Sequential multi-step fabric folding is a challenging problem in robotic manipulation, requiring a robot to perceive the fabric state and plan a sequence of actions leading to the desired goal. Existing approaches suffer from low data efficiency, weak cross-task generalization, and high deployment costs, which are not practical for industrial applications. Thus, we present Fold-Llama, a novel method for robotic fabric folding that utilizes geometry-to-text encoding and lightweight large language models (LLMs). Fold-Llama explicitly encodes human folding strategies into structured geometric operations, including 10 common geometric operation strategies, and subsequently DeepSeek-R1 generates a total of 2640 structured data pairs based on these geometric operation strategies for Low-Rank Adaptation (LoRA) fine-tuning of the Llama-3.2-3B-Instruct model. The fine-tuned model combines general LLM reasoning with 10 learned folding strategies, enabling it to generalize across diverse fabric configurations. This text-only synthesis and local deployment pipeline fundamentally shifts fabric manipulation learning from vision-based imitation to language-grounded geometric reasoning. We experimentally evaluate Fold-Llama on four representative sequential folding tasks and show that it significantly outperforms baseline LLM-based approaches in simulation, demonstrating strong data efficiency with only 2,640 text samples (approximately 6% of traditional methods’ 48K+ robotic interaction samples) while achieving 90.5% error reduction over the best LLM baseline. Furthermore, our approach can be transferred from simulation to the real world without additional training by directly calling the API of models deployed on the vLLM platform. Despite training on rectangular fabrics, we also show our approach generalizes to T-shirts and shorts. Videos are available at: https://sites.google.com/view/fold-llama/home

Air-jet looms are energy-intensive machines, with auxiliary nozzles accounting for nearly 80% of the total compressed air consumption. However, accurate prediction and visual analysis of nonlinear air consumption remain challenging due to limited training data and the poor interpretability of deep learning models. To address these issues, this study proposes a hybrid CNN-CBAM-SVR model optimized by an Improved Archimedes Optimization Algorithm (IAOA). Comparative experiments show that the IAOA-CNN-CBAM-SVR model achieves the lowest root mean square error (RMSE) of 0.6575, and the highest coefficient of determination (R2) of 0.9941, outperforming SVR, CNN, and CNN-SVR models. Furthermore, the contributions of nozzle structural parameters to air consumption are visually illustrated using the Shapley Additive ExPlanations (SHAP) method. The findings provide a robust and interpretable model for optimizing auxiliary nozzles design and improving energy efficiency in air-jet looms.

In air jet looms, relay nozzles are critical components in governing airflow velocity and air consumption during the weft insertion process. Although computational fluid dynamics (CFD) offers high-fidelity simulation for aerodynamic analysis, its computational burden hinders its practicality in iterative aerodynamic design of relay nozzles. To address the challenge, this study proposes a data-driven framework integrating a Chebyshev polynomial Kolmogorov–Arnold Network (Chebyshev KAN) surrogate model with an Improved Multi-objective Red-billed Blue Magpie Optimizer (IMORBMO). The accuracy of the Chebyshev KAN model was benchmarked against conventional multilayer perceptrons (MLP), convolutional neural networks (CNN), and the standard Kolmogorov–Arnold Network (KAN). Experimental results demonstrate that the Chebyshev KAN model achieves the lowest mean absolute error (MAE) of 0.103 for airflow velocity and 0.115 for air consumption. Building upon the non-dominated sorting and crowding distance strategies, IMORBMO was developed, incorporating an adaptive mutation mechanism by information entropy for improvement of convergence, diversity, and uniformity of the Pareto-optimal solutions. Comprehensive evaluations on the ZDT and WFG benchmark suites confirm that the IMORBMO consistently attains the best and highly competitive performance, yielding the lowest generation distance (GD), inverted generational distance (IGD) values and the highest hypervolume (HV). Applied to the aerodynamic optimization of a relay nozzle, the proposed framework delivers an optimal aerodynamic design that increases airflow velocity by 10.5% while reducing air consumption by 15.4%, as verified by CFD simulation. The steady-state flow field was simulated by solving the Reynolds-Average NavierStokes equations with the k–ω turbulent model, utilizing Fluent 2025.R2. No-slip wall, inlet pressure and outlet pressures are boundary conditions to the relay nozzle surfaces. This work establishes a computationally efficient and accurate optimization paradigm that holds significant promise for aerodynamic design and other complex real-world engineering applications.

