Application of machine learning for production optimization

Klöter, B.

doi:10.1109/pvsc.2018.8547467

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The paper formulates a joint optimization of the block size, task scheduling, and supply–demand configuration to maximize customers’ net profit with the probabilistic delay requirements, which addresses the critical issue of efficiency and latency in the blockchain-based live manufacturing process. Conversely, the study in [ 172 ] uses a support vector regression algorithm with an RBF kernel for troubleshooting production data to identify parameters responsible for high energy conversion efficiency variances. Ref.…”

Section: Maintenancementioning

confidence: 99%

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Frankó

Hollósi

Ficzere

et al. 2022

Sensors

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Industrial IoT (IIoT) has revolutionized production by making data available to stakeholders at many levels much faster, with much greater granularity than ever before. When it comes to smart production, the aim of analyzing the collected data is usually to achieve greater efficiency in general, which includes increasing production but decreasing waste and using less energy. Furthermore, the boost in communication provided by IIoT requires special attention to increased levels of safety and security. The growth in machine learning (ML) capabilities in the last few years has affected smart production in many ways. The current paper provides an overview of applying various machine learning techniques for IIoT, smart production, and maintenance, especially in terms of safety, security, asset localization, quality assurance and sustainability aspects. The approach of the paper is to provide a comprehensive overview on the ML methods from an application point of view, hence each domain—namely security and safety, asset localization, quality control, maintenance—has a dedicated chapter, with a concluding table on the typical ML techniques and the related references. The paper summarizes lessons learned, and identifies research gaps and directions for future work.

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Section: Maintenancementioning

confidence: 99%

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Frankó

Hollósi

Ficzere

et al. 2022

Sensors

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“…9 It has also been explored with regard to DoE optimization, 10 quality control, 11 and troubleshooting with access to wafer tracking. 12 However, simultaneous optimization of the entire fabrication process has not yet been done. This project aims to develop this capability.…”

mentioning

confidence: 99%

“…ML-based techniques for PV manufacturing have been explored for solar cell material design, optimizing individual processes, and a combination of processes . It has also been explored with regard to DoE optimization, quality control, and troubleshooting with access to wafer tracking . However, simultaneous optimization of the entire fabrication process has not yet been done.…”

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confidence: 99%

Optimization of Solar Cell Production Lines Using Neural Networks and Genetic Algorithms

Buratti

Eijkens

Hameiri

2020

ACS Appl. Energy Mater.

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To keep improving the efficiency-to-cost ratio of photovoltaic solar cells, manufacturing lines must be continuously improved. Efficiency optimization is usually performed process-wise and can be slow and time-consuming. In this study, we propose a machine-learning-based method to perform simultaneous multiprocess optimization. Using the natural variation of a production line, we train machine learning models to investigate the relationship between process parameters and cell efficiency. We employ genetic algorithms to identify new process parameters in order to maximize cell efficiency. The proposed method is demonstrated on a simulated production line of monocrystalline aluminum-back surface field solar cells. Using neural networks, an accurate model is built to predict cell efficiencies from input process parameters with errors of <0.03% absolute efficiency. In five iterations, the mean cell efficiency increases from 18.07% to 19.45%. Provided strong process monitoring and accurate wafer tracking, the proposed method is directly applicable to production-type datasets, enabling the photovoltaic industry to build smart factories and join the fourth industrial revolution.

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Development of an approach to wafer production in an industry 4.0 environment

Neduck¹,

Koepge²

2022

SiliconPV 2021, the 11th International Conference on Crystalline Silicon Photovoltaics

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Application of machine learning for production optimization

Cited by 5 publications

References 4 publications

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Optimization of Solar Cell Production Lines Using Neural Networks and Genetic Algorithms

Development of an approach to wafer production in an industry 4.0 environment

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