Jian Wang

To determine the motion characteristics of radioactive liquid sampling vials in pneumatic conveying systems in the nuclear industry, computational fluid dynamics (CFD) was employed to perform fluid–structure interaction (FSI) numerical simulations of the startup process of sampling vials under two—horizontal and vertical (upward)—conveying conditions. The motion of the sampling vial under various combinations of vacuum levels (−3 to −20 kPa) and gas volumetric flow rates (0.001 to 0.01 m³/s) was simulated to determine the transient velocity and pressure fields. The airflow velocity is lowest and the pressure is highest at the tail end of the sampling vial. For horizontal conveying, when the gas volumetric flow rate is insufficient, the vial tilts and becomes unstable due to gravity, preventing normal conveying; for vertical conveying, under high vacuum but low flow rates, the thrust pressure at the bottom of the vial is insufficient to overcome gravity, also resulting in conveying failure. Therefore, the gas volumetric flow rate has a greater impact on the conveying performance than the vacuum level. Under −5 kPa and 0.01 m³/s, the maximum velocity of the vial reached 20.39 m/s and 16.5 m/s, respectively, for horizontal and vertical conveying. These research findings can serve as a reference for optimizing the operating parameters and engineering design of the pneumatic conveying system for radioactive liquid sampling vials for the nuclear industry.

Injection molding is a foundational manufacturing process for the large-scale production of plastic parts and is also applied to rubber, metal, ceramic, glass, wood, and composites. Regular injection molding focuses on minimizing warpage and ensuring dimensional uniformity and high repeatability, as non-uniform material shrinkage in complex mold cavities causes undesirable deformations. In contrast, we reverse this traditional paradigm by leveraging controlled solidification shrinkage to induce purposeful, programmable deformation. We propose 4D injection molding, utilizing localized thermal activation and selective in-mold bonding to create non-uniform temperature and pressure distributions in spatiotemporal dimensions during the molding cycle. This enables the generation of complex, customized geometries from a single mold. Integrated with predictive modeling and multi-objective response optimization, our approach transforms warpage from a defect into a design feature, yielding functional parts with tunable shapes and performance characteristics. This method supports rapid, scalable, and cost-efficient mass customization across various materials. By reimagining shrinkage not as a limitation but as a design opportunity, the method represents a transformative shift in manufacturing science, with promising applications in biomedical, aerospace, and responsive consumer products.

Specific volume of polymer varies with pressure and temperature during injection molding and strongly correlates to local shrinkage and final warpage deformation of parts. Non‐destructive measurement of spatial specific volume distribution in finished injection‐molded parts remains challenging. This study presents a particle image visualization method to map specific volume distribution indirectly. Trace small amounts of carbon black (CB) were dispersed in polycarbonate (PC) as optical markers. The injection‐molded PC/CB flat plate as the standard sample was sectioned into 10 × 10 mm ² specimens for specific volume measurement and CB particle distribution analysis. The specific volume distribution was characterized and correlated with the digital‐image‐processed CB particle distribution. A strong linear correlation was established between CB particle area fraction and local specific volume. Pearson correlation coefficient ( r ) was 0.8988. The resulting regression model enables direct prediction of specific volume in other injection‐molded parts using only CB area fraction obtained via non‐destructive imaging. Validation on a complex‐geometry PC/0.01wt%CB fan‐shaped part yielded excellent agreement ( r = 0.9867, RMSE = 0.000244). This optical tracer method provides a practical, non‐destructive tool for characterizing specific volume heterogeneity in injection‐molded parts and can be extended to other transparent or semi‐transparent polymers and geometries.

Injection molding is one of the most widely employed manufacturing processes for the mass production of polymer products, owing to its high efficiency, reproducibility, and versatility

Insert injection molding is a widely adopted manufacturing process that integrates metal inserts into plastic components, enhancing the functionality and mechanical properties of the final product. Despite its advantages, the process often faces challenges related to the adhesion and bonding strength between metal and plastic. This chapter delves into applying surface engineering techniques to address these challenges. The adhesion between metal and plastic can be significantly improved by modifying the surface properties of metal inserts through various engineering methods such as surface treatments, coatings, and texturing. The chapter explores different surface engineering techniques and their mechanisms and presents their practical applications. Case studies will be presented to illustrate the effectiveness of these techniques in real-world scenarios, providing valuable insights for researchers and engineers in the field and promoting the advancement of hybrid material systems in manufacturing.

