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Accurate quantification of machined surface roughness is crucial to the characterization of part performance measures, including aerodynamics and biocompatibility. Given this cruciality, there exists a need for predictive metrology models which can predict the surface roughness before a part leaves a machine to reduce metrologyinduced bottlenecks and improve production planning efficiency under emergent production paradigms, e.g., Industry 4.0. Current predictive metrology approaches in machining generally train machine learning models on all available input features at once. However, this approach yields a high number of free parameters during all states of training, possibly leading to suboptimal prediction results because of the complexity of simultaneously optimizing all parameters at once. In addition, previous machine learning-enabled surface roughness prediction studies have used limited test dataset sizes, which reduces the reliability and robustness of the reported results. To address these limitations, this study proposes a two-stage model training approach based on domainincremental learning, wherein a second stage of training is performed using an expanded input domain. The proposed method is evaluated on a 3,000-element experimentally collected testing dataset of machined H13 tool steel surfaces, where it achieves 16.3 % roughness prediction error compared to the 29.5 % error of the conventional single-stage training approach, indicating the suitability of the two-stage training method for reducing surface roughness prediction error. 

During machining, kinetic energy is imparted to a workpiece to remove material. The integrity of the machined surface, which depends on the energy transfer, affects the quality and performance of the product, therefore needs to be quantified. Prior studies have indicated the potential of using machining power, or the power consumption at the tool-chip interface, as a process signature for predicting machined surface integrity. However, direct measurement of machining power is constrained by the availability of special equipment and the associated cost. To address this gap, this paper presents a machine learning-based method for machining power prediction through multi-sensor fusion and sequence-to-sequence translation from acoustic and vibration signals, which represent portions of the in-situ kinetic energy dissipation, to the machining power signal as a process signature. Specifically, a neural network architecture is developed to separately translate the acoustic and vibration signals to corresponding machining power signals. The two predicted power signals are subsequently fused to arrive at a unified power signal prediction. To check for spurious decision logic, the sensor fusion model is interpreted using integrated gradients to reveal temporal regions of the input data which have the most influence on the machining power prediction accuracy of the fusion model. Systematic cutting experiments performed on a lathe using 1018 steel have shown that the developed sensor fusion method for process signature prediction can successfully map machine acoustics to power consumption with 5.6% error, tool vibration to power consumption with 8.2% error, and acoustics and vibration, jointly, to power with 2.5% error. Model parameter interpretation reveals that the vibration signal is more influential on the machining power prediction result than the acoustic signal, but that overall model accuracy is diminished if only the vibration signal is used. 

Autonomous robots that understand human instructions can significantly enhance the efficiency in human-robot assembly operations where robotic support is needed to handle unknown objects and/or provide on-demand assistance. This paper introduces a vision AI-based method for human-robot collaborative (HRC) assembly, enabled by a large language model (LLM). Upon 3D object reconstruction and pose establishment through neural object field modelling, a visual servoing-based mobile robotic system performs object manipulation and navigation guidance to a mobile robot. The LLM model provides text-based logic reasoning and high-level control command generation for natural human-robot interactions. The effectiveness of the presented method is experimentally demonstrated. 

Abstract Skeletal fixation plates are essential components in craniomaxillofacial (CMF) reconstructive surgery to connect skeletal disunions. To ensure that these plates achieve geometric conformity to the CMF skeleton of individual patients, a pre-operative procedure involving manual plate bending is traditionally required. However, manual adjustment of the fixation plate can be time-consuming and is prone to geometric error due to the springback effect and human inspection limitations. This work represents a first step towards autonomous incremental plate bending for CMF reconstructive surgery through machine learning-enabled springback prediction and feedback bending control. Specifically, a Gaussian process is first investigated to complement the physics-based Gardiner equation to improve the accuracy of springback effect estimation, which is then incorporated into nonlinear model predictive controller to determine the optimal sequence of bending inputs to achieve geometric conformity. Evaluation using a simulated environment for bending confirms the effectiveness of the developed approach.