Recent Technological Progress to Empower Smart Manufacturing: Review and Potential Guidelines

Haricha, Karim; Khiat, Azeddine; Issaoui, Yassine; Bahnasse, Ayoub; Ouajji, Hassan

doi:10.1109/access.2023.3246029

Cited by 20 publications

(5 citation statements)

References 148 publications

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“…An expert system is a software application that simulates the problem-solving skills of a human specialist in a specific domain. It consists of a knowledge base, which stores facts and rules about the domain, and an inference engine, which applies logical reasoning to infer solutions or suggestions [19]. One of the benefits of using an expert system is its ability to provide consistent and reliable answers to complex problems, handling uncertainty and incomplete information through techniques such as probability.…”

Section: Expert Systemmentioning

confidence: 99%

Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes

Kirdponpattara,

Sooraksa,

Boonjing

2024

IEEE Access

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The manufacturing of hard disk drives involves the intricate assembly of numerous components, making the testing process time-consuming and resource intensive. To optimize the manufacturing process and increase testing efficiency, the development of a rule-based expert system is proposed. This system leverages predictive models constructed from assembly process data to identify potentially defective hard drives before undergoing extensive testing. By preemptively identifying defects, this approach substantially reduces testing time and enhances tester capacity. Given the categorical and imbalanced nature of assembly data, Decision Trees are employed as the prediction model. Specifically, three Decision Tree algorithms are explored: ID3, C4.5, and CART. In addition, four feature selection techniques, namely Information

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Section: Expert Systemmentioning

confidence: 99%

Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes

Kirdponpattara,

Sooraksa,

Boonjing

2024

IEEE Access

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“…However, there are various challenges in the way of the shift to Industry 4.0. Because different manufacturers produce different products, current manufacturing frameworks frequently struggle with challenges related to centralized control systems and data heterogeneity [18], [19]. Such centralized structures may instigate compatibility problems with current legacy systems and hinder the easy incorporation of Industry 4.0 technology [20].…”

Section: Industry 40: a Digital Revolutionmentioning

confidence: 99%

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Baroud

2024

J. Appl. Data Sci.

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Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter maintenance paradigms in the mechatronics sector. With the advent of Industry 4.0, mechatronic systems have become increasingly sophisticated, highlighting the critical need for advanced maintenance methodologies that are both efficient and effective. This paper delves into the confluence of cutting-edge AI techniques, including machine learning (ML) and deep learning (DL), with multi-agent systems (MAS) to enhance fault diagnosis precision and facilitate PdM in the context of Industry 4.0. Specifically, we explore the use of various ML models, including Support Vector Machines (SVMs) and Random Forests (RFs), and DL architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have been effectively oriented to analyses complex industrial data. Initially, the study examines the progress in AI algorithms that accelerate fault identification by leveraging data from system operations, sensors, and historical trends. AI-enabled fault diagnosis rapidly detects irregularities and discerns the fundamental causes, thereby minimizing downtime and enhancing system reliability and efficiency. Furthermore, this paper underscores the adoption of AI-driven PdM approaches, emphasizing prognostics that predict the Remaining Useful Life (RUL) of machinery. This predictive capability allows for the strategic scheduling of maintenance activities, optimizing resource use, prolonging the lifespan of expensive assets, and refining the management of spare parts inventory. The tangible advantages of employing AI for fault diagnosis and PdM are showcased through a case study from authentic mechatronics implementations. This case study highlights successful implementations, documenting real-world challenges such as data integration issues and system interoperability, and elaborates on the strategies deployed to navigate these obstacles. The results demonstrate improved operational reliability and cost savings and shed light on the pragmatic considerations and solutions that facilitate the adoption of AI and MAS in industrial applications. The paper also navigates the challenges and prospective research avenues in applying AI within the mechatronics domain of Industry 4.0, setting the stage for ongoing innovation and exploration in this transformative domain.

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“…It calls for further research into management tools integrating machine learning and artificial intelligence and suggests broadening future studies beyond academic papers [7]. Contemporary literature in SL advocates for AI integration in manufacturing, emphasizing its role in optimizing logistics processes for enhanced efficiency in the context of Smart Logistics [8] emphasizing the importance of AI-driven solutions in SL, proposing advanced system that optimizes order dispatch, improves delivery time predictions, and outperforms traditional methods, ultimately enhancing efficiency and reducing costs in warehousing using deep learning [9] However, the proposal to use clustering as a method to tackle SLAP within the context of SL has never been introduced in the literature. It's crucial to emphasize that efficient storage location assignments are the foundation for optimizing order preparation, and this aspect should take precedence over the role of TSP in routing optimization.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

Kalkha,

Khiat,

Bahnasse

et al. 2024

IEEE Access

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In the rapidly evolving domain of e-commerce, effective warehouse management emerges as a critical factor for ensuring timely deliveries. This paper addresses the Storage Location Assignment Problem (SLAP) in e-commerce warehouses, a challenge intensified by varying product volumes and unpredictable demands. We introduce a novel Intelligent Storage Location Assignment (ISLA) method that utilizes advanced time series clustering algorithms specifically, Self-Organizing maps, dynamic time warping-Based k-means, and Agglomerative Hierarchical Clustering (AHC), to optimize order fulfillment and enhance warehouse efficiency. By clustering and positioning items with similar demand patterns, our approach minimizes order preparation time, reduces unnecessary warehouse movements, and improves operational flows. Our empirical evaluation, based on a real-world dataset from Kaggle, demonstrates the superiority of AHC in efficiently grouping high-turnover items, as evidenced by higher silhouette scores. Applying this method in simulations across various picking strategies such as s-shape, mid-point, discrete order picking, zone picking, and batch picking, we achieve significant efficiency improvements. Notably, our ISLA method results in up to 61% and 69% efficiency gains under s-shape and midpoint routing policies, respectively, outperforming traditional random and ABC storage assignments. These results not only highlight the significant potential of Artificial Intelligence (AI) in revolutionizing warehouse operations but also bridge the existing knowledge gap by showcasing a practical and impactful application of AI in SLAP. Our research advances the field of smart logistics, emphasizing the critical role of AI-driven intelligent storage location assignment in optimizing warehouse processes and enhancing the efficiency of the e-commerce supply chain.

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Recent Technological Progress to Empower Smart Manufacturing: Review and Potential Guidelines

Cited by 20 publications

References 148 publications

Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes

Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

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