Material defects (MD) represent a primary challenge affecting product performance and giving rise to safety issues in related products. The rapid and accurate identification and localization of MD constitute crucial research endeavors in addressing contemporary challenges associated with MD. In recent years, propelled by the swift advancement of machine learning (ML) technologies, particularly exemplified by deep learning, ML has swiftly emerged as the core technology and a prominent research direction for material defect detection (MDD). Through a comprehensive review of the latest literature, we systematically survey the ML techniques applied in MDD into five categories: unsupervised learning, supervised learning, semi-supervised learning, reinforcement learning, and generative learning. We provide a detailed analysis of the main principles and techniques used, together with the advantages and potential challenges associated with these techniques. Furthermore, the survey focuses on the techniques for defect detection in composite materials, which are important types of materials enjoying increasingly wide application in various industries such as aerospace, automotive, construction, and renewable energy. Finally, the survey explores potential future directions in MDD utilizing ML technologies. This survey consolidates ML-based MDD literature and provides a foundation for future research and practice.

Mycelium-based composites (MBCs) are an emerging category of cost-effective and environmentally sustainable materials that are attracting significant research and commercial interest across various industries, including construction, manufacturing, agriculture, and biomedicine. These materials harness the natural growth of fungi as a low-energy bio-fabrication method, converting abundant agricultural by-products and waste into sustainable alternatives to energy-intensive synthetic construction materials. Their affordability and eco-friendly characteristics make them attractive for both research and commercialisation. Currently, mycelium-based foams and sandwich composites are being actively developed for applications in construction. These materials offer exceptional thermal insulation, excellent acoustic absorption, and superior fire safety compared to conventional building materials like synthetic foams and engineered wood. As a result, MBCs show great potential for applications in thermal and acoustic insulation. However, their foam-like mechanical properties, high water absorption, and limited documentation of material properties restrict their use to non- or semi-structural roles, such as insulation, panelling, and furniture. This paper presents a comprehensive review of the fabrication process and the factors affecting the production and performance properties of MBCs. It addresses key elements such as fungal species selection, substrate choice, optimal growth conditions, dehydration methods, post-processing techniques, mechanical and physical properties, termite resistance, cost comparison, and life cycle assessment.

Hollow box pultruded fibre-reinforced polymers (PFRP) profiles are increasingly used as structural elements in many structural applications due to their cost-effective manufacturing process, excellent mechanical properties-to-weight ratios, and superior corrosion resistance. Despite the extensive usage of PFRP profiles, there is still a lack of knowledge in the design for manufacturing against local buckling on the structural level. In this review, the local buckling of open-section (I, C, Z, L, T shapes) and closed-section (box) FRP structural shapes was systematically compared. The local buckling is influenced by the unique stresses distribution of each section of the profile shapes. This article reviews the related design parameters to identify the research gaps in order to expand the current design standards and manuals of hollow box PFRP profiles and to broaden their applications in civil structures. Unlike open-section profiles, it was found that local buckling can be avoided for box profiles if the geometric parameters are optimised. The identified research gaps include the effect of the corner (flange-web junction) radius on the local buckling of hollow box PFRP profiles and the interactions between the layup properties, the flange-web slenderness, and the corner geometry (inner and outer corner radii). More research is still needed to address the critical design parameters of layup and geometry controlling the local buckling of pulwound box FRP profiles and quantify their relative contribution and interactions. Considering these interactions can facilitate economic structural designs and guidelines for these profiles, eliminate any conservative assumptions, and update the current design charts and standards.

Thermoset polymer composites have increased in use across multiple industries, with recent applications consisting of high-complexity and large-scale parts. As applications expand, the emphasis on accurate process-monitoring techniques has increased, with a variety of in situ cure-monitoring sensors being investigated by various research teams. To date, a wide range of data analysis techniques have been used to correlate data collected from thermocouple, dielectric, ultrasonic, and fibre-optic sensors to information on the material cure state. The methods used in existing publications have not been explicitly differentiated between, nor have they been directly compared. This paper provides a critical review of the different data collection and cure state correlation methods for these sensor types. The review includes details of the relevant sensor configurations and governing equations, material combinations, data verification techniques, identified potential research gaps, and areas of improvement. A wide range of both qualitative and quantitative analysis methods are discussed for each sensing technology. Critical analysis is provided on the capability and limitations of these methods to directly identify cure state information for the materials under investigation. This paper aims to provide the reader with sufficient background on available analysis techniques to assist in selecting the most appropriate method for the application.