Micro electrical discharge deposition (micro-EDD) enables maskless direct-write metallic tracks on conductive substrates, but reported implementations rely on purpose-built machines with programmable pulse generators and gap servos. This paper reports a low-cost micro-EDD platform retrofitted from a desktop CNC router driven by GRBL firmware, extended with an Android control application for path definition and process supervision. Discharge is produced by an RC relaxation circuit with no pulse generator and no gap feedback. Copper, aluminium, nickel, and titanium electrodes were deposited onto silicon substrates in an argon atmosphere, maintaining a 10 μm separation. The working point, pd ≈ 0.76 Torr·cm, lies within 20% of the argon Paschen minimum. Track width rises linearly with supply voltage over 330–400 V (w = 0.570 V − 99.2 μm, R2 = 0.994), extrapolating to a deposition threshold of 174 V, 37 V above the argon breakdown minimum—indicating an energy threshold distinct from breakdown. Across electrode materials, width shows a trend consistent with thermal diffusivity as w∝α0.46 (R2 = 0.69, N = 4), resolving the inconsistency that copper, the best conductor, gives the widest track. Polarity reversal changes width by factors of 4.6 (Cu) and 7.5 (Al), consistent with anode-dominated energy partition. Péclet numbers remain below 3 × 10−4 and overlap ratios above 103, excluding thermal advection and insufficient overlap as causes of the feed-rate collapse. A five-axis kinematic extension is derived, with Z travel identified as the bounding constraint.
Lightweight design of high-speed-train water tanks can easily lower the natural frequency of baffles, increasing the risk of resonance and fatigue failure and thus threatening operational safety. To address this issue, a lightweight optimization method that does not reduce the first-order natural frequency of the baffle is proposed, taking a suspended water tank of a certain type of CRH electric multiple unit (EMU) as the research object. First, a two-way fluid–structure interaction (FSI) finite element model was established based on the computational fluid dynamics (CFD) method. The design of experiments method was employed to determine the optimal number of baffles inside the tank, and the response surface methodology was applied to construct quadratic polynomial surrogate models for baffle mass, maximum water tank stress, baffle deformation, and the first-order natural frequency. Analysis of variance was conducted to verify the fitting accuracy and significance of each model. After clarifying the influence of design variables on the response indicators, and to overcome the shortcomings of the standard multi-objective particle swarm optimization (MOPSO) algorithm, such as susceptibility to local optima, simplistic constraint handling, and premature convergence, an improved multi-objective particle swarm optimization (IMOPSO) algorithm integrating chaotic initialization, adaptive parameter adjustment, and a dynamic mutation strategy was proposed. With the first-order natural frequency serving as a constraint, multi-objective optimization of the baffle structure was carried out. Finally, the prediction accuracy of the surrogate models was numerically validated using finite element simulation software. The results show that after optimization, the baffle mass was reduced by 16.13%, the first-order natural frequency increased by 0.41 Hz (by FEM), the maximum water tank stress decreased by 288 Pa (by FEM), and the baffle deformation was reduced by 7.66% according to FEM verification (RSM surrogate-model prediction gave a reduction of 9.07%). The maximum prediction error of the surrogate models was only 1.53%, confirming the effectiveness and feasibility of the proposed method. This study can provide a theoretical basis and engineering reference for the improvement and performance optimization of water tanks on CRH EMUs and other similar tank structures.
