Abstract
AbstractAdditive manufacturing (AM) is a revolutionary technology that enables production of intricate structures while minimizing material waste. However, its full potential has yet to be realized due to technical challenges such as the dependence of part quality on numerous process parameters, the vast number of design options, and the occurrence of defects. These complications may be magnified by the use of polymers and polymer composites due to their complex molecular structures, batch‐to‐batch variations, and changes in final part properties caused by small alterations in process settings and environmental conditions. Machine learning (ML), a branch of artificial intelligence, offers approaches to tackle these challenges and significantly reduce the experimental and computational time and expense. This review provides a comprehensive analysis of existing research on integrating ML techniques into polymer AM. It highlights the challenges involved in adopting ML in polymer AM, proposes potential solutions, and identifies areas for future research.
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