Approach to adapt manufacturing process parameters systematically based on machine learning algorithms

Radetzky, Max; Rosebrock, Christoph; Bracke, Stefan

doi:10.1016/j.ifacol.2019.11.458

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…It seems that machine learning is a suitable approach for analysis of a manufacturing process, which is characterized with performance variations of high-speed interconnections [16]. How a manufacturing process and its parameters to be improved through applying machine learning is also discussed in several other papers [17]- [19]. A review regarding machine learning and artificial intelligence application in manufacturing processes is performed in [20], where the authors draw the benefits and gaps.…”

Section: Figure 1 the Constructed Bibliometric Mapmentioning

confidence: 99%

Statistical and machine learning approach for evaluation of control systems for automatic production lines

Tsenev

Ivanova

2022

Bulletin EEI

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The manufacturing processes and the control systems for automatic production lines mainly are evaluated through usage of statistical methods as recently machine learning algorithms are also used. The aim of the paper is to present an approach for control measurement systems evaluation, based on a combination of statistical techniques like attribute repeatability and reproducibility analysis, measurement system analysis and supervised machine learning algorithms like random forest and KNN. The proposed method is verified in the production of the G8680x connector, which is used in the automotive industry. The control is performed 100% for all manufactured parts immediately after the “injection molding” process. It is proved that taking advantages of the statistics and machine learning, the manufacturing process and control measurement systems could be evaluated with very high accuracy. The exploration and analysis leads to the formulation of some recommendations in support of process engineers and managers.

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Section: Figure 1 the Constructed Bibliometric Mapmentioning

confidence: 99%

Statistical and machine learning approach for evaluation of control systems for automatic production lines

Tsenev

Ivanova

2022

Bulletin EEI

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“…The former, in combination with the advancements in computational capabilities, has resulted in machine learning approaches based on ANNs gaining a lot of popularity within the manufacturing community as a whole, both in industry and academia. They are used for a wide range of applications, including tool wear monitoring and forecasting [16,17], decision support systems [18], process parameter predictions [19], quality control [20,21], etc. They are also gaining more traction within incremental sheet forming; specifically, Khan et al [22] used ANNs to predict local springback errors in an SPIF process and adjusted the tool path accordingly.…”

Section: Introductionmentioning

confidence: 99%

Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium

et al. 2020

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A strategy to adjust the product geometry autonomously through an online control of the manufacturing process in incremental sheet forming with active medium is presented. An axial force sensor and a laser distance sensor are integrated into the process setup to measure the forming force and the product height, respectively. Experiments are conducted to estimate the bulging behavior for different pre-determined tool paths. An artificial neural network is consequently trained based on the experimental data to continuously predict the pressure levels required to control the final product height. The predicted pressure is part of a closed-loop control to improve the geometrical accuracy of formed parts. Finally, experiments were conducted to verify the results, where truncated cones with different dimensions were formed with and without the closed-loop control. The results indicate that this strategy enhances the geometrical accuracy of the parts and can potentially be expanded to be implemented for different types of material and geometries.

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Digitalized manufacturing process sequences – foundations and analysis of the economic and ecological potential

Beckers

Hommen

Becker

et al. 2022

CIRP Journal of Manufacturing Science and Technology

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Approach to adapt manufacturing process parameters systematically based on machine learning algorithms

Cited by 7 publications

References 2 publications

Statistical and machine learning approach for evaluation of control systems for automatic production lines

Statistical and machine learning approach for evaluation of control systems for automatic production lines

Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium

Digitalized manufacturing process sequences – foundations and analysis of the economic and ecological potential

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