Mapless navigation often removes global maps while retaining localization-derived goal vectors or bearings. We study a stricter setting in which a mobile robot observes only local LiDAR, scalar goal range, and short histories of executed actions; neither pose nor goal direction is provided to the policy. We introduce ACR-Nav, an action-conditioned range navigation framework that converts scalar-range evolution into closed-loop progress information. Its range–action history associates each distance change with the motion that produced it, while the sectorized LiDAR captures local geometry and short-term obstacle motion. A LiDAR-only safety filter provides immediate collision intervention, and a static-to-mixed curriculum stabilizes learning. A lightweight multilayer–perceptron is optimized with Proximal Policy Optimization (PPO), while the ACR-Nav formulation itself remains optimizer-agnostic. In corridor simulations, ACR-Nav achieved 93.2%, 80.4%, and 84.4% success in static, mixed, and dynamic environments. Removing the safety filter reduced success by 15.2, 14.6, and 16.0 percentage points in static, mixed, and dynamic environments, respectively, and random-goal tests yielded 91.2% and 81.4% success in static and mixed settings. Topology-shift experiments further quantified adaptation to an L-shaped corridor. The results show that action-conditioned scalar-range evolution can support goal-directed, segment-level navigation within locally straight corridor passages without exposing robot pose or target bearing to the policy.
The increasing performance requirements of electric vehicle powertrains demand lightweight, efficient, and low-noise transmission systems. High-speed electric drive unit concepts offer significant potential for reducing motor size and mass by shifting torque generation to higher rotational speeds. However, this approach places increased demands on gearbox power density, efficiency, and noise, vibration, and harshness (NVH) performance. This work investigates the NVH behaviour of a compact, high-speed automotive gearbox with a focus on planetary gear stages. Although planetary stages offer high compactness, their complex kinematics can lead to pronounced NVH challenges. In particular, sequentially phased gear meshing results in characteristic sideband components whose orders can be predicted analytically, while their amplitudes remain difficult to estimate reliably during the design phase, necessitating experimental validation. Several NVH-oriented design measures, including high-contact-ratio gearing and low-NVH microgeometry, are applied to a two-stage gearbox comprising a planetary and a cylindrical gear stage. Peak-to-peak transmission error is used as a primary NVH design metric. The planetary stage is analysed in detail to assess the influence of sequential phasing on sideband components in the dynamic response and resulting vibration behaviour. The NVH-oriented gearbox is tested on a bench, with housing accelerations used to analyse planetary sidebands, providing insights into the NVH potential of compact, high-speed gearboxes and the role of sequential phasing in the vibration response.
This study compares a conventional feature-based representation and a physics-guided condition-index (CI) representation for vibration-based fault diagnosis of industrial motors. Both representations were constructed from identical vibration signal segments and evaluated under the same classification conditions using support vector machine (SVM) classifiers. For each representation, a genetic algorithm (GA) was repeatedly applied to the training data to select three representative variables, after which the SVM hyperparameters were optimized using three-fold cross-validation. Permutation Importance and SHapley Additive exPlanations (SHAP) were subsequently used to interpret the contributions of the selected CIs. Independent test motors, whose fault conditions had been established through manufacturer troubleshooting before the present analysis, were excluded from all model-development procedures. For the independent Unbalance and Misalignment test motors, the CI-based model achieved segment-level classification rates of 99.83% and 100%, respectively, whereas the conventional representation showed substantial misclassification. Because these segments originated from a single physical motor for each fault condition, the reported rates represent within-motor segment-level outcomes rather than population-level estimates of diagnostic performance. FFT analysis revealed dominant 1X and 2X components in the corresponding test data, consistent with their established fault conditions. For an additional independent motor identified as Air-gap Unbalance, the CI-based model classified all test segments as Air-gap Unbalance, while the FFT spectrum exhibited fractional-frequency characteristics similar to those observed in the corresponding fault data. Overall, the physics-guided CI representation produced classification outcomes that were more consistent with the established fault conditions and provided a more physically interpretable basis for model decisions under the industrial motor conditions examined in this study.
