The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages of biopolymer production, enable the analysis of complex data generated throughout production, identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due to their reliance on variable bio-based feedstocks and complex processing conditions. This review systematically summarizes the current applications of ML techniques in biopolymer production, aiming to provide a comprehensive reference for future research while highlighting the potential of ML to enhance efficiency, reduce costs, and improve product quality. This review also shows the role of ML algorithms, including supervised, unsupervised, and deep learning algorithms, in optimizing biopolymer manufacturing processes.

The emergence of 3D and 4D printing has transformed the field of polymer composites, facilitating the fabrication of complex structures. As these manufacturing techniques continue to progress, the integration of machine learning (ML) is widely utilized to enhance aspects of these processes. This includes optimizing material properties, refining process parameters, predicting performance outcomes, and enabling real-time monitoring. This paper aims to provide an overview of the recent applications of ML in the 3D and 4D printing of polymer composites. By highlighting the intersection of these technologies, this paper seeks to identify existing trends and challenges, and outline future directions.

This paper surveys the application of machine learning in fiber composite manufacturing, highlighting its role in adaptive process control, defect detection, and real-time quality assurance. First, the need for ML in composite processing is highlighted, followed by a review of data-driven approaches—including predictive modeling, sensor fusion, and adaptive control—that address material heterogeneity and process variability. An in-depth analysis examines six case studies, among which are XPBD-based surrogates for RL-driven robotic draping, hyperspectral imaging (HSI) with U-Net segmentation for adhesion prediction, and CNN-driven surrogate optimization for variable-geometry forming. Building on these insights, a hybrid AI model architecture is proposed for natural-fiber composites, integrating a physics-informed GNN surrogate, a 3D Spectral-UNet for defect segmentation, and a cross-attention controller for closed-loop parameter adjustment. Validation on synthetic data—including visualizations of HSI segmentation, graph topologies, and controller action weights—demonstrates end-to-end operability. The discussion addresses interpretability, domain randomization, and sim-to-real transfer and highlights emerging trends such as physics-informed neural networks and digital twins. This paper concludes by outlining future challenges in small-data regimes and industrial scalability, thereby providing a comprehensive roadmap for ML-enabled composite manufacturing.

The paper introduces a computer vision methodology for detecting pitting corrosion in gas pipelines. To achieve this, a dataset comprising 576,000 images of pipelines with and without pitting corrosion was curated. A custom-designed and optimized convolutional neural network (CNN) was employed for binary classification, distinguishing between corroded and non-corroded images. This CNN architecture, despite having relatively few parameters compared to existing CNN classifiers, achieved a notably high classification accuracy of 98.44%. The proposed CNN outperformed many contemporary classifiers in its efficacy. By leveraging deep learning, this approach effectively eliminates the need for manual inspection of pipelines for pitting corrosion, thus streamlining what was previously a time-consuming and cost-ineffective process.

The integration of machine learning (ML) has begun to reshape the development of advanced polymeric materials used in technical textiles. Polymeric materials, with their versatile properties, are central to the performance of technical textiles across industries such as healthcare, aerospace, automotive, and construction. By utilizing ML and AI, researchers are now able to design and optimize polymers for specific applications more efficiently, predict their behavior under extreme conditions, and develop smart, responsive textiles that enhance functionality. This review highlights the transformative potential of ML in polymer-based textiles, enabling advancements in waste sorting (with classification accuracy of up to 100% for pure fibers), material design (predicting stiffness properties within 10% error), defect prediction (enabling proactive interventions in fabric production), and smart wearable systems (achieving response times as low as 192 ms for physiological monitoring). The integration of AI technologies drives sustainable innovation and enhances the functionality of textile products. Through case studies and examples, this review provides guidance for future research in the development of polymer-based technical textiles using AI and ML technologies.

This study delves into the mechanical characteristics of polyamide PA2200 components crafted using selective laser sintering (SLS) technology. Our primary objective is to analyze the tensile behavior of the components printed at various orientations, showing its response to diverse loading conditions. Finite element method (FEM) modeling was employed to analyze the tensile behavior of these details. The time determined for breaking the detail is 9 s. In addition we forecast key properties, such as tensile behavior and strength, using machine learning (ML) techniques, and the best models are for predicting relative elongation are KNeighborsRegressor and SVR.