Abstract The demand for cost-effective and sustainable battery manufacturing promotes interest in solvent-free electrode processing. Conventional slurry-based wet coating requires substantial energy for solvent evaporation and recovery, imposing process complexity and environmental burdens. By eliminating toxic organic solvents, dry electrode processing based on polytetrafluoroethylene (PTFE) fibrillation offers a sustainable alternative. Without solvents, structural formation depends on mechanical processes rather than liquid-phase assembly. This review examines the structural evolution of freestanding dry electrode films during roll-to-roll processing from a mechanical perspective. Force transmission mechanisms across mixing, kneading, grinding, and rolling are analyzed to establish how shear-driven PTFE fibrillation and compression-induced densification govern microstructure formation. Rolling-induced stress controls PTFE network formation and electrode mesostructure, which determines mechanical and electrochemical properties. Comparisons with slurry electrodes reveal fundamental structural differences in network formation. Also, we identify mechanical challenges for industrial scalability, including particle fracture during consolidation, tribological wear at processing interfaces, and process stability constraints. Extension to all-solid-state and lithium-sulfur batteries demonstrates cross-chemistry applicability of these mechanical design principles for sustainable manufacturing.

Laser Powder Bed Fusion (LPBF) enables the fabrication of complex soft magnetic components but often involves high energy input and trade-offs between magnetic and mechanical performance. This study presents an energy-aware optimization framework for LPBF processing of Fe–50wt%Ni soft magnetic alloys. Two L9 orthogonal experiments were conducted, and machine learning models were trained to predict magnetic permeability and ultimate tensile strength (UTS) from four key process parameters. The best condition, 3500 ppm oxygen, 300 smm/s scan speed, 250 W laser power, and 0.09 mm hatch distance, achieved relative permeabilities of 416.48, 400.58, 332.39, and 231.46 at 50, 200, 400, and 800 Hz, respectively, and a UTS of 504.11 MPa. A recommendation system integrating ensemble learning with NSGA-II was developed to minimize energy consumption while meeting user-specified property targets. A case study demonstrated a 47.2% reduction in energy use with about 5% loss in material performance. The key contribution lies in optimizing LPBF parameters based on specified property requirements while minimizing energy consumption rather than ideal trade-off balance, offering a practical strategy for sustainable additive manufacturing of soft magnetic alloys.

Abstract Ultra-high molecular weight polyethylene (UHMWPE) is widely used in biomedical applications due to its excellent wear resistance, low friction, and high impact strength than other polymers. However, its challenging machinability especially under dry machining stemming from poor thermal conductivity, viscoelasticity, and susceptibility to burr formation necessitates advanced process control to ensure high surface quality and productivity. This study proposes a data-driven, multi-objective optimization framework for UHMWPE high speed slot milling, aimed at minimizing surface roughness and burr height while maintaining high material removal rates (MRR). The methodology integrates principal component analysis (PCA) for constructing a unified surface roughness index (SRI) from twelve measured topographic parameters, gaussian process regression (GPR) for surrogate modeling of process responses, non-dominated sorting genetic algorithm II (NSGA-II) for pareto-based optimization, and the technique for order preference by similarity to ideal solution (TOPSIS) for decision support. Milling experiments were conducted under three cooling strategies including dry, super critical CO 2 (scCO₂), and scCO₂+ bio MQL, with four cutting speeds and four feed per tooth levels. Results demonstrate that the PCA-based dimensionality reduction captured over 87% of the surface texture variance, enabling efficient and interpretable optimization. Pareto front analysis revealed that scCO₂+MQL dominated the low-SRI and low-burr region. Under this condition, the process achieved superior surface quality with minimum SRI of − 2.57 and a burr height as low as 0.03 mm, while sustaining high MRR. TOPSIS identified the optimal condition (f z = 0.086 mm/tooth, v c = 970 m/min), which was experimentally validated with a prediction error below 20%.