Collaborative Robust Multi-Objective Optimization of Electrode Air-Flotation Drying Under Equipment Aging Uncertainty

In the wet-process stage of lithium-ion battery manufacturing, double-sided coating combined with air-flotation drying can reduce repeated drying operations, thereby helping improve production throughput. However, the requirements of air-flotation drying for equipment stability, together with the coupling among process parameters such as temperature, air velocity, and tension, substantially increase the difficulty of process-parameter calibration. As critical components degrade over time, deviations arise between nominal process parameters and actual operating conditions, introducing non-negligible uncertainty and further complicating parameter recalibration. This paper proposes a collaborative robust multi-objective optimization algorithm to obtain stable and reliable process-parameter combinations under limited computational resources. Specifically, multi-objective optimization models are first established. Then, the operating condition of new equipment is approximately formulated as an undisturbed auxiliary optimization problem, whereas the operating condition of aged equipment with parameter perturbations is formulated as a robust optimization problem; surrogate models are constructed for both problems. Finally, search information from the auxiliary problem is used to guide the evolution of the robust optimization problem, thereby improving its optimization efficiency. Experimental results demonstrate that the proposed algorithm can obtain robust Pareto solutions with favorable convergence and diversity while consuming fewer resources, providing engineers with reliable references for selecting suitable process parameters.