While accurate mapping of strain distribution is crucial for assessing stress concentration and estimating fatigue life in engineering applications, conventional strain sensor arrays face a great challenge in balancing sensitivity and sensing density for effective strain mapping. In this study, we present a Fowler-Nordheim tunneling effect of monodispersed spiky carbon nanosphere array on polydimethylsiloxane as strain sensor arrays to achieve a sensitivity up to 70,000, a sensing density of 100 pixel cm⁻², and logarithmic linearity over 99% within a wide strain range of 0% to 60%. The highly ordered assembly of spiky carbon nanospheres in each unit also ensures high inter-unit consistency (standard deviation ≤3.82%). Furthermore, this sensor array can conformally cover diverse surfaces, enabling accurate acquisition of strain distributions. The sensing array offers a convenient approach for mapping strain fields in various applications such as flexible electronics, soft robotics, biomechanics, and structure health monitoring.

Machining complex thin-wall components (such as compressor disks and casings in aircraft engines) has been a challenging task because workpiece deformations and vibrations not only compromise the surface integrity but also induce residual stresses in the final products. This paper offers a physics-based method that accounts for the damping effects and external loads for reconstructing the dynamic displacement and strain fields of a thin-wall workpiece in real-time with non-contact displacement measurements during machining. Given that part dynamic behaviors can be characterized by superposition of mode shapes, the time-varying displacement and strain fields are reconstructed with modal coefficients that are updated in real time using in situ measurements. The reconstruction method has been numerically verified with finite element analyses with the sensor locations optimized using a genetic algorithm; both static and dynamic field reconstructions are analyzed. Tradeoffs between the number of sensors and the reconstruction efficiency in terms of computation time and error are discussed. The method has been evaluated experimentally on a lathe machine testbed, where the dynamics of the distributed physical fields have been successfully captured and analyzed, demonstrating its practicality as a real-time tool for continuously monitoring the displacement and strain distributions across a disk workpiece during machining.

Motivated by the need to investigate thermal effects on the deflection and stresses in a thin-wall workpiece during machining, the thermal problem is modeled with an axisymmetric input to emulate the heat generated at the tool-workpiece interface in a turning process. Using a compressor disk as an illustrative example, the boundary value problem is formulated with a plate model where the perimetric edge is clamped and insulated, and the upper and lower surfaces are subjected to heat convection. The closed form solution of temperature distribution is obtained via Green’s function method, based on which the thermal deflection/stresses are obtained in serial forms from the plate constitutive relations. The obtained solutions have been numerically verified with finite-element analysis (FEA), where simulations have been performed for three different materials with discrepant thermomechanical properties to study the thermal effects on the induced deflection and stresses. The analytical result is justified by its good agreement with FEA and its time efficiency in computation offers advantages in potential real-time application to manufacturing process monitoring.

This paper presents an active control method, consisting of an adaptive sliding-mode controller (ASMC) and a displacement field reconstruction (DFR) method, for chatter suppression in turning of thin-walled workpieces (such as compressor disks and casings in aircraft engines) where low workpiece stiffness renders machining with potential regenerative chatter. Due to the presence of multi-modal dynamics, variant modal parameters, and measurement difficulties, active chatter control of thin plate turning has been challenging. Unlike existing controls based on a lumped-parameter single degree-of-freedom cutting model, a distributed-parameter dynamic model of a rotating thin plate with multiple vibration modes is used to analyze the machining stability with the designed controller. Moreover, model parameters of the plate are not needed to construct the controller. The DFR is employed to capture the plate dynamic behavior for feedback to the ASMC during turning, overcoming the long existing difficulties to measure plate vibration at the cutting point. A fast tool servo is utilized in the control implementation. Theoretical analyses, numerical simulations, and experimental evaluation on a lathe demonstrate that chatter in thin plate turning can be effectively attenuated with the proposed active control method.

