Integration of response surface methodology ( <scp>RSM</scp> ), machine learning ( <scp>ML</scp> ), and artificial intelligence ( <scp>AI</scp> ) for enhancing properties of polymeric nanocomposites‐A review
Yasir Raza, Hassan Raza, Arslan Ahmed, M. M. Quazi, Muhammad Jamshaid, Muhammad Tuoqeer Anwar, Muhammad Nasir Bashir, Talha Younas, Ali Turab Jafry, Manzoore Elahi M. Soudagar
Abstract
Abstract This review elucidates the amalgamation of machine learning (ML), artificial intelligence (AI), and response surface methodology (RSM) for the optimization of fabrication and the enhancement of the properties of polymeric nanocomposites. It analyzes recent accomplishments, methodologies, and future possibilities in this interdisciplinary field. Polymers and their nanocomposites are garnering attention because of their cost‐effectiveness, biodegradability, and non‐toxicity. Polymeric nanocomposites have been employed in several technical applications; nevertheless, their restricted mechanical, electrical, and thermal properties have impeded their extensive use. Numerous additives, including clay, fiber, and two‐dimensional materials such as graphene or MoS 2 , were extensively employed as nanofillers to enhance their qualities. The effects of filler concentration are thoroughly examined by conventional approaches; however, optimization via statistical techniques may be more suitable. The optimization method produces accurate results with a reduced number of tests. Diverse statistical techniques, including Taguchi and RSM, alongside ML algorithms, can be employed to ascertain the optimal filler concentration, type, fabrication method, characterization, and process parameters to enhance the properties, manufacturing, or efficiency of polymers or polymer‐based nanocomposites. The response surface methodology (RSM) produces superior results compared to Taguchi and conventional methods. Nonetheless, ML/AI can also be utilized to attain additional improvements in the requisite mechanical, thermal, electrical, and electrochemical properties. Recent advancements in the optimization of polymeric nanocomposites are emphasized, and the use of machine learning and artificial intelligence techniques is proposed for future progress. Highlights Summarized techniques to enhance polymeric nanocomposites properties. Presented process, production, and additive optimization of various polymers. Summarized statistical techniques and ML‐based nanocomposites efficiency. Future directions: ML and AI to improve polymeric nanocomposite properties.