Failure mode and effects analysis (FMEA), as a means of identifying, preventing, and controlling potential system failures, has been widely applied in many industries. However, the classic FMEA method has several drawbacks in practical applications, such as the uncertainty and ambiguity in the expression of evaluation information, the neglect of the reliability of evaluation information, and the failure to consider the psychological behavior of experts, which leads to inaccurate evaluation results. Therefore, this paper proposes an improved FMEA framework that integrates Z-number theory with the criteria importance through intercriteria correlation (CRITIC) and technique for order preference by similarity to ideal solution (TOPSIS) methods to improve the accuracy of failure mode risk ranking. Specifically, Z-numbers are employed to represent expert assessment information, effectively capturing both fuzziness and reliability. The CRITIC method is then extended with Z-numbers to determine the weights of risk factors, which not only accounts for the interrelationships among factors but also prevents information loss caused by defuzzification of weights. Moreover, to handle missing assessment data, a generalized Z-number distance measure is introduced, and the TOPSIS model is enhanced to rank failure modes by considering the reliability and uncertainty of the information. Finally, the effectiveness of the proposed method is verified by taking the pallet exchange device of a CNC machine tool as an example. The results show that, compared with other FMEA methods, the proposed method considers the reliability and fuzziness of the evaluation information, maintains the original Z-number information structure, and provides more accurate and reliable risk-ranking results.
This paper proposes a data-driven model predictive control (MPC) framework for high-precision speed control of rotary traveling wave ultrasonic motors (RTWUSMs) under temperature drift. To address the strong nonlinearity and time-varying thermal characteristics of RTWUSMs, a Koopman–convolutional neural network–long short-term memory (Koopman–CNN–LSTM) prediction model with radial basis function (RBF) observable features is constructed. The model maps the nonlinear electromechanical coupling and friction-driven dynamics of the motor into a linear invariant subspace, achieving high prediction accuracy while maintaining low computational complexity. On this basis, a data-driven MPC scheme is designed, which eliminates the dependence on accurate analytical plant models and compensates for thermal-induced speed drift through online driving frequency adjustment. The experimental results show that under 900 s of continuous operation, the proposed scheme achieves a relative steady-state speed error of 0.37%, which is significantly better than typical temperature drift compensation methods. This scheme can also provide stable tracking performance under load torques of 0.5 N·m and 1.0 N·m, providing a practical solution for the long-term stable speed regulation of RTWUSM in precision drive applications.
Cross-condition bearing fault diagnosis suffers from severe performance degradation due to domain shift across different operating conditions, especially when only a few labeled target-domain samples are available. To address this challenge, this paper proposes a progressive attention-guided two-stage transfer learning framework for few-shot bearing fault diagnosis across different fixed operating points. First, the raw time-domain vibration signals are fused with frequency-domain representations extracted by short-time Fourier transform (STFT) to enhance fault feature representation. Then, a progressive attention-guided feature learning strategy is developed by integrating dual efficient channel attention (ECA) modules into a deep one-dimensional convolutional neural network (1D-CNN), enabling the network to adaptively emphasize fault-sensitive features while suppressing redundant information. Subsequently, a two-stage transfer learning strategy is designed, consisting of transferable feature learning from the source domain and few-shot adaptation to the target domain. During target-domain adaptation, key feature extraction layers are frozen, and a sample-balanced optimization mechanism is introduced to alleviate the dominance of source-domain samples during joint training. Experimental results on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves an average accuracy of 99.96% across three cross-condition transfer tasks. Furthermore, experiments conducted on a self-built shaft system dataset show that the proposed method achieves an average accuracy of 87.11% under three representative transfer scenarios. The results verify that the proposed framework effectively mitigates domain shift and enables accurate bearing fault diagnosis with limited labeled target-domain samples.
Optimizing transmission efficiency is critical for advancing electric vehicle (EV) technology. This review paper covers advanced driveline schemes for electric cars and electric heavy vehicles through a selection and analysis of articles published over the past decade, focusing on passenger and commercial electric vehicle drivetrains, transmission efficiency modeling, gear optimization, and tribological and NVH outcomes. The selected sources are grouped into analytical themes to map current academic and industrial developments in transmission topologies, specifically comparing single-speed reduction units with two-speed and multi-speed configurations. A comparative summary is discussed to consolidate the characteristics, complexity, efficiency impact, and typical applications of the various gearbox types. The review article is complemented by an examination of loss mechanisms through the lens of international tribological research. Key focal points include mechanical power losses, gear and bearing friction, and the trade-offs associated with low-viscosity fluids for electric vehicles to provide a comprehensive support resource for researchers and designers in the field of transmissions for EVs. Future development trajectories are delivered regarding the increase in efficiency and specific challenges of EV gearboxes.
