Similarity-search and Prediction Based Process Parameter Adaptation for Quality Improvement in Interlinked Manufacturing Processes

Schmitt, Jacqueline; Deuse, Jochen

doi:10.1109/ieem.2018.8607361

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…In this context, knowledge about processes, machines, and or causal relationships is needed so that data can be meaningfully used for the modelling. In this respect, Schmitt and Deuse (2018) for example stressed the relevance of process knowledge in selecting input variables for the modeling. Qu et al (2016) also argued that expert knowledge about the manufacturing system is needed to design effective algorithms.…”

Section: Performancementioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

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Advanced analytics applications in smart manufacturing provide beneficiary effects such as predictive maintenance, energy optimized factories, and automized quality management of products. While the benefits and thus the sustainability effects are well investigated, the research field is characterized by many single applications with little conceptual synthesis. Therefore, this research aims to systematize the field and to identify specific sustainability themes in context of advance analytics applications. This chapter provides an overview of existing advanced analytics applications in smart manufacturing and clusters the addressed sustainability themes. Applying a systematic literature review, following the PRISMA statement, 65 different applications within four research perspective could be identified. Furthermore, ten different sustainability-related effects of the applications in smart manufacturing unfold. The content analysis resulted in 27 sustainability themes, which holistically cover all spheres of sustainability, with a focus on the social and economic spheres.

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Section: Performancementioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

View full text Add to dashboard Cite

show abstract

“…Lieber et al (2013) describe a methodical framework based on data mining for predicting the physical quality of intermediate products in interlinked manufacturing processes in the context of a rolling mill case study. Other approaches to defect prediction also do not include specific modules for they systematic assessment of the available database (Arif, Suryana and Hussin, 2013;Kao et al, 2017;Wuest, Irgens and Thoben, 2013;Schmitt and Deuse, 2018).…”

Section: Implementation Of Defect Predictionmentioning

confidence: 99%

Methodological Assessment of Data Suitability for Defect Prediction

et al. 2020

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Purpose: This paper provides a domain specific concept to assess data suitability of various data sources along the production chain for defect prediction.Methodology/Approach: A seven-phase methodology is developed in which the data suitability for defect prediction in interlinked production steps is assessed. For this purpose, the manufacturing process is mapped and potential influencing variables on the origin of defects are identified. The available data is evaluated and quantified with regard to the criteria relevancy, completeness, appropriate amount of data, accessibility and interpretability. The individual assessments are then visualized in an overview, gaps in data acquisition are identified and needs for action are derived.Findings: The research shows a seven-phase methodology to systematically assess data suitability for defect prediction and identify data gaps in interlinked production steps.Research Limitation/implication: This research is limited to the analysis of contextual data quality for the use case of defect prediction. Other data analytics applications or processes outside of manufacturing are not included.Originality/Value of paper: The paper provides a new approach to identify gaps in data acquisition by systematically assessing data suitability for defect prediction and deducting needs for action. The accuracy of predictive defect models is then to be improved by the subsequent optimization of the data basis.

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Six Sigma and Random Forests Application for Product Quality System Control Development

Antosz

Gola

Paśko

et al. 2022

Lecture Notes in Mechanical Engineering

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Similarity-search and Prediction Based Process Parameter Adaptation for Quality Improvement in Interlinked Manufacturing Processes

Cited by 6 publications

References 7 publications

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Methodological Assessment of Data Suitability for Defect Prediction

Six Sigma and Random Forests Application for Product Quality System Control Development

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