Quality Prediction in Interlinked Manufacturing Processes based on Supervised &amp; Unsupervised Machine Learning

Lieber, Daniel S.; Stolpe, Marco; Konrad, Benedikt; Deuse, Jochen; Morik, Katharina

doi:10.1016/j.procir.2013.05.033

Cited by 106 publications

(39 citation statements)

References 13 publications

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“…The work published by Lieber, Stolpe, Konrad et al [33] represents a good research within steel industry production. It proposes an approach for automatically preprocessing value series data to improve the quality of the process and products.…”

Section: Emerging Trends Of Artificial Intelligence and Machine Learnmentioning

confidence: 99%

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

et al. 2020

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Adaptation and innovation are extremely important to the manufacturing industry. This development should lead to sustainable manufacturing using new technologies. To promote sustainability, smart production requires global perspectives of smart production application technology. In this regard, thanks to intensive research efforts in the field of artificial intelligence (AI), a number of AI-based techniques, such as machine learning, have already been established in the industry to achieve sustainable manufacturing. Thus, the aim of the present research was to analyze, systematically, the scientific literature relating to the application of artificial intelligence and machine learning (ML) in industry. In fact, with the introduction of the Industry 4.0, artificial intelligence and machine learning are considered the driving force of smart factory revolution. The purpose of this review was to classify the literature, including publication year, authors, scientific sector, country, institution, and keywords. The analysis was done using the Web of Science and SCOPUS database. Furthermore, UCINET and NVivo 12 software were used to complete them. A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now. Eighty-two articles were reviewed and classified. A first interesting result is the greater number of works published by the USA and the increasing interest after the birth of Industry 4.0.

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Section: Emerging Trends Of Artificial Intelligence and Machine Learnmentioning

confidence: 99%

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

et al. 2020

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“…This study also suggested an effective assessment method for small samples of CP and provided a guideline for enterprise management on the implementation of CP for vanadium extraction from stone coal. Lieber et al (2013) developed a systematic framework based on DM for predicting the quality of products in interlinked manufacturing processes using a rolling mill case study.…”

Section: Data Miningmentioning

confidence: 99%

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Ren

Zhang

Liu

et al. 2019

Journal of Cleaner Production

358

189

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“…Classification algorithms can be broadly categorised as supervised, semisupervised or unsupervised (for a theoretical review of these methods, [5] [6] [7] are recommended). With a supervised approach, the algorithm is presented with labelled data -a set of input vectors, each of which is associated with an observed output value (or 'label').…”

Section: Introductionmentioning

confidence: 99%

Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

Okaro¹,

Jayasinghe²,

Sutcliffe³

et al. 2018

Preprint

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Risk-averse areas such as the medical, aerospace and energy sectors have been somewhat slow towards accepting and applying Additive Manufacturing (AM) in many of their value chains. This is partly because there are still signicant uncertainties concerning the quality of AM builds. This paper introduces a machine learning algorithm for the automatic detection of faults in AM products. The approach is semi-supervised in that, during training, it is able to use data from both builds where the resulting components were certied and builds where the quality of the resulting components is unknown. This makes the approach cost ecient, particularly in scenarios where part certication is costly and time consuming. The study specically analyses Selective Laser Melting (SLM) builds. Key features are extracted from large sets of photodiode data, obtained during the building of 49 tensile test bars. Ultimate tensile strength (UTS) tests were then used to categorise each bar as `faulty' or `acceptable'. A fully supervised approach identied faulty specimens with a 77% success rate while the semi-supervised approach was able to consistently achieve similar results, despite being trained on a fraction of the available certication data. The results show that semi-supervised learning is a promising approach for the automatic certication of AM builds that can be implemented at a fraction of the cost currently required.

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Quality Prediction in Interlinked Manufacturing Processes based on Supervised & Unsupervised Machine Learning

Cited by 106 publications

References 13 publications

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Automatic Fault Detection for Selective Laser Melting Using Semi-Supervised Machine Learning

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