This study proposes a human-in-the-loop framework that integrates operator observations into a large language model (LLM) to control process parameters for defect handling in fused deposition modeling (FDM) 3D printing. Fully autonomous LLM-based control handles ambiguous sensor data poorly and cannot detect abnormal conditions that lie beyond the installed sensors. Operator observations may compensate for these limitations, but their actual impact on LLM decision-making has not been sufficiently validated. We therefore implemented the proposed framework and defined experimental scenarios involving erroneous parameter injection and environmental disturbances. The framework was evaluated in terms of LLM response quality and print quality. The LLM achieved over 80% response quality on the defined evaluation metrics and generated appropriate parameter adjustments, improving print quality by more than 55% on average. Comparative experiments further revealed that, without operator observations, the LLM sometimes failed to recognize defects. These findings demonstrate the effectiveness of human–LLM collaboration and provide a practical foundation for intelligent FDM process control.
Here in, a high-quality automotive camera lens was developed based on an ultra-precision diamond turning core and cyclic olefin polymer (COP) injection molding process. To improve surface roughness and achieve the accuracy of plastic injection molding lens, systematic mold core machining process was developed and demonstrated using the diamond turning machine. The cutting tool path was generated by using NanoCAM 2D, and it was partly revised to prevent interference between the cutting tool and the workpiece. After the initial machining using the generated tool path, the compensation-cutting process was conducted based on the measured surface profile of an initially machined surface. After two times of compensation machining, the fabricated core mold showed a shape error of 100 nm between peak to valley (PV) and Arithmetic mean roughness (Ra) of 3.9 nm. The performance of the fabricated core was evaluated using an injection molding test. Injection molded aspheric plastic lens showed contrasts that were higher than 55% at 0.0 F, 30% at 0.3 F, and 20% at 0.7 F without any moiré phenomenon that meets the specification for automotive vision module with 1MP and 140° field of view.
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Research progress on grinding contact theory of axisymmetric aspheric optical elements Wenzhang Yang, Bing Chen, Bing Guo, Qingliang Zhao, Juchuan Dai, Guangye Qing Precision Engineering.2026; 97: 24. CrossRef
Non-uniform residual tool mark errors in diamond end-fly-cutting microstructures Jianpeng Wang, Zejia Zhao, Ling Ling Chen, Linhe Sun, Tengfei Yin, Suet To International Journal of Mechanical Sciences.2026; 311: 111148. CrossRef
Wafer‐Scale Vitreous Carbon Molds Enabling Precision Glass Molding of Aspherical Lenses Muzahir Ali, Yong Kyu Kim, Seongmin Lee, Tasadduq Hussain, Jung‐Ho Lee, Azfar Ali, Seok‐min Kim Journal of the American Ceramic Society.2026;[Epub] CrossRef
Performance enhancement of material removal using a surface-refinement model based on spatial frequency–response characteristics in magnetorheological finishing Minwoo Jeon, Seok-Kyeong Jeong, Woo-Jong Yeo, Hwan-Jin Choi, Mincheol Kim, Min-Gab Bog, Wonkyun Lee The International Journal of Advanced Manufacturing Technology.2024; 135(11-12): 5391. CrossRef
Dual-drive H-type gantry stages, powered by direct-drive linear motors, are commonly used in precision industrial applications that require high positioning accuracy over a large workspace. The cross-arm rigidly couples the two parallel axes, making strict synchronization control essential for maintaining positioning accuracy. Conventional master-master control treats the coupling effects as disturbances without accounting for the synchronization of the two motors. As a result, its performance deteriorates significantly under non-uniform load distribution, payload rotation, and directly applied torque disturbances, all of which necessitate reliable synchronization control. This study proposes a synchronization control scheme that integrates a sliding-mode controller with an uncertainty and disturbance estimator (SMC-UDE) and a feedforward torque compensator for the inertial torque induced by payload rotation. The proposed controller was implemented on a Hardware-in-the-Loop (HIL) simulator constructed with two parallel voice coil motors, and its performance was experimentally validated on the testbed. The HIL experimental results demonstrate that the SMC-UDE controller with feedforward torque-disturbance compensation achieves a quicker settling time compared to the conventional master-master control strategy. Additionally, it effectively suppresses disturbances generated by a rotary motor attached to a payload, thereby maintaining synchronization accuracy under disturbed conditions.
