Review of artificial intelligence applications in engineering design perspective

Having passed the primitive phases and starting to revolutionize many different fields in some way, artificial intelligence is on its way to becoming a disruptive technology. It is also foreseen to totally change human -centred traditional engineering design approaches. Although still in the early phases, AI-powered engineering applications enable them to work with ambiguous design parameters and solve complex engineering problems, not otherwise possible with traditional design methods. This work attempts to shine a light on current progress and future research trends in AI applications in design/engineering design concepts, covering the last 15 years which is the ramp-up period for AI. Methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully examined from an engineering design perspective. AI-powered design studies have been categorized and critically reviewed for various design stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modelling. As an overview result of this review, we can confidently say that the interest in data-based design methods and Explainable Artificial Intelligence (XAI) has increased in recent years. Furthermore, the use of AI methods in engineering design applications helps to obtain efficient, fast, accurate, and comprehensive results. Especially with deep learning methods and combinations, situations where human capacity is insufficient can be addressed efficiently. However, choosing the right AI method for a design problem under consideration is significantly important for such successful results. Hence, we have given an outline perspective on choosing the right AI method based on the literature outcomes for design problems.

Automated summary

Having surpassed the initial stages, artificial intelligence is gradually revolutionizing multiple fields and is predicted to transform human-centric traditional engineering design approaches. In its early phases, AI-powered engineering applications enable the solving of complex engineering problems and allow for the handling of ambiguous design parameters, which are not attainable through traditional methods. This study provides an overview of the progress and future research trends in AI applications in design/engineering design concepts over the past 15 years. Various methods, including machine learning, genetic algorithms, and fuzzy logic, are examined in the context of engineering design. The categorized and critical review of AI-powered design studies covers stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modeling. The increasing interest in data-driven design methods and Explainable Artificial Intelligence (XAI) is highlighted. The use of AI methods in engineering design applications leads to efficient, fast, accurate, and comprehensive results, especially when combined with deep learning methods to address situations that exceed human capacity. However, the selection of the appropriate AI method for a design problem is crucial for successful outcomes, and this study offers an outline for making such choices based on literature outcomes.