A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment

Abstract

Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial intelligence (AI) has empowered smart manufacturing and digital transformation. AI enhances the migration towards industry 4.0 through AI-based decision-making by analyzing real-time data to optimize different processes such as production planning, predictive maintenance, quality control etc., thus guaranteeing reduced costs, high precision, efficiency and accuracy. This paper explores AI-driven smart manufacturing, revolutionizing traditional approaches and unlocking new possibilities throughout the major phases of the industrial equipment lifecycle. Through a comprehensive review, we delve into a wide range of AI techniques employed to tackle challenges such as optimizing process control, machining parameters, facilitating decision-making, and elevating maintenance strategies within the major phases of an industrial equipment lifecycle. These phases encompass design, manufacturing, maintenance, and recycling/retrofitting. As reported in the 2022 McKinsey Global Survey (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review), the adoption of AI has witnessed more than a two-fold increase since 2017. This has contributed to an increase in AI research within the last six years. Therefore, from a meticulous search of relevant electronic databases, we carefully selected and synthesized 42 articles spanning from 01 January 2017 to 20 May 2023 to highlight and review the most recent research, adhering to specific inclusion and exclusion criteria, and shedding light on the latest trends and popular AI techniques adopted by researchers. This includes AI techniques such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Bayesian Networks, Support Vector Machines (SVM) etc., which are extensively discussed in this paper. Additionally, we provide insights into the advantages (e.g., enhanced decision making) and challenges (e.g., AI integration with legacy systems due to technical complexities and compatibilities) of integrating AI across the major stages of industrial equipment operations. Strategically implementing AI techniques in each phase enables industries to achieve enhanced productivity, improved product quality, cost-effectiveness, and sustainability. This exploration of the potential of AI in smart manufacturing fosters agile and resilient processes, keeping industries at the forefront of technological advancements and harnessing the full potential of AI-driven solutions to improve manufacturing processes and products.

Texture and Flag Color Extraction in Backpropagation Neural Network Architecture

“…Conducted research on the classification of apple characteristics among three classifiers, CNN had the best performance with a detection accuracy of 98% for both apple varieties, followed by SVM and RF (Benmouna et al, 2022;Wu et al, 2023). This research shows that SIRI, coupled with machine learning algorithms, can be a new, versatile and effective modality for fruit defect detection (Elahi et al, 2023;Taner et al, 2024;Ukwuoma et al, 2022).…”

Graph convolution networks for social media trolls detection use deep feature extraction

“…Interpretation of the extracted text often employs machine learning and deep learning techniques, commonly used in natural language processing (NLP) tasks like sentiment detection. Various models, including Support Vector Machine (SVM) and Extreme Machine Learning (ELM), have been applied to this task, showing high success rates in classifying texts [37][38][39][40]. Research on the classification of online toxic comments has explored standard machine learning algorithms applied to datasets comprising different types of toxicity.…”

AI-Driven Real-time Quality Monitoring and Process Optimization for Enhanced Manufacturing Performance

“…Improved efficiency and productivity are among the foremost advantages of AI in manufacturing, as AI-powered systems can analyse vast amounts of data to optimize production processes and resource utilization. Predictive maintenance, enabled by AI algorithms, helps reduce downtime and extend the lifespan of equipment, leading to cost savings [22]. Quality control is another area where AI excels, ensuring that products meet stringent standards and reducing the likelihood of defects.…”