Air-jet weaving is a highly efficient textile manufacturing process in which compressed air propels the weft yarn, and the aerodynamic performance of the main nozzle directly affects weaving efficiency and fabric quality. However, strong nonlinearity between nozzle structural parameters and internal airflow behavior makes accurate velocity prediction difficult. To address this problem, this study proposes an interpretable INRBO-optimized CNN-BiLSTM-Attention framework for data-driven airflow velocity prediction in air-jet loom main nozzles. The framework combines CNN-based local feature extraction, BiLSTM-based modeling of ordered structural-parameter dependencies, and attention-based adaptive feature weighting. An improved Newton-Raphson-based optimizer (INRBO), incorporating Tent chaotic initialization, adaptive inertia weighting, and logistic chaotic perturbation, is introduced to automatically tune key hyperparameters and enhance convergence stability under limited-sample conditions. Airflow velocity measurements at 12 positions in the main-nozzle jet field were used for model construction and validation. In repeated tests on Measurement Point 1, the proposed model achieved the best predictive performance, with an RMSE of 5.6247 ± 0.1065, an MAE of 4.5428 ± 0.1016, and an R2 of 0.9875 ± 0.0010, outperforming conventional machine learning, deep learning, and other optimization-based hybrid models. Statistical tests confirmed the significance of these improvements. SHAP analysis identified the conical section length (Lz1) and the major diameter of the nozzle core cone (Dz1) as the dominant structural factors, with average SHAP values of 40.46 and 20.80, respectively. The proposed approach provides an accurate, automatically optimized, and interpretable tool for rapid main-nozzle structural evaluation and optimization.

Multi-objective optimization plays an important role in modern design and complex engineering applications. However, achieving an effective balance between the convergence and diversity of Pareto-optimal solutions remains challenging. This paper proposes a Sobol-driven Multi-objective Whale Migration Algorithm (SMOWMA), which extends the Whale Migration Algorithm within a non-dominated sorting and elite-selection framework. A maximin scrambled Sobol initialization scheme is first employed to improve the distribution of the initial population. An archive-guided adaptive Student-t flight mechanism is then incorporated into the leader-whale position update to dynamically balance global exploration and local exploitation. In addition, archive crowding information and archive-entry success feedback are jointly used to adjust the search behavior according to both environmental diversity and recent search performance. SMOWMA is evaluated on five widely used multi-objective benchmark suites, namely ZDT, DTLZ, WFG, UF, and CF, using four performance indicators: generational distance (GD), inverted generational distance (IGD), spacing (SP), and hypervolume (HV). The results, together with Friedman tests and Holm-adjusted Wilcoxon tests, demonstrate that SMOWMA achieves competitive overall performance in terms of convergence, diversity, and objective-space coverage, although its relative advantage remains problem-dependent. The practical applicability of SMOWMA is further examined using multi-objective welded-beam design formulations, a bi-objective four-bar truss design problem, and a five-objective car side-impact design problem. The engineering results show that SMOWMA can obtain competitive and stable approximation sets for constrained design problems with different numbers of objectives, supporting its effectiveness and applicability in multi-objective engineering optimization.

