Artificial Intelligence Challenges for Engineers

Artificial Intelligence (AI) is revolutionizing engineering disciplines by introducing unprecedented efficiencies, predictive capabilities, and automation across various fields. From structural analysis and material optimization to real-time monitoring and fault detection, AI-driven solutions are redefining how engineers approach problem-solving and innovation. These advancements enable more precise simulations, faster computations, and enhanced decision-making, ultimately leading to improved design, performance, and sustainability. However, despite its vast potential, engineers face numerous obstacles when incorporating AI into their workflows. The successful adoption of AI in engineering is hindered by several factors, including data limitations, ethical dilemmas, lack of interpretability, high computational costs, and regulatory challenges. Engineers must not only grapple with technical complexities but also navigate the broader implications of AI, such as ensuring fairness, accountability, and compliance with industry standards. This paper systematically explores these key challenges by reviewing recent case studies and emerging industry trends. By analyzing real-world applications and research developments, we highlight the most pressing issues that engineers encounter when deploying AI-based systems. Furthermore, we propose strategic solutions to address these difficulties, enabling responsible and effective AI integration in engineering fields. Addressing these challenges will be crucial in harnessing the full potential of AI while maintaining safety, reliability, and ethical integrity in engineering practices.

کلیدواژه ها:

Artificial Intelligence ، engineering disciplines ، predictive capabilities ، automation ، structural analysis ، material optimization ، real-time monitoring ، fault detection ، data limitations ، ethical dilemmas ، interpretability ، computational costs ، regulatory challenges ، fairness ، accountability ، compliance ، industry standards ، case studies ، industry trends ، real-world applications ، research developments ، AI-based systems ، strategic solutions ، AI integration ، safety ، reliability ، ethical integrity