AHSS applications in Industry 4.0: Determination of optimum processing parameters during coiling process through unsupervised machine learning approach

Srivastava, Ashok; Patra, P.K.; Jha, Rajesh

doi:10.1016/j.mtcomm.2022.103625

Cited by 5 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Product design [38] Correlation between process and quality [23] Quality prediction [26] Quality improvement [39]…”

Section: Productmentioning

confidence: 99%

“…Know-why, based on the information from Know-what, aims to identify inner patterns from historical data, thereby discovering the reasons for a thing happening. Knowwhy includes the identification of interactions among different variables [23] and the discovery of cause-effect relationship between an event and other variables [24,25]. On one hand, Know-why can indicate most important factors for understanding Know-what.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

et al. 2023

View full text Add to dashboard Cite

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.

show abstract

“…Product design [38] Correlation between process and quality [23] Quality prediction [26] Quality improvement [39]…”

Section: Productmentioning

confidence: 99%

mentioning

confidence: 99%