Distributed recycling and additive manufacturing (DRAM) offer a unique promise for obtaining a circular economy. To maintain or even enhance the value of common 3D printing feedstocks like polylactic acid (PLA) waste an approach to further incentivize prosumers to use recycled feedstocks is to provide something the market currently does not—custom filament colors. To enable prosumers to create custom colors from their own recycled 3D printing waste this article presents a new open-source software named SpecOptiBlend. Specifically, this study introduces a novel method for customizing color filaments by recycling waste 3D printing samples, thereby enhancing the capabilities of color 3D printing. Traditional 3D printing is limited by a narrow range of filament colors, and even multi-color printing heads can utilize only a limited number of colored filaments among the available options. The new approach here repurposes discarded prototypes and unused samples back into the printing cycle with desired colors, allowing for a broader spectrum of colors and gradients. This enables engineers and designers to create more intricate and functionally graded materials. To do this, waste plastics are quantified after processing for spectral reflectance, then Kubelka-Munk theory provides the initial estimate for color mixing. Three discrete optimization techniques are applied: Nelder-Mead, Limited-memory BFGS with bounds, and Sequential Least Squares Quadratic Programming. To determine the optimal method, assessment criteria include the application of root mean square (RMS) and the color difference (ΔE CIE-2000). Three case studies were conducted, and the Nelder-Mead method was found to provide an optimal balance between the precision of color differences and the RMS, essential for producing high-quality colors. This research has provided a free tool that will now enable prosumers to convert their plastic waste into specific custom colors to enable DRAM.

This paper proposes an optimal control strategy for process parameters aimed at addressing layup defects, achieving defect recognition and localization, and maintaining temperature stability on the actual layup surface. Infrared thermography is employed to identify defects during the carbon fiber layup process, and by integrating path control, the identification and localization of defects during the layup are effectively achieved. The minimum edge detection rate on both sides of a single layer reaches 94.3%, and the interlayer gap measurement is below 10%. Considering that temperature instability on the layup surface can lead to defects, a dynamic temperature control model has been established, and the infrared lamp’s temperature rise coefficient is determined, reducing defects caused by temperature fluctuations during the layup process. The results indicate that infrared thermography technology is feasible for the detection and reduction of defects in CFRP composites.

Here, wire-arc additive manufacturing (WAAM) is employed to manufacture a super-invar alloy thin-wall rectangular component. The microstructure is characterized by cellular sub-grains with different morphologies inside the epitaxially grown columnar crystals. Based on the finite element simulation results, the value of the G (the temperature gradient)/R (the solidification rate) during the deposition process is calculated as 1.59 × 108 K·s·m−2, which is associated with the columnar cellular microstructure. The transfer mode of the droplet during the WAAM is liquid bridge transition. The mechanical properties of specimens are anisotropic, and the longitudinal samples are better than transverse samples; the UTS is 398.8 MPa, the YS is 291.4 MPa, and the elongation is 40.8%. The coefficient of thermal expansion (CTE) is measured to be 0.265 × 10−6 K−1 in the range of 20 °C to 100 °C. The findings provide a reference for the fast fabrication of super-invar alloy components through WAAM, which promotes the applications of super-invar alloy in aerospace.

In the context of rapid technological advancement, haptic human-machine interfaces (HMIs) enhance user experience by simulating touch. Electroactive polymers (EAPs) are smart materials with high responsiveness, flexibility, and tunability, making them suitable for haptic actuators and feedback applications. This review examines the role of EAPs in haptic interaction, analyzing driving mechanisms, structural design, functional materials, fabrication methods, and practical applications. We also address challenges like performance limitations and manufacturing complexities, while discussing future trends in material optimization, structural design, and innovative driving strategies. This review serves as a valuable reference for future research and technological advancements in EAPs.

Laser-based powder bed fusion (LB-PBF) as-built material properties; residual stresses and final component shape are dependent on the thermal history of the printed part. Design and optimization of the deposited material thus far depends on experimental trial and error. A systematic approach based on model informed optimization is missing; mainly due to the computational expense of resolving the scan path and performing the required transient simulations. In this work; the heat conduction equation is reformulated to enable accelerated simulations. The laser position and operating conditions are read from the build file. The laser trajectory throughout the component is resolved providing 3D temperature evolution. Simple demonstration parts exhibiting both hot and cold regions are used to induce different metallurgical responses of the deposited IN718. Thermocouples are used to validate calculated temperatures. CALPHAD based calculations utilize temperature predictions to obtain localized phase concentrations and predict the distribution of thermophysical properties throughout the build. Results are compared with hardness measurements confirming the accuracy of the overall modelling chain.

The integration of automation, connectivity, and advanced analytics in manufacturing enhances productivity but also increases vulnerability to cyber threats including sensor attacks. Sensors—critical to automation—are particularly susceptible to false data injection attacks that can disrupt operations and lead to system failures. Despite advancements in prevention and detection methods, effective post-attack recovery remains an underexplored area—critical to minimizing operational downtime in manufacturing. The research addresses this gap by introducing a novel agent-based recovery modeling approach tailored for manufacturing systems. A reinforcement learning-driven recovery strategy is developed to restore operations efficiently after sensor attacks. The approach is evaluated through two distinct sensor attack scenarios. The recovery agent's performance is benchmarked against a PID controller using key metrics: downtime, throughput, and efficiency. Results demonstrate significant improvements by enhancing the resilience and security of manufacturing systems against sensor attacks.