Applying Natural Language Processing in Manufacturing

May, Marvin Carl; Neidhöfer, Jan; Körner, Tom; Schäfer, Louis; Lanza, Gisela

doi:10.1016/j.procir.2022.10.071

Cited by 14 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Process monitoring [40] Root cause analysis of process failure [41], Process modelling [42] Process fault prediction [43], Process characteristics prediction [44] Self-optimizing process planning [45], Adaptive process control [46] Machine Machine tool monitoring [47] Fault diagnosis [48], Downtime prediction [49] RUL prediction [50], Tool wear prediction [51] Adaptive compensation of errors [52,53],…”

Section: Processmentioning

confidence: 99%

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

et al. 2023

Self Cite

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

Section: Processmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, identifying commonality could foster knowledge exchange among sectors that may not typically interact. Several methods have been employed for analysing manufacturing processes and which have the potential to be used in identifying commonality, including Process Flow Diagrams (PFDs) [14], Life Cycle Assessment (LCA) [15], Material Flow Analysis (MFA) [16], and Natural Language Processing (NLP) [17].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, PFDs provide a visual representation of the process flow, but they are limited in analysing complex interdependencies [14]. NLP, while capable of extracting information from unstructured text, may require significant preprocessing and may not be as specialised for manufacturing process analysis [17].…”

Section: Introductionmentioning

confidence: 99%

Greening Foundation Industries: Shared Processes and Sustainable Pathways

Sherif,

Sarfraz,

Jolly

et al. 2023

Sustainability

View full text Add to dashboard Cite

Foundation industries, encompassing metals, ceramics, cement, paper, chemicals, and glass, play a vital role in driving industrial economies. Despite their pivotal role, a comprehensive understanding of shared processes and their impact on resource utilisation remains elusive. This study employs a novel approach, leveraging an adapted Dependency Structure Matrix (DSM), to unveil the core processes commonly utilised among these industries. These processes are then evaluated based on their influence on energy consumption and CO2 emission. The investigation revealed 18 common processes categorised by their processing principles, their expected outcomes, and the equipment used. Remarkably, these processes emerge as significant contributors to both energy consumption and CO2 emissions. Notably, pyroprocessing emerged as a prevalent practice in five out of the six sectors, while the production of dried products and crushers and mills were the most frequently encountered outcomes and equipment used, respectively. This paper discusses the implications of these findings for foundation industries, emphasising potential areas for enhancing manufacturing operations to reduce environmental damage and facilitate knowledge transfer among the various sectors. Furthermore, the study identifies shared abatement options that can be collectively implemented across industries to achieve more substantial reductions in environmental footprint. By identifying and prioritising the most impactful processes in foundation industries, this study provides a strategic footing for advancing sustainable and efficient manufacturing practices within these critical sectors.

show abstract

Deep Learning in Industry 4.0: Transforming Manufacturing Through Data-Driven Innovation

Agrawal,

Nargund

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Applying Natural Language Processing in Manufacturing

Cited by 14 publications

References 11 publications

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

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

Greening Foundation Industries: Shared Processes and Sustainable Pathways

Deep Learning in Industry 4.0: Transforming Manufacturing Through Data-Driven Innovation

Contact Info

Product

Resources

About