The crankshaft is the key component of air-jet loom, which is prone to damage and failure under high speed rotating condition. In this paper, theoretical calculation, numerical simulation and experiment study have been carried out to investigate the fatigue damage mechanism and fatigue life of crankshaft. Firstly, the complex force conditions of the crankshaft under different rotation conditions are studied briefly by the combination of mechanics analysis method and experimental tests. Then, the stress characteristic of crankshaft under different rotation speeds are analyzed by the finite element modeling. The fatigue damage of hot spots with high stress level is calculated systematically combining the rain-flow counting method, S-N curve, and Miner's linear fatigue damage model, of which the accuracy is verified through comparing with the simulation results obtained by the Fe-safe software and the experimental testing results. Finally, scanning electron microscope testing of the fracture section of crankshaft under high rotation speed has been done to analyze the failure mechanism. Simulation results and experimental results both reveal that obvious fatigue fracture occurs at the keyway of crankshaft under high rotating speed. Additionally, when the rotation speed below 800 r/min, the maximum value of von-Mises stress of keyway is lower than 200 MPa and indicates the low stress level of crankshaft for a long-term stable operation. While with the rotation speed increasing to 900 r/min, the dynamic load and torque of crankshaft are increased significantly, resulting in the drastic increase of the stress level and the dramatic reduction of the fatigue life. Furthermore, the fatigue life of the crankshaft under the high rotating speed of 900 r/min obtained from the proposed time domain analysis method and the numerical simulation are 1643.6 h and 1594.8 h, of which the maximum difference between the experimental results are 10.9 % and 7.6 %, respectively. This work will provide a theoretical guidance for improving the operation efficiency of air jet loom and lay a foundation for optimizing the fatigue life of the crankshaft.

PurposeThis study aims to address the challenges of robotic cloth folding, stemming from complex dynamics and high degrees of freedom. While existing learning-based approaches have shown promise, they often suffer from limited generalization across diverse fabrics and require extensive real-world data. To address this gap, we propose a perception-centric strategy introducing HrcbamFolding, a dual-arm system that leverages a deep network to directly map visual inputs to the key manipulation points required. This approach simplifies the complex problems of state estimation and motion planning into a structured keypoint detection task, effectively bypassing the need for explicit physical modeling of the fabric.Design/methodology/approachThis study proposes HrcbamFolding, which combines a multiresolution neural network with a channel-spatial attention mechanism to spotlight task-critical fabric regions, thereby enhancing visual-perception accuracy and generalization across diverse materials. It further uses a grasp-pose prediction module that translates visual inputs directly into coordinated grasping and placement actions for each arm, which reduces motion-planning errors and improves execution efficiency. The framework is trained purely in simulation on rectangular fabrics before being assessed on three multistep folding benchmarks.FindingsThis study shows that in all three tasks, HrcbamFolding achieves greater execution efficiency than baseline methods. It also delivers higher folding accuracy in two tasks while maintaining competitive performance in the third. Despite being trained only on simulated rectangular cloth, the system generalizes well to real-world manipulation of nonrectangular garments such as T-shirts and shorts, requiring only minimal fine-tuning. The demonstration video is available at: https://www.youtube.com/@Jaui-g9j.Originality/valueThis study presents a practical dual-arm folding system featuring a novel perception architecture that yields both high accuracy and exceptional sample efficiency for sim-to-real transfer. By focusing on structural feature learning via multiresolution attention and direct action prediction, HrcbamFolding advances the state-of-the-art toward generalized and data-efficient robotic fabric manipulation, offering significant value for real-world automation.

This paper proposes an online wear monitoring and lifetime assessment method for textile needle hooks, based on yarn tension sensing, closed-loop tension control, and the capstan model. The yarn tensions on both sides of the yarn-needle wrap interface are measured in real time and used to estimate an equivalent friction coefficient, which serves as the monitoring index for wear evolution. Closed-loop average-tension control was employed to reduce variability in operating conditions and enhance the consistency of friction coefficient estimation. To improve robustness, the signal-processing pipeline includes tension-floor gating, ratio clipping, missing-data handling, outlier rejection, pre-filtering, and post-filtered differentiation. Wear-life determination is achieved through a baseline-referenced criterion that combines a relative threshold with persistence time, defining the life endpoint as the earliest sustained deviation from the steady-stage baseline, rather than isolated spikes. Experiments conducted on needle hooks of different quality grades demonstrate that the proposed method yields stable yarn-tension measurements, enables clear discrimination among wear states, and produces wear-life assessments consistent with offline microscopy observations. The aforementioned method is computationally lightweight and suitable for practical online wear monitoring, thereby enabling data-driven timing of needle replacement in looms.