Ti-6Al-4V titanium alloy is widely utilized in aerospace components, such as torque tubes and turbine blades, due to its outstanding strength-to-weight ratio and corrosion resistance. However, controlling surface roughness during machining is challenging because the alloy's low thermal conductivity and high chemical reactivity result in unpredictable variations in Ra. Traditional contact-based measurement methods are not only time-consuming but also incompatible with in-process monitoring, creating a disconnect between production and quality control. This study introduces a CNN-LSTM hybrid model for predicting surface roughness in Ti-6Al-4V shape machining, utilizing multi-sensor CNC data. The model effectively captures spatial correlations among nine sensors and temporal dependencies in sequential operations. We implement a stratified time-series split validation that maintains chronological order while ensuring a representative distribution of Ra values, reflecting realistic deployment conditions. Data were collected from machining tests on features of a torque tube part, comprising 4,154 samples with Ra values ranging from 0.57 to 0.74 μm. The CNN-LSTM model achieved R² = 0.8512, RMSE = 0.0199 μm, and MAE = 0.0096 μm, outperforming Random Forest, XGBoost, and standalone neural networks. These results demonstrate the feasibility of non-contact, in-process surface roughness prediction in aerospace manufacturing, facilitating proactive quality control without interrupting operations.
Cooperative 3D printing (C3DP) with multiple robotic manipulators can reduce build time through parallel deposition, but it requires layer partitioning that accounts for collision clearance, workload balance, G-code toolpath compatibility, and interlayer boundary alignment. This study presents a Voronoi- and graph-based layer partitioning framework for C3DP. STL geometry and G-code were integrated into layer-aligned data, and a 65 mm collision clearance was defined from the measured end-effector collision radius as the minimum separation preventing collisions between robots approaching non-adjacent Voronoi cells. Each layer was divided into Voronoi cells so that non-adjacent cells could be treated as collision-free regions. Cell adjacency and toolpath-based processing time were modeled as a weighted graph, and adjacent cells were clustered into workload-balanced task regions. Interlayer seed offsets staggered the partition boundaries, and graph coloring identified regions that could be printed simultaneously.The framework was evaluated by workload-balance simulations and printing experiments. Balance deteriorated when clusters were excessive relative to graph nodes. In experiments, the end-effector separation always exceeded the 65 mm clearance. Partitioned printing reduced the layer printing time from 70.063 to 63.57 min, a 9.3% reduction, and the second layer covered the preceding partition boundary, confirming the staggered-boundary implementation.
Environmental issues have become a global concern recently. Countries worldwide are making efforts for carbon neutrality. In the automotive industry, focus has shifted from internal combustion engine vehicle to eco-friendly vehicles such as Electric Vehicles (EVs), Hybrid Electric Vehicles (HEVs), and Fuel Cell Electric Vehicles (FCEVs). For driving strategy, research on vehicle driving method that can reduce vehicle energy consumption, called eco-driving, has been actively conducted recently. Conventional cruise mode driving control is not considered an optimal driving strategy for various driving environments. To maximize energy efficiency, this paper conducted research on eco-driving strategy for EVs-based on reinforcement learning. A longitudinal dynamics-based electric vehicle simulator was constructed using MATLAB Simulink with a road slope. Reinforcement learning algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Deep QNetwork (DQN), were applied to minimize energy consumption of EVs with a road slope. The simulator was trained to maximize rewards and derive an optimal speed profile. In this study, we compared learning results of DDPG and DQN algorithms and confirmed tendencies by parameters in each algorithm. The simulation showed that energy efficiency of EVs was improved compared to that of cruise mode driving.
This paper describes the calibration of a 2D vision system utilizing a high-precision robot that moves accurately along the z-axis. To achieve this, a one-axis high-precision robot was designed and manufactured, achieving a minimum travel distance of 0.007 μm during testing. The 2D vision system comprised an RGB camera, an LVDT sensor, and a laser sensor for distance measurement. Additionally, a subpixel-based algorithm was developed for the calibration and size measurement of the system. Following the calibration process with the high-precision robot, the size error was found to be within ±0.1 mm when using the LVDT sensor and ±0.2 mm with the laser sensor. Thus, the 2D vision system and calibration algorithm presented in this paper are deemed suitable for accurate object measurement by robots.
