Introduction to the Special Section on AI in Manufacturing-Reference-Cited by-同舟云学术

Introduction to the Special Section on AI in Manufacturing

Published:2022-11-29 Issue:2 Volume:24 Page:81-85
ISSN:1931-0145
Container-title:ACM SIGKDD Explorations Newsletter
language:en
Short-container-title:SIGKDD Explor. Newsl.

Author:

Lijffijt Jefrey¹,Gkorou Dimitra²,Van Hertum Pieter²,Ypma Alexander²,Pechenizkiy Mykola³,Vanschoren Joaquin³

Affiliation:

1. Ghent University, Belgium

2. ASML, Netherlands

3. TU Eindhoven, Netherlands

Abstract

On 19 September 2022, the first workshop on AI for Manufacturing (AI4M Workshop) took place at ECML-PKDD, the European Conference on Machine Learning and Principles and Practice for Knowledge Discovery in Databases. The workshop brought together researchers and practitioners, from academia and industry, contributing their perspectives. This special section includes five articles in which Artificial Intelligence methods are used to address real problems in the manufacturing industry, ranging from the supply chain, to production, to quality insurance, and predictive maintenance. In this introduction, we present a high-level overview of the current state of the area: observed trends and the main open challenges. This overview is based on these papers, the keynote presentation, the panel discussion, and the discussion that emerged during the workshop.

Publisher

Association for Computing Machinery (ACM)

Subject

General Medicine

Link

https://dl.acm.org/doi/pdf/10.1145/3575637.3575650

Reference24 articles.

1. A. Giannoulidis , N. Nikolaidis , A. Naskos , A. Gounaris , and D. Caljouw . Investigating thresholding techniques in a real predictive maintenance scenario. SIGKDD Expl., 24(2) , 2022 . A. Giannoulidis, N. Nikolaidis, A. Naskos, A. Gounaris, and D. Caljouw. Investigating thresholding techniques in a real predictive maintenance scenario. SIGKDD Expl., 24(2), 2022.

2. Z. Li and M. van Leeuwen . Feature selection for fault detection and prediction based on log analysis. SIGKDD Expl., 24(2) , 2022 . Z. Li and M. van Leeuwen. Feature selection for fault detection and prediction based on log analysis. SIGKDD Expl., 24(2), 2022.

3. M.L. Nunes , M. Barandas , H. Gamboa , and F. Soares . Acoustic structural integrity assessment of ceramics using supervised machine learning and uncertainty-based rejection. SIGKDD Expl., 24(2) , 2022 . M.L. Nunes, M. Barandas, H. Gamboa, and F. Soares. Acoustic structural integrity assessment of ceramics using supervised machine learning and uncertainty-based rejection. SIGKDD Expl., 24(2), 2022.

4. S. Gamage , B. Kl¨opper , and J. Samarabandu . Experiences with contrastive predictive coding in industrial time-series classification. SIGKDD Expl., 24(2) , 2022 . S. Gamage, B. Kl¨opper, and J. Samarabandu. Experiences with contrastive predictive coding in industrial time-series classification. SIGKDD Expl., 24(2), 2022.

5. N. Brockmann , E. Elson Kosasih , S. Baker , I. Blair , and A. Brintrup . Supply chain link prediction on an uncertain knowledge graph. SIGKDD Expl., 24(2) , 2022 . N. Brockmann, E. Elson Kosasih, S. Baker, I. Blair, and A. Brintrup. Supply chain link prediction on an uncertain knowledge graph. SIGKDD Expl., 24(2), 2022.