Passive tuned mass dampers (TMDs) can reduce chatter, but designing and fabricating an accurately tuned absorber remains challenging due to manufacturing constraints. This study proposes a Design of Experiments and Finite Element Analysis (DOE-FEA) based constrained design optimization framework for a passive two-degree-of-freedom (TDOF) TMD to suppress regenerative chatter in boring operations by considering practical and manufacturing constraints on absorber position, mass ratio, moment of inertia and fixed inter-spring distance. The proposed, additively manufactured TMD housing, made from polylactic acid (PLA), includes a mass block supported by two spring-damper elements that enable coupled translational and rotational interactions with the boring bar. A finite-element forced vibration analysis of the boring bar TMD system is developed to obtain the real and imaginary parts of the frequency response function (FRF), which are then used to construct the stability lobes. The minimum limiting depth of cut over the spindle speed range is used as the optimization criterion, and response surface methodology (RSM) is used to obtain optimum absorber parameters within realistic design constraints. The dynamic behaviour of the TDOF TMD is experimentally and numerically evaluated and compared with that of a single-degree-of-freedom (SDOF) TMD, attributing the relative improvement in performance primarily to the combined effects of independent absorber architecture, mass, stiffness and damping distribution and dynamic tuning. The results showed that the optimal TDOF TMD achieved a DOC of 11.054 mm, while the SDOF TMD achieved 4.335 mm. The experimental investigation of additively manufactured TDOF and SDOF TMDs demonstrated qualitatively similar dynamic phenomena to those of the corresponding numerically optimized absorbers. Time-domain acceleration response, spectrogram and power spectrum were used to compare these dynamic phenomena demonstrated by the SDOF and TDOF TMDs. A reduction in corresponding first and second amplitude peaks from −2.8 dB (670 Hz) and −22.7 dB (1360 Hz) in the case of the SDOF TMD to −17.6 dB (600 Hz) and −23.8 dB (1150 Hz) for the TDOF TMD verified the vibration attenuation and frequency redistribution phenomenon as exhibited by the FE-model.
Class imbalance is common in vibration-based fault diagnosis because normal-condition data are generally more abundant than fault data. This study proposes an auxiliary-classifier Wasserstein generative adversarial network with gradient penalty and spectral normalization, termed ACWGAN-SG, for fault-sample generation and progressive dataset augmentation. The method combines class-conditioned generation, Wasserstein adversarial learning, gradient penalty, spectral normalization, and PCC-CS-based sample screening. Experiments were conducted on the public CWRU bearing dataset and a self-built 12-class harmonic-reducer dataset, with the downstream diagnostic experiments covering balance ratios from 1:100 to 1:1. The CWRU and harmonic-reducer experiments were independently repeated five and three times, respectively. Under the balanced condition, ACWGAN-SG achieved mean diagnostic accuracies of 98.40% and 97.627% on the two datasets. On the harmonic-reducer dataset at BR = 1:2, the method obtained a Macro-F1 of 94.298%, a balanced accuracy of 94.333%, and an MCC of 0.9383. Repeated-run statistical analyses showed significant overall differences among the evaluated methods across the tested balance ratios. These results indicate that the proposed generation and progressive-augmentation procedure improves downstream diagnostic performance under the reported experimental settings.