Injection molding is the most commonly used net-shape manufacturing process for materials. For polymers and some of their composites, injection molding can mass-produce high-precision parts with complex geometry at a very low cost. Injection-molded products have been widely used in various fields of human life, including daily necessities, baby care products, medical devices, electronic appliances, the automobile industry, aviation, aerospace, etc. With the continuous emergence of new polymeric and composite materials, more and more materials, including thermoplastics, thermosets, elastomers, engineering plastics, recycled plastics, and their composites with fibers and additives, are used in injection molding to meet the increasing demand for different plastic products. Injection molding involves the interdisciplinary intersection of materials, processes, molds, machines, and controls. This special issue is to explore the latest developments in injection molding of polymeric and composite materials and to advance the understanding of the potential benefits, challenges, and theoretical implications of injection molding in the mass manufacturing of polymeric products with various geometry and functions.The integration of new technologies and methodologies, as highlighted in the articles featured in this special issue, underscores the dynamic nature of research and development in this field. Here, we explore five notable contributions that advance our understanding and capabilities in injection molding, focusing on fiber orientation modeling, residual stress analysis, electrical conductivity, thermal warpage control, and multiscale feedback systems. The relevant polymeric and composite materials include glass fiber-reinforced polyamide composites, polycarbonate, polypropylene/polyamide 6/carbon black composites, polypropylene, and polyethylene. The relevant injection-molded products include Venturi tubes, goggles, dumbbell-shaped microparts, flat parts, and rectangular samples for X-ray scattering.Rienesl et al. introduced a novel automated metamodel optimization approach to calibrate fiber orientation models for short glass fiber-reinforced polyamide components. Using the Informed Isotropic (IISO) equation, the study demonstrated significant improvements in the accuracy of fiber orientation predictions, which are crucial for structural simulations and local mechanical property assessments. By applying their method to a Venturi tube geometry, the authors highlighted the potential for future advancements, such as higher CT resolution and Bayesian inference, to further refine simulation capabilities. This work lays the foundation for more precise and efficient injection molding simulations.Wang et al. presented an innovative combination of photoelasticity and digital image processing, termed PDIP, to qualitatively characterize residual stress in transparent injection-molded polycarbonate goggles. Validated through numerical simulations, the technique offers real-time residual stress identification and holds significant promise for industrial applications. The authors advocated for the integration of machine learning techniques to enhance stress analysis, emphasizing the importance of accurate stress characterization in improving the performance and reliability of molded components.Lei et al. explored the fabrication of electrically conductive microparts using polypropylene/polyamide 6/carbon black composites under high shear conditions in microinjection molding. Their study revealed the critical role of shear-induced phase separation in achieving a carbon black-rich network and, consequently, electrical conductivity. By correlating experimental findings with simulations, the research provided valuable insights into the design and production of functional microparts, highlighting the potential for scalable applications in electronics and sensor technologies.Wang et al. delved into the local thermal warpage deformation of polypropylene injection-molded flat parts. Using an experimental mold with nine local heating sections, the study investigated the nonlinear relationship between temperature distribution and warpage deformation. By employing neural network modeling, the authors achieved a prediction accuracy of up to 97%, showcasing the potential of machine learning in addressing complex thermal behaviors. Their findings underlined the utility of local heating techniques and neural networks in controlling deformation and optimizing part quality.Massano et al. proposed a paradigm shift in injection molding by integrating time-resolved X-ray scattering into the process. This innovative approach facilitates dynamic multiscale feedback, enabling precise control over operational parameters and laying the groundwork for digital twin technology. The study highlighted the potential of X-ray techniques to enhance pressure control, reduce material damage, and improve recyclability. By bridging traditional injection molding with Industry 4.0 concepts, the authors paved the way for a fully digitalized and optimized manufacturing process.Injection molding has long been a cornerstone of modern manufacturing, enabling the mass production of polymeric and composite materials with precision and efficiency. This well-established process is continually evolving to meet the demands of advanced materials, complex geometries, and sustainable practices. The articles in this special issue collectively demonstrate the transformative potential of innovative technologies in injection molding. From advanced simulation techniques and stress analysis methods to functional micropart fabrication and digital feedback systems, these contributions highlight the multifaceted advancements driving this field forward. As researchers and practitioners continue to address the challenges of material performance, process optimization, and sustainability, injection molding remains at the forefront of modern manufacturing...

This chapter aims to provide a comprehensive overview of real-time rheological measurement techniques and their applications in various fields. Rheology, as the study of the flow and deformation of materials, plays a vital role in understanding the mechanical properties and behavior of complex fluids and soft matter. With the advancement of technology, real-time rheological measurements have become increasingly crucial for in situ monitoring and control applications. This chapter discusses real-time rheological measurement techniques’ principles, instrumentation, and applications.