Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities

Jan, Zohaib; Ahamed, Farhad; Mayer, Wolfgang; Patel, Niki; Großmann, Georg; Stumptner, Markus; Kuusk, Ana

doi:10.1016/j.eswa.2022.119456

Cited by 128 publications

(67 citation statements)

References 105 publications

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“…Interestingly, the authors suggest that manufacturers that have implemented lean and/or six sigma approaches have relatively small defect samples and data sets, causing challenges with the adoption of ML and DL methods that otherwise require big data sets. Jan et al (2023) also discuss CNNs for quality control (by capturing product defects through CCTV images) and Duc and Bilik (2022) further present computer vision and AI technology as a means of detecting surface scratches on production parts in a machining center, reducing defectives from 100% to zero defects.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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“…196,197 The development of robust ML models relies heavily on the availability and quality of data. 198,199 One of the challenges in this field is the limited access to comprehensive data sets that capture the complexity of biopolymer systems. Future research should prioritize the creation of extensive, high-quality data sets.…”

Section: Future Workmentioning

confidence: 99%

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Bin Abu Sofian,

Sun,

Gupta

et al. 2024

Energy Fuels

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This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, and biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for their roles in sustainable energy and fuel sectors. These polymers, when integrated with ML techniques, exhibit enhanced functionalities, optimizing renewable energy systems, storage, and conversion. Detailed case studies reveal the potential of biobased polymers in energy applications and the fuel industry, further showcasing how ML bolsters fuel efficiency and innovation. The intersection of biobased polymers and ML also marks advancements in biochemical production, emphasizing innovations in drug delivery and medical device development. This review underscores the imperative of harnessing the convergence of ML and biobased polymers for future global sustainability endeavors in energy, fuels, and biochemicals. The collective evidence presented asserts the immense promise this union holds for steering a sustainable and innovative trajectory.

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“…The industrial revolution has driven companies to seek out cost-effective ways to improve manufacturing efficiency and product quality [ 1 ]. The fourth industrial revolution, Industry 4.0, has been fueled by rapid technological advances and is transforming people’s lives through the adoption of technologies such as AI, blockchain, AR, robotics, and IoT [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

Pellicer

Tungekar

Carpitella

2023

Sensors

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Industry 4.0 technologies offer manufacturing companies numerous tools to enhance their core processes, including monitoring and control. To optimize efficiency, it is crucial to effectively install monitoring sensors. This paper proposes a Multi-Criteria Decision-Making (MCDM) approach as a practical solution to the sensor placement problem in the food industry, having been applied to wine bottling line equipment at a real Italian winery. The approach helps decision-makers when discriminating within a set of alternatives based on multiple criteria. By evaluating the interconnections within the different equipment, the ideal locations of sensors are suggested, with the goal of improving the process’s performance. The results indicated that the system of electric pumps, corker, conveyor, and capper had the most influence on the other equipment which are then recommended for sensor control. Monitoring this equipment will result in the early discovery of failures, potentially also involving other dependant equipment, contributing to enhance the level of performance for the whole bottling line.

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Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities

Cited by 128 publications

References 105 publications

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Where to Place Monitoring Sensors for Improving Complex Manufacturing Systems? Discussing a Real Case in the Food Industry

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