福特汽车公司

PurposeThis study explores how initial exposure to immersive spatial computing experiences using AR headsets generates lasting inspiration and shapes consumers expected long-term life consequences (i.e., enhancement of reality, perceived substitutability and social impact).Design/methodology/approachThe study uses a time-lagged research design based on 148 first-time users of spatial computing devices (AR headsets). Respondents were interviewed once shortly after being exposed to AR and a few days later. Data is analyzed using partial least squares structural equation modeling (PLS-SEM).FindingsUsers' immediate "inspired-by" experiences predict increased "inspired-to" intentions days later. Such inspiration translates into anticipated consequences such as virtually customizing their physical environments, substituting physical products with AR content, and influencing social relationships with other users.Research limitations/implicationsThe current research focuses on positive life outcomes for consumers. However, the ubiquitous and pervasive use of AR may also lead to negative, undesired effects.Practical implicationsThe study demonstrates that AR experiences can produce detectable effects long after initial exposure and underscores that AR adoption results from the synergy of hardware and content, providing insights for future research in immersive spatial computing technologies.Originality/valueThe current research is one of the first to study AR users over time. Drawing on inspiration theory, findings show that an initial exposure to spatial computing through AR can have lasting effects when consumers think about how these technologies could impact their lives.

Anomaly detection in rotating machinery is essential for reliable industrial operations, yet building accurate detectors remains difficult when fault labels in a new domain are scarce. Although transfer anomaly detection has been increasingly studied, most methods do not explicitly exploit the characteristic fault-frequency structure-i.e., the fact that only specific orders/frequency components are strongly diagnostic of emerging faults. Here, we extend our prior key-order transfer framework. In this framework, a key order is a spectral feature-weight vector that upweights diagnostically informative orders and down-weights less relevant components when computing the anomaly score, and we adapt it to the realistic regime in which a small (but growing) number of labeled target anomalies becomes available over time. We estimate key orders in both source and target domains and fuse them using uncertainty-aware Bayesian combination as well as robust heuristic rules. Experiments on automotive transmission vibration data from two manufacturing sites show that adaptive fusion consistently outperforms source-only or target-only weighting in label-scarce settings. Overall, these results highlight the value of uncertainty-aware transfer for practical industrial anomaly detection under domain shift.

In this study, we describe Ford’s practices and propose three industry-based frameworks for supply chain digital twin (SCDT) design and implementation at scale. First, a generalized three-layer framework for the design of SCDTs based on Ford's approach is developed. The layers are intracompany, Tier-1 network, and deep-tier network, classified based on data visibility. We describe how digital twins can enhance operational performance and be utilized for resilience stress testing. Second, generalized frameworks of SCDT implementation are shown composed of two dimensions, i.e., implementation scale and implementation scope. The three-stage implementation scale framework proposes a roadmap for transition from data-driven organizations to digital twin-driven management systems. The four-level implementation scope framework encompasses product, process, organization, and extended network levels, with a focus on the key role of the data analytics department in deploying SCDTs. We then generalize four fundamental principles for SCDTs: (i): object-driven and data-driven design and adaptation, (ii) visibility as the central angle of digital twin design and technology, (iii) digital twins are integrators of data and knowledge, and (iv) SCDT continuous adaptation. To the best of our knowledge, our paper is the first in the literature to report on the design and deployment of an SCDT at scale, which can be useful for academics and practitioners alike. We conclude that a properly developed SCDT can enable strategic and operational performance improvements, end-to-end visibility, agentic AI integration in decision-making, and supply chain stress testing, as well as create a new approach to managing the supply chain.

The objective of this paper is to develop a physics-informed machine learning methodology for parametric modeling of permanent magnet synchronous machines (PMSMs). A deep neural network is trained to compute the magnetic field as a function of spatial coordinates and machine parameters, while enforcing physical properties such as Dirichlet boundary conditions and periodicities. Leveraging a DeepONet architecture, the network is trained in a data-free fashion by minimizing a physics-informed functional using a mesh-based coenergy evaluation. The methodology is demonstrated on a 15-dimensional PMSM problem, with model accuracy validated by comparing predictions with finite element analysis (FEA) results, focusing on coenergy, average torque, and total core loss. Computational cost is also assessed relative to FEA.

Battery recycling is essential for mitigating the resource and environmental impacts of the electric vehicle industry. However, real-world assessments of battery recycling at the industrial scale remain limited. Here, we present the most comprehensive life-cycle assessment to date using operational data from 46 recycling facilities in China, covering approximately 50% of the global capacity in 2023. We evaluate multiple recycling outputs, black mass, metal salts, precursors, and cathodes and reveal that new hydrometallurgical technologies for direct precursor and cathode recovery could reduce carbon emissions by 61% compared to mining production due to skipping multiple extraction steps. Real-world recycling often requires blending with virgin materials to maintain the targeted Ni-Co-Mn ratio for recycling the nickel-cobalt-manganese (NCM) precursor or cathode due to market preference for high-nickel chemistries. Our results show that, compared with virgin production, fully recycled cathode materials can reduce pack-level carbon footprint levels of lithium-iron phosphate (LFP) batteries by 11% (2-14%), significantly greater than previous estimates, and by 24% (12-27%) for NCM811 batteries. Coupled with dynamic fleet modeling, battery recycling is identified to cumulatively avoid 147-433 million tons of CO2 emissions in China by 2050. These insights offer important guidance for carbon footprint regulations and the advancement of circular economy practices globally.