Air-jet looms are widely used for weaving lightweight fabrics due to their outstanding high performance. To enhance the overall structural strength of air-jet looms and reduce operational vibration, dynamic analysis and structural parameter optimization of the loom frame system were carried in this study. First, after the structural designing and finite element modeling, the modal tests of the loom frame system were conducted. The modal results showed the high consistency between the simulation and experiment, confirming the accuracy of the dynamic model. Then, the dynamic characteristics and maximum stress data of the frame were obtained and analyzed through vibration tests and simulation calculations. The results indicated that the maximum deformation occurred at the middle of the beam while the maximum stress occurred at the connection between the lower beam and the wall panel. Moreover, the loom frame parameter optimization model was constructed based on the BA-HHO-SVR (Balanced Adaptive—Harris Hawks Optimization—Support Vector Regression) algorithm, demonstrating excellent learning and predictive capabilities. Eventually, the optimal combination of the frame structural parameters was obtained through above optimization algorithm. Furthermore, the effectiveness and reliability of the optimal combination were verified by finite element calculations. The vibration analysis method and optimization strategy proposed in this study provide valuable guidance for subsequent structural design and optimization of the high-end textile equipment.

At present, the relay nozzle with a single circular hole (S1) is commonly used in the air jet loom. The jet speed of the S1 relay nozzle is low and attenuates rapidly. To increase the jet speed of the S1 relay nozzle, a specially shaped relay nozzle (C1) was developed, which consists of a circular hole with radius r and four rectangular grooves of width w and the length l. The main purpose is to analyze the global sensitivity index of structural parameters to the maximum airflow velocity for the relay nozzle C1. First, numerous three-dimensional jets were simulated by changing the structural parameters of the relay nozzle using Reynold-averaged Navier-Stokes equation(RANS). Second, the nonlinear relationship between the structural parameters and the maximum airflow velocity was determined using support vector regression (SVR). Finally, the global sensitivity index of structural parameters was analyzed using the Sobol's method. The results show that the radius r has the largest influence on the airflow speed and the length l of rectangular groove has the least influence. It suggests that reducing the aperture and slot width can achieve the optimal layout of the relay nozzle C1.

This paper introduces innovative technology called ultrasonic vibration-electrical discharge assisted milling (UVEDAM). This technology incorporates a flexible electrode to improve the discharge efficiency using a specially designed tool. A specially developed high-frequency vibration spindle was incorporated into the machining process to combine electrical discharge milling (EDM) and ultrasonic vibration machining. UV-EDAM technology was compared with conventional milling (CM) to demonstrate its superiority. The cutting force, the electrode's and the machined workpiece's surface morphology, and discharge signals were analyzed for both methods. In addition, a UV-EDAM cutting force model in three dimensions was created, and several experimental verifications were carried out. The experiment's results showed that the model based on the finite volume method could accurately predict the cutting force. Simultaneously, the introduction of the specially designed tool based on copper foam electrode effectively improves the discharge efficiency and stability of EDM. Compared to CM, UVEDAM achieved a maximum reduction in surface roughness of 46 % and a reduction in cutting force of up to 33 %. Additionally, the percentage error in cutting force was relatively lower with UV-EDAM, indicating improved stability. This novel UV-EDAM technique, based on flexible electrodes, offered a precise and efficient approach for machining Inconel 718.

Carbon fiber reinforced polymer (CFRP) and Ti-6Al-4V alloy (Ti) stacks structures are increasingly utilized in aircraft load-bearing applications, where the quality of hole processing significantly impacts their connection performance. Conventional drilling (CD) methods for CFRP/Ti stacks often encounter challenges such as severe interface damage, high cutting forces, and excessive heat generation. This study investigates the application of ultrasonic-assisted drilling (UAD) on CFRP/Ti stacks by integrating finite element method (FEM) modeling with experimental approaches to analyze the machinability of these materials under different machining methods and parameters. A self-developed high-frequency vibration spindle was employed to process CFRP/Ti stacks using both CD and UAD techniques. The newly developed numerical model effectively elucidates the mechanical and thermal coupling involved in the machining process and the transfer behavior of temperature and stress at the laminate interface. The experimental results of cutting force, hole inlet and outlet surface morphology, inner wall condition, chips, tool wear, etc. under different machining parameters were studied and analyzed. To ensure the reliability of our findings, experimental data were validated against simulation results. The findings reveal that UAD, when optimal separation conditions are achieved, significantly reduces cutting forces and enhances the surface morphology of both the inner and outer hole surfaces. Specifically, UAD lowers cutting forces by 27.2 % and the damage coefficient (Fd) by 15.7 % compared to CD. Furthermore, the high-frequency vibration generated by UAD markedly extends tool life. The specially designed high-frequency vibration spindle successfully meets drilling requirements, thereby significantly improving the machining performance of CFRP/Ti stacks materials, and it provides a certain reference for practical industrial applications.

