Improving Polymer Composite Properties through Reinforcement Learning guided Prototyping a Novel Approach for Material Engineering

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Year : July 19, 2024 at 2:21 pm | [if 1553 equals=””] Volume : [else] Volume :[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : | Page : –

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Ms. Bindiya Jain, Mr Jeetandra Singh, Dr. Udit Mamodiya,

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  1. Associate Professor, Industrialist, Associate Professor FCE Poornima University,Jaipur, National Franchise Head, Cad desk, PTTS Pvt. Ltd, FET Poornima University, Jaipur Rajasthan, Uttar Pradesh, Rajasthan India, India, India

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Abstract

nInnovative approaches integrating reinforcement learning (RL) and machine learning (ML) into the fields of polymer composite prototyping and soft actuator manufacturing for applications. This new an algorithm utilizing RL optimizes polymer composite fabrication parameters to enhance material properties efficiently. By iteratively adjusting parameters based on predefined objectives, the RL agent guides the prototyping process, promising to revolutionize polymer composite engineering. A finest control method for locked loop control of Shape Memory Polymer (SMP) in compared to conventional control methods. Find recent advancements in ML built design of reinforced composite materials, improvements in time efficiency and prediction accuracy. The paper emphasizes the status of data hygiene and explores ML mixing in material, method range, and along with emerging digital tools and platforms. Resin-based stereolithography (SLA) commences with a detailed 3D digital model using CAD software. This model is divided into thin layers. A resin tank is filled with a photopolymer resin, & a platform is submerged. A UV laser cures every layer of resin, bonding it to the previous one. Post-processing comprises rinsing the object in a solvent with using a UV curing slot. Finishing touches like sanding, polishing, or painting were applied. These advancements allow for the precise fabrication of complex geometries and the fine-tuning of material properties, overcoming the limitations of traditional manufacturing methods. The combination of additive manufacturing with sophisticated optimization techniques enables the production of custom-designed actuators with enhanced performance and reliability. SLA produces detailed prototypes, complex models, and custom components with fine structures and smooth surfaces, widely second hand in automotive, aerospace, healthcare, and consumer goods industries. Utilizing additive manufacturing and Bayesian optimization, this model overcomes challenges in creating tradition shaped actuators and mitigates complex dynamic effects.

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Keywords: Reinforcement Machine Learning, Polymer Composites, Prototyping, Shape Memory Polymer, Additive Manufacturing.

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Polymer and Composites(jopc)]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Polymer and Composites(jopc)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article: Ms. Bindiya Jain, Mr Jeetandra Singh, Dr. Udit Mamodiya. Improving Polymer Composite Properties through Reinforcement Learning guided Prototyping a Novel Approach for Material Engineering. Journal of Polymer and Composites. July 16, 2024; ():-.

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How to cite this URL: Ms. Bindiya Jain, Mr Jeetandra Singh, Dr. Udit Mamodiya. Improving Polymer Composite Properties through Reinforcement Learning guided Prototyping a Novel Approach for Material Engineering. Journal of Polymer and Composites. July 16, 2024; ():-. Available from: https://journals.stmjournals.com/jopc/article=July 16, 2024/view=0

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[if 424 not_equal=””][else]Ahead of Print[/if 424] Open Access Review Article

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Journal of Polymer and Composites

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[if 344 not_equal=””]ISSN: 2321–2810[/if 344]

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Volume
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424]
Received May 15, 2024
Accepted June 26, 2024
Published July 16, 2024

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