Demand for high-performance bonded magnets with complex geometries is growing in electric motors, sensors, and energy devices. In lithography-based composite manufacturing (LCM), the magnetic properties of anisotropic NdFeB composites depend strongly on particle alignment during curing. This study compares the magnetic characteristics of 70 wt% NdFeB composites fabricated under magnetic-field-assisted and non-aligned conditions. An in situ alignment module was integrated into the manufacturing platform to induce directional particle orientation. The magnetic flux distribution was analyzed in ANSYS Maxwell, and particle alignment behavior was simulated by discrete element method (DEM) modeling in ANSYS Rocky. Vibrating sample magnetometry confirmed that magnetic-field-assisted processing enhances the anisotropic magnetic response, in agreement with the simulation predictions.
Gearboxes used in the drivetrain of intelligent robots are key mechanical components that play a significant role in determining the performance of modern robotic systems. Gearboxes employing the planetary gear mechanism, known to achieve a wide range of reduction ratios while remaining relatively cost-effective, have recently been adopted in robot drivetrains. In this paper, we utilize domestic technology to fabricate a gearbox using a compound planetary gear mechanism and conduct an evaluation of eight performance aspects of the developed gearbox through the fabrication of a dynamometer and a jig. The dynamometer comprised of the gearbox, input motor, input-output torque sensors, and a powder brake. By driving the input motor and applying braking force with the powder brake, we compare input torque sensor values with output torque sensor values to derive results. A test jig is created, consisting of an input motor, gearbox, and encoder sensor, for the measurement of inverse operation characteristics and backlash. By conducting a performance evaluation of the developed high-strength, high-reduction-ratio compact planetary gearbox, we validate the potential of the testing system and extend the scope of domestic gearbox technology development.
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Development of an Actuator and Controller for Robotic Joints Integrating a Frameless BLDC Motor and a Stepped Planetary Gear Reducer Sangsin Park Journal of the Korean Society for Precision Engineering.2026; 43(2): 183. CrossRef
Performance Test for 1-stage Shifting Planetary Gear Reducer for Hydraulic Motors Jin-Gyeong Kang, Young-Rak Yoo, Kyu-Tae Park, Sung-Ki Lyu Journal of the Korean Society of Manufacturing Process Engineers.2026; 25(5): 127. CrossRef
Three-dimensional reconstruction of gearbox from multi-view point clouds with surface feature parfameter measurement method Jian Chen, Zhijia Zhang, Guanghui Liu, Dejian Li, Qiushuang Li Engineering Research Express.2025; 7(4): 045253. CrossRef
This study presents a Python-based design tool that utilizes a data-driven backtracking method for aspheric coefficients to effectively address thermal deformation in aspheric glass lenses produced by the Glass Molding Press (GMP) process. To achieve the nanometer-level form accuracy essential for optical communications and high-power laser applications, it is crucial to compute compensation coefficients through nonlinear least-squares fitting of the lens's measured data to the standard aspheric equation. The proposed tool enhances user-friendliness with a PyQt GUI and incorporates the lmfit library, offering unique flexibility by allowing users to select or fix specific variables among up to 22 parameters, including the radius of curvature, conic constant, and aspheric coefficient, during the fitting process.
Military shelters house various high-power electronic systems that generate significant heat during operation, making effective thermal management essential for ensuring system reliability and operational stability. This study proposes a one-dimensional (1D) modeling approach for analyzing the thermal behavior of a military shelter–HVAC system using Simcenter AMESIM. The model incorporates key thermal components, such as internal heat sources, shelter structures, and HVAC performance characteristics, to accurately represent dynamic thermal behavior under realistic operating conditions. To validate the proposed 1D model, a comparative analysis was performed using threedimensional computational fluid dynamics (CFD) simulations with ANSYS Fluent, maintaining identical boundary and operating conditions. The analysis focused on the temporal and spatial variations of the internal air temperature within the shelter. Results showed a strong correlation between the AMESIM model and the CFD simulations. This approach significantly reduces computational costs and modeling complexity compared to traditional CFD-based analyses, while still providing adequate accuracy for system-level thermal performance evaluation. Consequently, the developed AMESIM-based 1D model serves as an efficient and reliable tool for the design and performance assessment of military shelter HVAC systems.
