Volume 3, Number 1, May 2023 https://doi.org/10.47709/brilliance.v3i1.2170 32 Artificial Intelligence in Engineering Mohamed Mohamed Khaleel 1* Abdussalam Ali Ahmed 2 , Abdulgader Alsharif 3 1 Department of Aeronautical Engineering, Division of Automatic Control, College of Civil Aviation, Misrata, Libya 2 Mechanical Engineering Department, Bani Waleed University, Bani Waleed, Libya 3 Division of Electric Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor, Malaysia 1 lykhaleel@yahoo.co.uk, *Corresponding Author ABSTRACT Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communication, and renewable energy approaches that have been human-centred. Despite being in its early stages, AI-powered engineering applications can work with vague design parameters and resolve intricate engineering problems that cannot be tackled using traditional design, electrical, communication, and renewable energy methods. This article aims to shed light on the current progress and future research trends in AI applications in engineering concepts, focusing on the ramp-up period of the last 5 years. Various methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully evaluated from an engineering standpoint. AI-powered design studies have been reviewed and categorized for different design stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modeling. The review shows that there has been an increased interest in data-based design methods and explainable artificial intelligence in recent years. The use of AI methods in engineering applications has proven to be efficient, fast, accurate, and comprehensive, particularly with the use of deep learning methods and combinations that address situations where human capacity is inadequate. However, it is crucial to choose the appropriate AI method for an engineering problem to achieve successful results. Article History: Submitted: 24-02-2023 Accepted: 25-02-2023 Published: 13-03-2023 Keywords ARTIFICIAL INTELLIGENCE; MACHINE LEARNING; GENETIC ALGORITHM FUZZY LOGIC; ENGINEERING; Brilliance: Research of Artificial Intelligence is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). INTRODUCTION The conventional approach to product and engineering design processes is centred around human expertise, utilizing scientific, intuitive, experiential, and creative methods [1]. However, recent years have seen a shift in this approach with the incorporation of Artificial Intelligence (AI) in product and engineering design, similar to other engineering disciplines. AI refers to computer algorithms that mimic cognitive processes or activities observed in living organisms. AI can be utilized in tasks such as learning, understanding, estimating, problem-solving, suggestion, and decision-making in various disciplines, including the engineering design process [2]. The integration of AI-supported design techniques can effectively manage complex design operations, such as comparison, evaluation, and estimation, thereby allowing the designer to focus on tasks that require innovation and creativity. In this regard, AI methods facilitate shortened design processes, precise results, and reduced overall design costs. Moreover, its exceptional computational ability provides Artificial Intelligence (AI) with a competitive edge in performing such tasks, surpassing human capabilities. Among the plethora of Artificial Intelligence (AI) techniques, expert systems, fuzzy logic, artificial neural networks, and genetic algorithms have been the most frequently employed classical methods in the design evaluation and optimization processes over an extensive period [3]. Nevertheless, the application of contemporary data-driven methods, including machine learning and deep learning, in the design process has seen a significant rise in recent times. Several other Artificial Intelligence (AI) techniques can be employed in one or more phases of the design process, encompassing the likes of idea inspiration, concept generation, evaluation, optimization, and decision-making. In the realm of engineering design, the use of classical AI techniques, specifically fuzzy logic, genetic algorithm, and the artificial neural network has been thoroughly studied. The research findings have yielded favourable outcomes in utilizing these methods for addressing design problems. Similarly, the application of such conventional AI techniques has been scrutinized concerning the design and development of photovoltaic, wind, and fuel cells [4].