Ultra-wideband (UWB) positioning provides cost-effective localization for quadrotors in global navigation satellite system (GNSS)-denied environments, but geometry-dependent, heavy-tailed errors challenge state estimation and safety-critical control. This paper presents a risk-aware framework linking geometry-aware error quantification with belief-space collision avoidance under a fixed four-anchor UWB configuration. Specifically, horizontal dilution of precision (HDOP) and nearest-anchor distance are used as spatial features in a Student’s-t process regression (STPR) model to predict UWB positioning errors and quantify the associated uncertainty. The compensated UWB measurements are then fused with inertial data through a Kalman filter to obtain a Gaussian belief state. A belief control barrier function (BCBF) maps ellipsoidal collision regions to a unit sphere, approximates them using tangent half-spaces, and is embedded in nonlinear model predictive control (NMPC). In outdoor flight experiments, the positioning RMSE is reduced to 0.071 m by the proposed STPR-KF method, compared with 0.299 m for raw UWB and 0.292 m for conventional KF. Feasible risk-aware obstacle avoidance and adjustable safety clearance are further demonstrated through numerical simulations. The feasibility of linking geometry-aware UWB error characterization with belief-space safety constraints for UWB-localized quadrotor navigation is therefore indicated.
Hydraulic short-circuit (HSC) operation is an important approach to enhancing the operational flexibility of pumped-storage power plants (PSPPs). However, under this new operating mode, the flow characteristics in the bifurcated pipe deteriorate significantly, posing a threat to the efficiency of the piping system and potentially affecting the inflow conditions for the turbine. In this study, six improved bifurcated pipe models were designed, and their internal flows under pumping, generating, and HSC modes were numerically simulated. Entropy production theory and vortex identification method were employed for flow field analysis. The results show that local modifications confined to the bifurcation are insufficient to simultaneously improve energy characteristics across different modes. In contrast, the bypass pipe enables early flow diversion, weakening the original high-dissipation regions while introducing controllable additional losses. M6 achieves an average energy loss reduction of 47.85% in the mid-to-high flow split ratio range (FSR > 0.3). A strong correlation is observed between vortex suppression and energy loss reduction: the bypass pipe substantially shortens the main vortex length at the inlet section of the generating branch, while simultaneously inducing new shear vortices at the junction; adjustment of its installation position is expected to further shorten their extension, thereby ensuring the normal operation of the turbine. This study provides a new technical pathway for extending the operating range of HSC operation and contributes to enhancing the grid-regulation capability of PSPPs.
A collision-free path is not sufficient for visibility-dependent mobile robot tasks: a moving obstacle can block the camera–target line of sight and cause inspection or visual-servoing failure even when the robot remains physically safe. Maintaining visual contact with targets is therefore important in Industry 4.0 environments, yet visibility-preserving maneuvers can conflict with navigation progress and collision avoidance. This work presents the Visibility-Informed Safety and Target Awareness framework with control barrier function filtering and occlusion-evasive local replanning (VISTA-CBF+ELR). The architecture combines visibility-risk planning, target-bearing control, an ELR supervisor, and a CBF quadratic program that keeps collision constraints hard while relaxing field-of-view and occlusion requirements through slack. Counterproductive interventions are limited through persistence, benefit–cost and feasibility gates, progress protection, bounded dwell, recovery, and cooldown. In locked factory simulations, redesigned VISTA achieved 67% and 73% strict-goal success under clean and nominal sensing, whereas Visibility-CEM-2D achieved 87% and 86% but with lower clearance. In matched Gazebo trials, strict success was 19/30 for redesigned VISTA, 26/30 without ELR, and 16/30 for Nav2 Smac+MPPI; zero-clearance collisions were 8/30, 3/30, and 14/30, with no difference surviving multiplicity correction. A separate CEM stress test sustained 6.875 Hz optimization, missed 26.31% of 100 ms deadlines, and held commands on 31.35% of ticks. The results demonstrate repair of the ELR pathology and conditional visibility-risk reduction while exposing safety–visibility trade-offs, transfer limitations, and real-time constraints.
