Startseite A review on industrial optimization approach in polymer matrix composites manufacturing
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A review on industrial optimization approach in polymer matrix composites manufacturing

  • Adryan Ang Seng Theng , Elammaran Jayamani EMAIL logo , Jeyanthi Subramanian , Vinoth Kumar Selvaraj , Shreya Viswanath , Ravi Sankar , Subramani Raja EMAIL logo und Maher Ali Rusho
Veröffentlicht/Copyright: 12. Juni 2025
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

Optimization is generally referred to as making the best and most effective use of materials. Optimization plays the most significant role when it comes to the field of research and development. Developing new products needs the best results with optimal time, cost, and resources. Especially in the case of industrial experiments, optimizing materials can save time, money, and manual power. So, it is necessary to have a comprehensive knowledge of various optimizing techniques currently adopted in industry. Hence this review covers the multiple types of polymer matrix composites manufacturing techniques currently adopted in industry, focusing on the manufacturing problems from the optimization perspective. Also, this review addresses some of the optimization approaches that current researchers attempt at every step of their research journey. Generally, optimization has to be coupled with the advancement of the manufacturing process that provides an ideal solution for cost reduction, energy consumption minimization, and improved competitiveness while assuring the end products’ quality. Stochastic algorithms such as Genetic Algorithms and Particle Swarm Optimization are examples of advanced statistical optimization techniques adopted by researchers in solving process parameters. Furthermore, experimental approaches such as the Taguchi Method and Response Surface Methodology for polymer matrix composite manufacturing optimization are also discussed in this review. Last but not least, a brief overview of how 3D printing can benefit the fabrication of polymer matrix composites is mentioned.


Corresponding authors: Elammaran Jayamani, Swinburne University of Technology Sarawak Campus, Jalan Simpang Tiga, Sarawak, Kuching, 93350, Malaysia, E-mail: ; and Subramani Raja, Center for Advanced Multidisciplinary Research and Innovation, Chennai Institute of Technology, Chennai, 600069, Tamil Nadu, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Conceptualization, AAST, RS, SR; methodology, AAST, SR; software, EJ, SR; validation, JS, SV, RS; investigation, RS; resources, SR; data curation, RS; writing – original draft, RS & SR; writing – review & editing, SV, VKS; visualization: VKS, JS; supervision, RS & SR; funding acquisition SR.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work is partially funded by Center for Advanced Multidisciplinary Research and Innovation. Chennai institute of technology, India, vide funding number CIT/CAMRI/2024/RP/001. This work was supported by Swinburne University of Technology (Sarawak Campus).

  7. Data availability: Not applicable.

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Received: 2024-09-13
Accepted: 2025-04-08
Published Online: 2025-06-12

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