Wearable devices developed with flexible electronics have great potential applications for human health monitoring and motion sensing. Although material softness and structural flexibility provide a deformable human–machine interface to adapt to joint bending or tissue stretching/compression, flexible sensors are inconvenient in practical uses as they usually require calibration every time they are installed. This article presents an approach to design and fabricate a flexible curvature sensor to measure human articular movements for amphibious applications. This flexible sensor employs the capacitive sensing principle, where the dielectric layer and electrodes are made from the polyurethane resin and eutectic gallium-indium (EGaIn) liquid metal; and the fabrication process is implemented with shape deposition molding for batch production. The sensing method for articular rotation angles employs the Euler beam model to make the sensor reusable after one-time calibration by compensating for the unpredicted manual installation error. The illustrative application to ankle sensing in amphibious gaits shows that the root-mean-square error is within 5° for different walking speeds (0.7–1.1 m/s) in treadmill tests and the maximum error is within 3° for underwater sensing with quasi-static measurements. It is expected that the proposed waterproof flexible sensor can push the boundaries of wearable robotics, human locomotion, as well as their related applications.

Background: Temporal bone computed tomography (CT) helps diagnose chronic otitis media (COM). However, its interpretation requires training and expertise. Artificial intelligence (AI) can help clinicians evaluate COM through CT scans, but existing models lack transparency and may not fully leverage multidimensional diagnostic information. Objective: We aimed to develop an explainable AI system based on 3D convolutional neural networks (CNNs) for automatic CT-based evaluation of COM. Methods: Temporal bone CT scans were retrospectively obtained from patients operated for COM between December 2015 and July 2021 at 2 independent institutes. A region of interest encompassing the middle ear was automatically segmented, and 3D CNNs were subsequently trained to identify pathological ears and cholesteatoma. An ablation study was performed to refine model architecture. Benchmark tests were conducted against a baseline 2D model and 7 clinical experts. Model performance was measured through cross-validation and external validation. Heat maps, generated using Gradient-Weighted Class Activation Mapping, were used to highlight critical decision-making regions. Finally, the AI system was assessed with a prospective cohort to aid clinicians in preoperative COM assessment. Results: Internal and external data sets contained 1661 and 108 patients (3153 and 211 eligible ears), respectively. The 3D model exhibited decent performance with mean areas under the receiver operating characteristic curves of 0.96 (SD 0.01) and 0.93 (SD 0.01), and mean accuracies of 0.878 (SD 0.017) and 0.843 (SD 0.015), respectively, for detecting pathological ears on the 2 data sets. Similar outcomes were observed for cholesteatoma identification (mean area under the receiver operating characteristic curve 0.85, SD 0.03 and 0.83, SD 0.05; mean accuracies 0.783, SD 0.04 and 0.813, SD 0.033, respectively). The proposed 3D model achieved a commendable balance between performance and network size relative to alternative models. It significantly outperformed the 2D approach in detecting COM (P≤.05) and exhibited a substantial gain in identifying cholesteatoma (P<.001). The model also demonstrated superior diagnostic capabilities over resident fellows and the attending otologist (P<.05), rivaling all senior clinicians in both tasks. The generated heat maps properly highlighted the middle ear and mastoid regions, aligning with human knowledge in interpreting temporal bone CT. The resulting AI system achieved an accuracy of 81.8% in generating preoperative diagnoses for 121 patients and contributed to clinical decision-making in 90.1% cases. Conclusions: We present a 3D CNN model trained to detect pathological changes and identify cholesteatoma via temporal bone CT scans. In both tasks, this model significantly outperforms the baseline 2D approach, achieving levels comparable with or surpassing those of human experts. The model also exhibits decent generalizability and enhanced comprehensibility. This AI system facilitates automatic COM assessment and shows promising viability in real-world clinical settings. These findings underscore AI’s potential as a valuable aid for clinicians in COM evaluation. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000036300; https://www.chictr.org.cn/showprojEN.html?proj=58685