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The future of intelligent manufacturing machines involves autonomous selection of process parameters to maximize productivity while maintaining quality within specified constraints. To effectively optimize process parameters, these machines need to adapt to existing uncertainties in the physical system. This paper proposes a novel framework and methodology for feedrate optimization that is based on a physics-informed data-driven digital twin with quantified uncertainty. The servo dynamics are modeled using a digital twin, which incorporates the known uncertainty in the physics-based models and predicts the distribution of contour error using a data-driven model that learns the unknown uncertainty on-the-fly by sensor measurements. Using the quantified uncertainty, the proposed feedrate optimization maximizes productivity while maintaining quality under desired servo error constraints and stringency (i.e., the tolerance for constraint violation under uncertainty) using a model predictive control framework. Experimental results obtained using a 3-axis desktop CNC machine tool and a desktop 3D printer demonstrate significant cycle time reductions of up to 38% and 17% respectively, while staying close to the error tolerances compared to the existing methods. 

Currently, there is considerable interest in developing advanced rechargeable batteries that boast efficient distribution of electricity and economic feasibility for use in large-scale energy storage systems. Rechargeable aqueous zinc batteries are promising alternatives to lithium-ion batteries in terms of rate performance, cost, and safety. In this investigation, we employ Cu₃(HHTP)₂, a two-dimensional (2D) conductive metal-organic framework (MOF) with large one-dimensional channels, as a zinc battery cathode. Owing to its unique structure, hydrated Zn²⁺ions which are inserted directly into the host structure, Cu₃(HHTP)₂, allow high diffusion rate and low interfacial resistance which enable the Cu₃(HHTP)₂cathode to follow the intercalation pseudocapacitance mechanism. Cu₃(HHTP)₂exhibits a high reversible capacity of 228 mAh g⁻¹at 50 mA g⁻¹. At a high current density of 4000 mA g⁻¹(~18 C), 75.0% of the initial capacity is maintained after 500 cycles. These results provide key insights into high-performance, 2D conductive MOF designs for battery electrodes.