutions without taking labeled input data into consideration. Therefore, the training of an Agent aims not only to predict quality-relevant joint characteristics, but also at learning a policy of how to obtain them. As a result, the parameters of the deep neural networks are adapted to represent the effects of varying tool configurations on the target variables. This provides the definition of a novel approach to analyze and optimize clinch joint characteristics for certain use-case scenarios.

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“…[2] Consequently, the adjustment of design or process parameters often requires high time-and cost-intensive development iterations. Therefore, data-based methods provide high potentials for the optimization of target features [3]. However, for the efficient application of techniques like artificial neural networks, a sufficiently large amount of data is often required.…”

mentioning

confidence: 99%

Approach for the Automated Analysis of Geometrical Clinch Joint Characteristics

Zirngibl¹,

Schleich²

2021

KEM

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Due to their cost-efficiency and environmental friendliness, the demand of mechanical joining processes is constantly rising. However, the dimensioning and design of joints and suitable processes are mainly based on expert knowledge and few experimental data. Therefore, the performance of numerical and experimental studies enables the generation of optimized joining geometries. However, the manual evaluation of the results of such studies is often highly time-consuming. As a novel solution, image segmentation and machine learning algorithm provide methods to automate the analysis process. Motivated by this, the paper presents an approach for the automated analysis of geometrical characteristics using clinching as an example.

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Potentiale datengestützter Methoden zur Gestaltung und Optimierung mechanischer Fügeverbindungen

Cited by 5 publications

References 6 publications

Approach for the Automated and Data-Based Design of Mechanical Joints.

Approach for the Automated and Data-Based Design of Mechanical Joints.

Application of reinforcement learning for the optimization of clinch joint characteristics

Approach for the Automated Analysis of Geometrical Clinch Joint Characteristics

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