The air jet loom is an energy-intensive machine, it is significantly reducing air consumption of relay nozzles for saving energy of air compressor. This paper proposes a Convolutional Neural Network (CNN)-Attention regression model to predict air consumption of the relay nozzle, enhancing accuracy and efficiency with an Improved Football Team Training Algorithm (IFTTA). We initially presented the architectural CNN-Attention model for predicting air consumption of relay nozzles. Then, the hyperparameters of CNN-Attention model were automatically tuned using an IFTTA algorithm that imitates the collaboration in football team training. Finally, experimental validation was performed. The IFTTA-CNN-Attention model stands out with the lowest mean absolute error (MAE) of 0.8686, root mean square error (RMSE) of 1.1027, and the highest determination coefficient (R2) of 0.9941. An in-depth analysis of predicted data reveals that the outlet diameter is the most sensitive factor affecting the airflow rate, followed by inlet diameters and cone angle of the relay nozzle. This study’s findings contribute to optimizing design of relay nozzles, resulting in lower electricity usage and environmental impact in textile industry.

Fabric folding through robots is complex and challenging due to the deformability of fabric. Based on deconstruction strategy, we split the complex fabric folding task into three relatively simple sub-tasks, and propose a Deconstructed Fabric Folding Network (DeFNet), including corresponding three modules to solve them. (1) We use the Folding Planning Module (FPM), which is based on Latent Space Roadmap, to infer the most straight folding intermediate states from the start to the goal in latent space. (2) We utilize the flow-based approach, Folding Action Module (FAM), to calculate the action coordinates and execute them to reach the inferred intermediate state. (3) We introduce an Iterative Interactive Module (IIM) for fabric folding tasks, which can iteratively execute the FPM and FAM after every grasp-and-place action until the fabric reaches the goal. Experimentally, We demonstrated our method on multi-step fabric folding tasks against three baselines in simulation. We also apply the method to an existing robotic system and present its performance.

Graphene/cotton fibers show significant promise in wearable energy storage due to their low cost, porous structure, and exceptional integration ability into wearable systems. However, the eco-unfriendly reductants and standalone electric double-layer capacitor hindered their application. Herein, a green and rapid hydrothermal-electrodeposition method was proposed to fabricate polyaniline (PANI) decorated reduced graphene oxide (rGO)/cotton yarns without using any chemical reductants and oxidants. The PANI/rGO/cotton (PRC) yarn exhibited porous conductive network, structural controllability, and mechanical flexibility. Additionally, the PRC yarn electrode delivers a fast electron transport and ion migration, synergistic energy storage contribution, and a controllable capacitance (up to 81.2 mF cm−1 at 0.2 mA cm−1). The assembled yarn supercapacitor shows a good capacitance (19.8 mF cm−1 at 0.08 mA cm−1), excellent energy-power density (2.7 μWh cm−1 at 40 μW cm−1), and great capacitance retention. This green fabrication of PRC yarns brings new insights into the development of wearable energy storage.

Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to account for the crumpled configuration.Then, we insert the items and lift the bag for transport. We perform our method on a dual-arm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking actions compared to the quasi-static manipulation in the bagging task. We also show that our method generalizes to variations despite the bag's size, pattern, and color.

