Integrating Artificial Intelligence in Polymer Extrusion: Trends, Challenges, and Future Directions
Keywords:
revolutionize, AI, practices, requirement, encountersAbstract
Polymer extrusion, a fundamental method in plastics production, is seeing great benefits from the adoption of AI technologies. This review looks at current trends and challenges, as well as where we might be headed in the future, with the use of AI to improve polymer extrusion processes. Techniques driven by AI such as machine learning, deep learning, and even reinforcement learning bring many clear advantages when it comes to dealing with complex process parameters. They offer a way to handle the nonlinearity and high dimensionality that are intrinsic to many aspects of extrusion. In addition, these same techniques allow for fault detection and process monitoring in "smart" extrusion systems. One significant advantage of using AI is its predictive capability. For example, neural networks can be trained to act as predictive models for how an extrusion process will behave given certain input conditions (e.g., material properties, temperatures, pressures). These models can replace or supplement the highly simplified mathematical models that have traditionally been used to describe extrusion processes. Nonetheless, the application of AI in polymer extrusion encounters hurdles like insufficient data, a lack of domain specific expertise, and the requirement for clear models. This review examines how these challenges can be overcome to use AI for advancing sustainable practices in polymer extrusion. Overall, this article fills a few gaps in the current research and provides a thorough understanding of how AI is beginning to "revolutionize" polymer extrusion.
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