Survey on AI Applications for Product Quality Control and Predictive Maintenance in Industry 4.0

• Published in: Electronics (Basel) Vol. 13; no. 5; p. 976
• Main Authors: Andrianandrianina Johanesa, Tojo Valisoa, Equeter, Lucas, Mahmoudi, Sidi Ahmed
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2024
• Subjects: Artificial intelligence; Assembly lines; Automation; Big Data; Computational linguistics; Data analysis; Data collection; Deep learning; Defective products; Defects; Failure; Forecasts and trends; Industrial applications; Industry 4.0; Internet of Things; Language processing; Machine learning; Manufacturing; Natural language interfaces; Predictive control; Predictive maintenance; Product introduction; Product quality; Quality control; Sensors; Statistical process control; Support vector machines; Useful life; Germany
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics13050976

Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating artificial intelligence (AI) across various applications to enhance production processes. We cite two critical areas where AI can play a key role in industry: product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.