Porosity is one of the most serious defects in laser powder bed fusion (LPBF). Reducing porosity is essential to improve the mechanical properties of parts in high-end applications. It is found that the spatter dynamic status is closely related to the porosity, giving an idea to identify. In this study, we propose a novel approach for in-situ identification of porosity in the LPBF with spatter features. To achieve efficient and accurate detection, segmentation, and motion tracking of spatters from captured high-speed images during the LPBF process, YOLO and DeepSORT algorithms are employed. Subsequently, a two-staged attention-based recurrent neural network (TARNN) method is proposed to realize the classification of porosity. The input to TARNN consists of both static and dynamic spatter features extracted from every 10 consecutive frames. Leveraging the RNN architecture enables us to effectively exploit temporal information. Moreover, we introduce an attention-aware linear layer and an attention-based RNN to enhance the extraction of representative features related to porosity characteristics. Through the analysis of the attention mechanism, we can explicitly assess the importance ranking of input features, providing a deeper understanding of spatter features. Experimental results demonstrate the superior performance of the proposed TARNN, with an accuracy of 99.50 % at an inference time of 6.5 milliseconds. The proposed method offers a promising avenue for advancing the understanding and characterization of porosity using spatter features in the LPBF process.

The fluctuations in process parameters, as a random phenomenon in the laser powder bed fusion (LPBF) process, is occasional. These subtle variations can lead to defects such as porosity if not monitored and corrected promptly. Therefore, monitoring the stability of the molten pool and small changes in process parameters is significant. For process monitoring, the commonly used Transformer architectures have demonstrated remarkable performance in single-sensor signal processing. However, as the core component of the Transformer, the self-attention mechanism has limitations in data fusion of multi-sensor signals. To this end, a novel Transformer-based deep learning method associated with the cross-attention mechanism (Trans-Cross) is proposed. Trans-Cross leverages the collected photodiode and acoustic signals to monitor the fluctuation of process parameters as well as the quality of parts. Specifically, the cross-attention mechanism is introduced to enhance representative feature extraction from photodiode and acoustic signals, exploiting their complementary information. To better characterize the raw signals, an improved adaptive variational mode decomposition (VMD) algorithm is proposed for data preprocessing. The proposed Trans-Cross achieves impressive prediction accuracies of 97.41 % for identifying the fluctuations in the process parameters and 99.73 % for part quality classification. Trans-Cross exhibits superior performance and strong robustness when dealing with constraints such as limited input signal length and training data ratio. These results validate the feasibility of the proposed method in accurately identifying the fluctuations in process parameters as well as the part quality. This work provided data guidance to enhance the stability of the LPBF process.

In robotic arm manipulation, folding clothes is highly challenging due to the garments' inherent infinite degrees of freedom. Our designed system can unfold garments from any configuration and automatically center them in the workspace during folding, thereby minimizing constraints on the robotic arm's motion range. High-quality garment unfolding significantly enhances folding efficiency. Therefore, this study introduces a novel action strategy, “Lift&Drag,” which involves high-speed dynamic actions to adjust garments from any state to a prede-fined configuration, ensuring superior unfolding. To bridge the simulation-reality gap and improve fabric keypoint detection ac-curacy, we collected extensive real-world fabric images, creating a dataset of approximately 2,000 images. This dataset notably enhances the performance of fabric folding keypoint detection. Experimental results show that the method reliably performs in real-world settings, achieving 91.3 % unfolding coverage and a 92.8 % folding success rate, particularly with heavily wrinkled t-shirts.

In the real world, unfolding the garment to a specific plane direction and vertical facing can make the downstream task, i.e. folding, more convenient and effective. Then, we propose a policy that strategically selects action between the dynamic fling and quasi-static pick&place to effectively unfold any arbitrarily configured garment into two specific orientations with maximum coverage. In this work, we define a novel factorized reward function comprising garment coverage and two orientations (plane direction and novel proposed vertical facing) to train the policy. Moreover, we employ two prior knowledge modules: the Value Attention Module and the Action Optimized Module. The former assigns higher value weights to the key points of the garment, while the latter optimizes the lifting height and the flinging speed. Experimentally, we demonstrate the performance against three baselines in simulation. Our approach achieves two specific orientation configurations, especially in the vertical facing, which is not addressed by other methods. Furthermore, compared to the SOTA, we achieved approximately 4.0% and 17.6% improvements in coverage and manipulation steps, respectively. Our method is also finetuned on a real dual-arm robot to narrow the gap between the real world and simulation. Finally, our method is applied to a whole folding task as the initial unfolding step and demonstrates its performance.