This study proposes a hybrid wire arc directed energy deposition (WA-DED) process that uses 3D sand-printed supports to overcome the limitations of low-angle overhang fabrication. WA-DED is a metal additive manufacturing process offering high deposition rates, cost efficiency, and suitability for large-scale components. However, because of its high heat input and molten pool instability, low-angle overhang and hollow structures remain difficult to fabricate, as the molten metal tends to collapse under gravity and thereby degrade geometric accuracy and surface quality. To address this issue, sand-printed supports were introduced. The supports provide tailored mechanical constraint and guide the solidification of the molten pool during deposition. Experiments were conducted to evaluate the feasibility of the proposed process at various overhang angles. The results show that the hybrid process markedly improves deposition stability and enables the fabrication of low-angle and curved overhang structures that conventional WA-DED cannot produce. These findings confirm the effectiveness of sand-supported WA-DED and highlight its potential for industrial applications requiring complex geometries, such as aerospace, marine, and energy components.
Hair-like surfaces in nature consist of high-aspect-ratio fibers with diameters below 100μm, falling to several tens of micrometers in softer hairs. These fine fiber arrays govern tactile softness, flexibility, surface texture, and mechanical response. Conventional fiber-spinning methods produce fine fibers effectively but offer limited control over the position, direction, and patterned arrangement of individual fibers. Here we propose a fused deposition modeling (FDM)-based strategy that combines melt extrusion with geometric drawing to fabricate PLA hair-like fibers. PLA melted fully at the processing temperature of 250oC, well below the thermal degradation onset near 330oC. DSC analysis showed that faster cooling suppressed thermodynamic crystallization, indicating that the final fiber structure is governed by drawing history and rapid cooling rather than by increased crystallinity. As the printing speed increased, the fiber diameter decreased nonlinearly, following D ≈ 106.3 v-0.45, in excellent agreement with the D v-0.5 scaling predicted by the continuity equation. Tensile strength and modulus increased with printing speed, whereas elongation and toughness decreased, indicating drawing-induced molecular orientation. These results demonstrate that FDM can serve as a programmable platform for fabricating biomimetic hair-like fiber arrays with predictable diameter and mechanical properties.
This study investigated the ultrasonic fatigue behavior of ABS and of a high-strength photopolymer resin (Rigid Black) fabricated by digital light processing (DLP) additive manufacturing. The dynamic elastic modulus of both materials was measured so that specimens could be designed to satisfy the 20 kHz resonance condition. ABS specimens were CNC-machined, whereas Rigid Black specimens were DLP-printed and post-cured. Thermal effects were minimized by compressed-air cooling with a 0.3 s/3 s duty cycle. S-N curves showed that fatigue life increased as the stress amplitude decreased for both materials. ABS exhibited higher fatigue strength and a more consistent life distribution, which is attributed to its homogeneous microstructure. Rigid Black showed lower fatigue strength with greater scatter, reflecting the anisotropy and interfacial inhomogeneity introduced by layer-by-layer fabrication. Fractographic analysis revealed that ABS underwent mixed-mode ductile-fatigue fracture through crazing, whereas Rigid Black failed in a brittle manner, with directional crack propagation driven by process-induced defects. These results confirm the feasibility of ultrasonic fatigue evaluation for DLP-printed polymer components and provide a basis for assessing the durability of additively manufactured parts.
This paper presents an advanced robotic automation framework that combines an impedance-based compliance controller with an imitation learning network for high-precision peg-in-hole assembly. The framework is characterized by three key features. First, it employs an impedance-based compliance controller to ensure stable contact. This approach enables the robot to adapt flexibly to external contact forces, functioning like a spring-damper system to prevent potential damage. Second, domain randomization is applied to both geometric and visual properties within a high-fidelity simulation environment. This strategy effectively narrows the reality gap, enhancing robustness against environmental uncertainties and visual disturbances. Third, the framework utilizes an action-chunking-transformer (ACT) network to predict precise action sequences based on multimodal data, reducing compounding errors in trajectory generation and improving assembly success rates. Each feature is supported by specific advancements, such as real-time force feedback integration, diverse simulation scenario generation, and multimodal sensor fusion. Extensive experiments conducted in various unseen environments demonstrate the framework's effectiveness, confirming its suitability for complex assembly tasks that require high adaptability and precision under diverse conditions.
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