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March 4, 2024Electronics44 citationsOpen Access

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

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TJTojo Valisoa Andrianandrianina JohanesaLELucas EqueterSMSidi Ahmed Mahmoudi

Key Points

  • Enhancing product quality control and predictive maintenance through AI applications is crucial for Industry 4.0.
  • The survey presents key metrics from experiments using machine learning models on two datasets to analyze application effectiveness.
  • Assessment involved deploying various AI models to improve data analysis, enabling better decision-making in production processes in a modern industrial setting. This includes identifying optimal deployment strategies for industry needs and challenges in integration and adaptation of AI technologies to existing frameworks, enhancing overall operational efficiency and reliability.

Abstract

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.

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Cite This Study

Johanesa et al. (2024) studied this question.

synapsesocial.com/papers/68e75b3db6db6435876d2f36https://doi.org/10.3390/electronics13050976
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