Machining-induced residual stress, cutting force, and temperature govern the fatigue life, dimensional stability, and surface integrity of milled components, yet predictive models for these responses are still routinely validated only in-sample, concealing overfitting on small, single-laboratory datasets. This study re-examines a published AISI 1045 end-milling dataset (N = 24, combining one-factor-at-a-time and Taguchi L9 trials) using six regression paradigms: Multiple Linear Regression (MLR), random forest, gradient boosting, Support Vector Regression (SVR), Gaussian process regression (GPR), and a shallow neural network (ANN)—under leave-one-out cross-validation (LOO-CV). The previously reported in-sample R2 of 0.84 (from a smaller n = 9 subset) was substantially higher than the LOO-CV R2 of 0.167 obtained here on the full dataset; although this gap cannot be attributed to cross-validation alone, it shows a substantial in-sample/out-of-sample performance gap. SVR gave the strongest, bootstrap- and nested-CV-confirmed cross-validated residual-stress prediction (R2 = 0.575); its apparent force advantage (R2 = 0.558) was statistically indistinguishable from GPR and did not survive nested tuning, so it is reported cautiously. GPR was narrowly best for temperature (R2 = 0.492); the ANN and SVR underperformed the linear baseline there, though nested tuning traced this largely to a fixed hyperparameter rather than the kernel method itself. Random forest permutation importance identified feed rate as the dominant residual-stress predictor, consistent with the original ANOVA. The contribution is a cross-validated, multi-paradigm reassessment with explicit uncertainty and sensitivity analysis, together with a candidate low-cost screening surrogate for AISI 1045 process planning—not a replacement for XRD or FE—and a broader caution to match model complexity to sample size.
To address the influence of multi-source probabilistic uncertain parameters on the dynamic transmission error (DTE) of harmonic drives, this paper proposes a robust DTE modeling and multi-objective optimization method. First, a Chebyshev surrogate model is constructed by integrating the measured static transmission error (STE) probability model, system dynamic equations, and identified nominal parameters. Prototype validations show a prediction mean absolute percentage error (MAPE) of 8.14% and a mean absolute error (MAE) of 7.761″. Meanwhile, compared to the original dynamic equations, the surrogate model reduces the single-evaluation time from 0.147 s to 0.000003 s (a 49,000-fold acceleration), effectively overcoming the efficiency bottleneck of numerical integration in dynamic response evaluation. Secondly, to achieve the collaborative optimization of system transmission accuracy and anti-disturbance robustness, a Chebyshev–AMP–MOPSO algorithm integrating a diversity entropy state-driven weight and a pyramid-hierarchical dual-track search strategy is proposed, which improves upon the issues of local convergence and uneven solution set distribution in the classical MOPSO and NSGA-II algorithms. On this basis, parameter optimization under three decision preferences was completed. The accuracy-first scheme reduces the DTE mean by 3.67%, the robustness-first scheme reduces the standard deviation by 9.36%, and the balanced scheme improves both. Finally, comparative tests on five prototypes show the actual dynamic parameters’ deviation (Di) relative to the theoretical optimal configuration exhibits a consistent corresponding trend with measured DTE means. Prototypes with the minimum (Di = 0.365) and maximum (Di = 0.474) deviations yield the lowest and highest measured means, respectively, matching theoretical optimization expectations.
Intelligent following control for electric-drive unmanned ground vehicles (UGVs) is important for human–robot collaboration and autonomous mobility. However, target occlusion, detection noise, and short-term detection failures can reduce following stability and reliability. To address these challenges, this paper proposes a vision-based following method that integrates adaptive Kalman filtering with target-state-aware control. The proposed method updates the observation noise covariance online using an innovation-sequence sliding window and evaluates the validity of visual measurements based on the intersection over union between adjacent frames, thereby enabling target position prediction and compensation under unstable observation conditions. Meanwhile, the longitudinal velocity and lateral angular velocity are dynamically adjusted according to the target detection state, image-center offset, and distance variation to achieve continuous and stable following control. Experimental results show that the proposed method reduces the target position prediction error by approximately 10% compared with conventional Kalman filtering and maintains the longitudinal following distance at about 3 m, improving the robustness, continuity, and motion smoothness of electric-drive UGV target following in complex environments.
