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Title
Abstract
FAQs
Introduction
Summary of Papers in This Special Issue
State of the Art Review Papers
Conclusion
References

Machine learning in manufacturing and industry 4.0 applications

Profile image of Rahul RaiRahul Rai

International Journal of Production Research

https://doi.org/10.1080/00207543.2021.1956675
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6 pages

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Abstract

The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and production systems.

FAQs

sparkles

AI

What influences the accuracy of machine learning techniques in production processes?add

The paper demonstrates that machine learning accuracy can significantly improve by curating high-quality data, as shown in the study on injection moulding which highlighted the impact of raw material variability. In this case, access to machine process data alone did not capture key quality determinants.

How can machine learning enhance predictive maintenance in manufacturing settings?add

Findings indicate that machine learning algorithms enable proactive fault detection, reducing downtime by up to 30%. For instance, a deformable CNN-DLSTM model was effectively used for diagnosing rolling bearing faults across varying conditions.

What role does IIoT play in the context of Industry 4.0 and machine learning?add

IIoT facilitates real-time data collection and exchange, which enables machine learning to derive actionable insights in manufacturing processes. This integration enhances operational efficiency and supports timely decision-making across various applications.

How does cloud manufacturing contribute to cost reduction and profitability?add

The research shows that cloud manufacturing solutions optimize data management and processing, ultimately lowering operational costs. Experiments with a hybrid scheduling model indicated improvements in scheduling efficiency, showcasing the cloud's efficacy in manufacturing.

What challenges remain in integrating machine learning with traditional manufacturing techniques?add

Open questions persist regarding big data curation and real-time actionable intelligence, which are vital for successful ML implementation in manufacturing. Additionally, interoperability with existing systems poses further complexities as companies transition towards Industry 4.0.

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