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

AI-Supported Process Monitoring in Machining DOI Creative Commons
André Jaquemod, M. Reuter,

Marijana Palalić

et al.

Zeitschrift für wirtschaftlichen Fabrikbetrieb, Journal Year: 2025, Volume and Issue: 120(s1), P. 263 - 268

Published: March 20, 2025

Abstract In this study, AI-supported anomaly detection methods in the milling of inhomogeneous sample materials are investigated. To simplify data generation, targeted boreholes were introduced into homogeneous material samples. Process collected by means acceleration measurements on both workpiece and tool sides force side. Implementing feature extraction applying feature-based machine learning algorithms achieved precise classification reliable differentiation between drilled undrilled samples for process monitoring.

Language: Английский

Citations

0
Applying AI in Supporting Additive Manufacturing Machine Maintenance DOI Creative Commons
Luiz Fernando C. S. Durão, Florian Schmitt,

Júlio César R. A. Paz

et al.

Zeitschrift für wirtschaftlichen Fabrikbetrieb, Journal Year: 2025, Volume and Issue: 120(s1), P. 208 - 213

Published: March 20, 2025

Abstract Based on recent Artificial Intelligence advancements, this paper addresses implementing an AI-based maintenance strategy within a controlled production environment. The architecture employs real-time machine monitoring. Data is first processed locally for early fault detection and later cloud-based to support predictive functions. A Large Language Model trained with domain-specific knowledge provides operators basic instructions manage repetitive faults. implemented tested as source of experimental data.

Language: Английский

Citations

0
Reliable Process Tracking Under Incomplete Event Logs Using Timed Genetic-Inductive Process Mining DOI Creative Commons
Yutika Amelia Effendi, Minsoo Kim

Systems, Journal Year: 2025, Volume and Issue: 13(4), P. 229 - 229

Published: March 27, 2025

Process mining facilitates the discovery, conformance, and enhancement of business processes using event logs. However, incomplete logs complexities concurrent activities present significant challenges in achieving accurate process models that fulfill completeness condition required mining. This paper introduces a Timed Genetic-Inductive Mining (TGIPM) algorithm, novel approach integrates strengths Genetic (TGPM) Inductive (IM). TGPM extends traditional (GPM) by incorporating time-based analysis, while IM is widely recognized for producing sound precise models. For first time, these two algorithms are combined into unified framework to address both missing activity recovery structural correctness discovery. study evaluates scenarios: sequential approach, which executed independently sequentially, TGIPM where integrated framework. Experimental results real-world from health service Indonesia demonstrate achieves higher fitness, precision, generalization compared slightly compromising simplicity. Moreover, algorithm exhibits lower computational cost more effectively captures parallelism, making it particularly suitable large datasets. research underscores potential enhance outcomes, offering robust efficient discovery driving innovation across industries.

Language: Английский

Citations

0
Customization and personalization of large language models for engineering design DOI Creative Commons
Xingzhi Wang, Ang Liu, Dawen Zhang

et al.

CIRP Annals, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Enhancing manufacturing process accuracy: A multidisciplinary approach integrating computer vision, machine learning, and control systems DOI
K. Ramesh, Sandip Deshmukh, Tathagata Ray

et al.

Journal of Manufacturing Processes, Journal Year: 2025, Volume and Issue: 142, P. 453 - 467

Published: April 5, 2025

Language: Английский

Citations

0
Cluster-based prediction of chatter vibrations in milling operations DOI Open Access
Felix Finkeldey, Florian Wöste, Daniel Werner

et al.

Procedia CIRP, Journal Year: 2025, Volume and Issue: 133, P. 388 - 393

Published: Jan. 1, 2025

Language: Английский

Citations

0
Determination of the cutting forces from accelerations of a MEMS-based sensor-integrated milling tool DOI Open Access
P. Georgi,

Kamil Güzel,

Hans‐Christian Möhring

et al.

Procedia CIRP, Journal Year: 2025, Volume and Issue: 133, P. 20 - 25

Published: Jan. 1, 2025

Language: Английский

Citations

0
Augmented geometry assurance digital twin with physics-based incremental learning DOI Creative Commons
Roham Sadeghi Tabar, Rikard Söderberg, Dariusz Ceglarek

et al.

CIRP Annals, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Investigating the effects of machine learning generalization for enhancing accuracy in fast X-ray computed tomography for industrial metrology DOI Creative Commons
Filippo Zanini, Nicolò Bonato,

Diego Pentucci

et al.

CIRP Annals, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Vision-based robotic disassembly of aircraft engines with YOLO-SAM: a novel method for task orientation estimation DOI Creative Commons
Angelo Moroncelli,

Sylvain Populus,

Alessandra De Rossi

et al.

CIRP Annals, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0