Cabin heating significantly affects the driving range of electric vehicles (EVs) under cold weather conditions, making energy-efficient thermal management essential. Conventional control strategies typically regulate a fixed cabin temperature setpoint, which may lead to suboptimal energy consumption and does not directly account for passenger thermal comfort. This study proposes a real-time thermal comfort-oriented nonlinear model predictive control (NMPC) framework for EV heat pump systems. To enable real-time implementation, a control-oriented model is developed by combining a data-driven model for heat pump performance with a linearized Predicted Mean Vote (PMV) model. The proposed NMPC optimizes the trade-off between passenger thermal comfort and compressor energy consumption while satisfying actuator constraints. A high-fidelity physics-based virtual plant is employed to evaluate the proposed strategy. Simulation results show that the proposed NMPC maintains thermal comfort within the recommended PMV range while reducing total energy consumption by 8.3% compared with a conventional rule-based controller. Furthermore, a parametric study involving 1210 gain-tuning cases of a rule-based controller and 100 NMPC weighting scenarios demonstrates a consistently superior comfort–energy trade-off. The average computation time remains below 0.15 s on the simulation platform, indicating that the proposed NMPC formulation can be solved within the selected sampling interval. These results highlight the potential of thermal comfort-oriented NMPC for improving both passenger comfort and energy efficiency in EV heat pump systems.
Commercial laser-processing systems are typically designed for a specific manufacturing process, limiting their adaptability in research and prototyping environments. This study presents the staged evolution of a laboratory-scale prototype originally conceived for metal powder bed fusion into a reconfigurable laser-processing platform. Successive mechanical, optical, electrical, and control developments are consolidated, including the integration and calibration of a 200 W fibre laser, a galvanometric scanning system, motorised powder feed and build platforms, a recoater mechanism, and a combined LabVIEW and weldMARK control architecture. The capabilities of the resulting platform were assessed through optical commissioning by laser marking and two experimental case studies involving single-layer fusion of AISI 316L powder and laser transmission welding of dissimilar thermoplastics. The marking trials provided qualitative confirmation of beam delivery, focal adjustment, and programmed path reproduction. In contrast, the powder experiments produced continuous fused regions, demonstrating controlled laser–powder interaction without constituting full multilayer powder-bed-fusion validation. Thermoplastic welding generated mechanically resistant joints, with failure occurring cohesively within the foam substrate rather than at the welded interface. These results demonstrate the potential of the developed system as a reconfigurable research platform for different laser-processing operations. Nevertheless, fully automated multilayer powder bed fusion still requires improvements in platform levelling, machine-zero integration, process synchronisation, and atmosphere monitoring.
The coordinated control of networked integrated energy systems (IESs) is complicated by the different response speeds of the electrical, gas, and thermal subsystems and by information exchange over directed communication graphs. Existing studies mainly consider the economic scheduling of a single IES, dynamic modeling of individual systems, or cooperative control of systems with two time scales, and therefore, they do not provide a unified supervisory framework for coordinating three energy domains with an explicit performance bound. This paper investigates whether a common leader-following framework can coordinate the principal variables of the three energy domains while limiting both regulation errors and control effort. Each IES is treated as an agent, the three energy domains are represented by separate reduced dynamic blocks sharing a common directed communication topology, and a distributed state feedback controller is developed using relative information, Riccati-based gain design, and complete Lyapunov analysis. Simulations of a network of four IESs show that the longest settling times in the electrical and gas domains are approximately 0.05 s and 5.41 s, respectively, whereas the thermal disagreement decreases by 96.5% over 300 s; the accumulated cost remains below its calculated upper bound in the nominal case and in all three time-scale settings, while a separate numerical communication reconfiguration case illustrates bounded responses during communication link removal and reconnection. These results demonstrate that the proposed framework can simultaneously coordinate variables with substantially different response speeds while accounting for regulation accuracy and control effort under the stated reduced model and fixed graph assumptions. The communication reconfiguration case provides a numerical illustration and does not constitute a general stability guarantee for arbitrary topology switching.
