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Bayesian stability and force modeling for uncertain machining processes
Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.
Derivation of human toxicokinetic parameters and internal threshold of toxicological concern for tenuazonic acid through a human intervention trial and hierarchical Bayesian population modeling
Tenuazonic acid (TeA), a mycotoxin produced by Alternaria alternata, contaminates various food commodities and is known to cause acute and chronic health effects. However, the lack of human toxicokinetic (TK) data and the reliance on external exposure estimates have stalled a comprehensive risk assessment for TeA.
Towards next-gen smart manufacturing systems: the explainability revolution
The paper shares the author’s perspectives on the role of explainable-AI in the evolving landscape of AI-driven smart manufacturing decisions. First, critical perspectives on the reasons for the slow adoption of explainable-AI in manufacturing are shared, leading to a discussion on its role and relevance in inspiring scientific understanding and discoveries towards achieving complete autonomy. Finally, to standardize explainability quantification, a new Transparency–Cohesion–Comprehensibility (TCC) evaluation framework is proposed and demonstrated.
Feasibility of meeting future battery demand via domestic cell production in Europe
Batteries are critical to mitigate global warming, with battery electric vehicles as the backbone of low-carbon transport and the main driver of advances and demand for battery technology. However, the future demand and production of batteries remain uncertain, while the ambition to strengthen national capabilities and self-sufficiency is gaining momentum. In this study, leveraging probabilistic modelling, we assessed Europe’s capability to meet its future demand for high-energy batteries via domestic cell production. We found that demand in Europe is likely to exceed 1.0 TWh yr−1 by 2030 and thereby outpace domestic production, with production required to grow at highly ambitious growth rates of 31–68% yr−1. European production is very likely to cover at least 50–60% of the domestic demand by 2030, while 90% self-sufficiency seems feasible but far from certain. Thus, domestic production shortfalls are more likely than not. To support Europe’s battery prospects, stakeholders must accelerate the materialization of production capacities and reckon with demand growth post-2030, with reliable industrial policies supporting Europe’s competitiveness.
First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes
MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.
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