Exploring the Integration of Informed Machine Learning in Engineering Applications: A Comprehensive Review
Description
Integrating Artificial Intelligence (AI) and Machine Learning (ML) into mechanical engineering catalyzes a transformative shift within Industry 4.0, offering unprecedented opportunities for innovation, efficiency, and problem-solving. This paper explores the pivotal role of AI and ML in reshaping mechanical engineering practices, from predictive maintenance and design optimization to quality control and supply chain management. By leveraging sophisticated algorithms and vast datasets, AI and ML enable mechanical systems to achieve higher autonomy, performance, and reliability levels. However, adopting these technologies also presents challenges, including technical hurdles, ethical considerations, and the need for specialized knowledge. Through a series of case studies, the paper illustrates successful implementations of AI and ML in mechanical engineering projects, highlighting the benefits and addressing the limitations encountered. Furthermore, it discusses the evolving role of mechanical engineers in this new landscape, emphasizing the importance of continuous learning and interdisciplinary collaboration to harness the full potential of AI and ML in Industry 4.0. The paper concludes with a forward-looking perspective on future research directions, underscoring the critical role of ethical AI and the development of robust algorithms to navigate the complexities of real-world applications.