Additive manufacturing has been employed in numerous areas owing to its advantages of fabricating complex geometries and creating less material waste. Nevertheless, parts manufactured by additive manufacturing processes tend to have poor surface quality and low dimensional accuracy. To overcome the limitations of additive manufacturing technologies, the favorable capabilities of subtractive manufacturing, i.e., high surface quality, can be integrated to form a hybrid process. A novel 6-axis hybrid additive-subtractive manufacturing process is proposed and developed in this study. The hybrid process is realized using a six degrees of freedom (DOF) robot arm, equipped with multiple changeable heads and an integrated manufacturing platform. Based on the obtained results from different case studies, the hybrid additive-subtractive process has shown to have potentials in reducing production time, fabricating parts with better surface quality by removing the staircase error, manufacturing high quality freeform surfaces through the dynamic adjustment of tool axis direction, and eliminating the need for support structure because of the 6-DOF flexibility.

The integration of shape memory materials into additive manufacturing has added a new dimension of time to conventional 3D printing and enabled innovative product designs with high tailorability and adaptability. To date, most studies on shape memory effects mainly adopt experimental approaches to characterize the material responsiveness to various stimulation conditions considering a single thermomechanical loading cycle. The information regarding the cyclic shape memory behaviors as well as the potential additive manufacturing-induced impacts on the achieved shape memory performance is limited. In this study, the shape memory behaviors of the stereolithography printed thermo-responsive structures are theoretically modeled by jointly considering the influences from both the printing process and the shape memory process. The cyclic shape memory effects are analytically characterized and experimentally validated using methacrylate copolymers under iterative thermomechanical loadings. It is also observed that the printing process parameters, including layer thickness and scan speed, have considerable impacts on the shape memory performance of the printed parts.

The utilization of advanced industrial informatics, such as industrial internet of things and cyber-physical system (CPS), provides enhanced situation awareness and resource controllability, which are essential for flexible real-time production scheduling and control (SC). Regardless of the belief that applying these advanced technologies under electricity demand response can help alleviate electricity demand–supply mismatches and eventually improve manufacturing sustainability, significant barriers have to be overcome first. Particularly, most existing real-time SC strategies remain limited to short-term scheduling and are unsuitable for finding the optimal schedule under demand response scheme, where a long-term production scheduling is often required to determine the energy consumption shift from peak to off-peak hours. Moreover, SC strategies ensuring the desired production throughput under dynamic electricity pricing and uncertainties in manufacturing environment are largely lacking. In this research, a knowledge-aided real-time demand response strategy for CPS-enabled manufacturing systems is proposed to address the above challenges. A knowledge-aided analytical model is first applied to generate a long-term production schedule to aid the real-time control under demand response. In addition, a real-time optimization model is developed to reduce electricity costs for CPS-enabled manufacturing systems under uncertainties.

Large-scale adoption of electric vehicles can reap significant energy and environmental benefits while also reducing reliance on fossil fuels. Nonetheless, accompanying the benefits of electric vehicles, several economic and ecological challenges arise from the production of Lithium-ion batteries. Remanufacturing is a promising end-of-life strategy and can lead to more sustainable Lithium-ion battery supply chains to support large-scale adoption of electric vehicles. Several factors will dictate the feasibility and effectiveness of remanufacturing, including economic viability, production capability, and battery demand and supply. Motivated by this, in this study, a state-of-the-art closed loop supply chain network model for Lithium-ion battery remanufacturing considering different quality levels of spent battery returns is proposed. An optimization model is developed to maximize the network profit and a sensitivity analysis is performed to determine the impact of several important model parameters on the profitability of the proposed supply chain network. This research will help stimulate the implementation of remanufacturing, promote economically and environmentally sustainable supply chain management in the electric vehicle battery industry, and support the transportation sector in reducing environmental burdens.

As a promising alternative to fossil fuels, cellulosic biofuel has obtained considerable interest due to its potential for mitigating global climate change and enhancing energy security. However, the widespread adoption of cellulosic biofuel is taking place in a slower pace than expected. One major challenge is that the cellulosic biofuel production is still highly energy-intensive. In fact, the energy contained in cellulosic biofuel is less than the energy required for its production. To address this issue, an analytical system-level energy model is proposed to characterize the fundamental relationships between total energy consumption and biofuel production parameters in cellulosic biofuel production systems. Furthermore, an optimization strategy based on Particle Swarm Optimization (PSO) is adopted to minimize the energy consumption of cellulosic biofuel production while maintaining the desired biofuel yield.