Leveraging industry 4.0 and circular open innovation for digital sustainability: The role of circular ambidexterity DOI
Noor Ul Hadi, Balqees Naser Almessabi, Muhammad Imran Khan

et al.

Journal of Open Innovation Technology Market and Complexity, Journal Year: 2025, Volume and Issue: 11(2), P. 100545 - 100545

Published: May 2, 2025

Language: Английский

Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis DOI Creative Commons
Lorena Espina-Romero, Humberto Gutiérrez Hurtado, Doile Enrique Ríos Parra

et al.

Sci, Journal Year: 2024, Volume and Issue: 6(4), P. 60 - 60

Published: Oct. 3, 2024

This study explores the evolution and impact of research on challenges opportunities in implementation artificial intelligence (AI) manufacturing between 2019 August 2024. By addressing growing integration AI technologies sector, seeks to provide a comprehensive view how applications are transforming production processes, improving efficiency, opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, thematic trends. Data were collected from Scopus, detailed review publications carried out identify knowledge gaps unresolved questions. The results reveal steady increase related manufacturing, with strong focus automation, predictive maintenance, supply chain optimization. also highlights dominance certain institutions authors driving this field research. Despite progress, significant remain, particularly regarding scalability solutions ethical considerations. findings suggest that while holds considerable potential for industry, more interdisciplinary is needed address existing maximize its benefits.

Language: Английский

Citations

9

Machine learning for quality control of Tin-Copper electrodes DOI Creative Commons
Anesu Nyabadza,

Lola Azoulay-Younes,

Mercedes Vázquez

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117191 - 117191

Published: March 1, 2025

Language: Английский

Citations

1

Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy DOI
Zhenya Wang, Liang Pan,

Rengui Bai

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 63, P. 102972 - 102972

Published: Dec. 2, 2024

Language: Английский

Citations

6

Digital twin-based smart shop-floor management and control: A review DOI
Cunbo Zhuang, Lei Zhang, Shimin Liu

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103102 - 103102

Published: Jan. 9, 2025

Language: Английский

Citations

0

Exploring the synergy of python programming in single point incremental forming DOI

S. Pratheesh Kumar,

N. Mugilan

International Journal on Interactive Design and Manufacturing (IJIDeM), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

Language: Английский

Citations

0

Integrating Knowledge and Data-Driven Artificial Intelligence for Decisional Enterprise Interoperability DOI
Pantelis Karapanagiotis, S. Waschull,

Jessica Zotelli

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 372 - 398

Published: Jan. 1, 2025

Language: Английский

Citations

0

Artificial Intelligence for Quality Defects in the Automotive Industry: A Systemic Review DOI Creative Commons
Oswaldo Morales Matamoros,

José Guillermo Takeo Nava,

Jesús Jaime Moreno Escobar

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1288 - 1288

Published: Feb. 20, 2025

Artificial intelligence (AI) has become a revolutionary tool in the automotive sector, specifically quality management and issue identification. This article presents systematic review of AI implementations whose target is to enhance production processes within Industry 4.0 5.0. The main methods analyzed are deep learning, artificial neural networks, principal component analysis, which improve defect detection, process automation, predictive maintenance. manuscript emphasizes AI’s role live auto part tracking, decreasing dependance on manual inspections, boosting zero-defect manufacturing strategies. findings indicate that control tools, like convolutional networks for computer vision considerably strengthen fault identification precision while reducing material scrap. Furthermore, allows proactive maintenance by predicting machine defects before they happen. study points out importance incorporating solutions actual ensure consistent adaptation 5.0 requirements. Future investigations should prioritize transparent approaches, cyber-physical system consolidation, enhancement sustainable production. In general terms, changing assurance industry, improving efficiency, consistency, long-term results.

Language: Английский

Citations

0

Leveraging AI to Ignite Innovation in Small and Medium Enterprises: Challenges and Opportunities DOI Creative Commons
Peiqian Wu, Yan Zhu, Wenli Chen

et al.

Published: March 5, 2025

Small and medium enterprises (SMEs) form the backbone of many economies, yet they often struggle to remain competitive innovative under resource constraints. Rapid advances in artificial intelligence (AI) offer fresh possibilities for SMEs transform their operations, discover untapped market segments, foster resilient business models. AI tools can enhance decision-making reduce operational inefficiencies, from automating repetitive processes generating predictive insights. At same time, ethical considerations data privacy concerns underscore importance implementing responsibly. By embracing cross-sector collaboration, developing robust training programs, advocating supportive policy frameworks, harness AI’s immense potential without compromising social values or organizational integrity. This paper highlights both opportunities challenges poses, proposing actionable strategies that enable drive sustainable, inclusive growth.

Language: Английский

Citations

0

Advances and Challenges of Enhancing Operational Efficiency and Maintenance Protocols in the Construction Industry by Combining DT, AI, and Optimization DOI
Mateja Držečnik, Uroš Klanšek

Advances in business information systems and analytics book series, Journal Year: 2025, Volume and Issue: unknown, P. 225 - 284

Published: Feb. 14, 2025

The construction industry is undergoing a fundamental transformation with the introduction of advanced digital technologies such as twins (DT), artificial intelligence (AI) and optimization. These increase operational efficiency, improve maintenance, promote sustainability. DT enable real-time monitoring optimization projects, while AI analyzes large data sets for predictive maintenance resource Optimization algorithms support efficient planning, scheduling cost reduction. Despite benefits, challenges cybersecurity management remain. This chapter explores synergy between these technologies, their benefits successful implementation in provides recommendations future research.

Language: Английский

Citations

0

Machine Learning Algorithms for Manufacturing Quality Assurance: A Systematic Review of Performance Metrics and Applications DOI Creative Commons
Ashfakul Karim Kausik, Adib Bin Rashid, Ramisha Fariha Baki

et al.

Array, Journal Year: 2025, Volume and Issue: unknown, P. 100393 - 100393

Published: March 1, 2025

Language: Английский

Citations

0