As one of the most vital metal bent-tube defects, wrinkling not only compromises the aesthetics of the finished product but also weakens the structural integrity of the tube, rendering it unsuitable for its intended application. Mainly, due to the complex constraints and interfacial behaviors between the dies and the tube, in-process monitoring and prediction of wrinkling occurring in the tube inner side is challenging, thereby limiting the cross-sectional control accuracy and efficiency. To break through this limitation, a digital-twin-enabled method is developed to achieve online monitoring and prediction of the wrinkling defect during tube bending. The strain sensors, serve as a bridge connecting the physical entity and the digital one, are employed inside the tube. Due to forming dies interference, the strain sensors can only be arranged at specific positions on the tube inner surface. As a countermeasure, an FC-GAN (Forward Convolution-Generative Adversarial Networks) based multi-fidelity method is proposed by utilizing combined data from measured results in the straight part and the simulation results in the bent part of the tube. Furthermore, a time adaptation module is incorporated to enhance the prediction efficiency, enabling online detection of wrinkling defects. The experimental verification is conducted using aluminum tube bending as a typical case. The verification demonstrates that the proposed method can provide superior prediction accuracy and efficiency as compared with other multi-fidelity fusion methods.

A biomechanical evaluation method for bone arthrosis morphology based on reconstructed dynamic kinesiology (RDK) is proposed. The hip joint is a ball-and-socket joint, morphologically characterized by an acetabulum with a nearly spherical concavity and uniform curvatures, where Gaussian curvature exhibits negative characteristic. Subsequently, RDK of bone joint morphology is developed, offering detailed anatomical and kinematic insights. The hip joint is taken as a verification instance, where a precise biomechanical evaluation of bone arthrosis morphology is simulated through finite element analysis (FEA). Latin Hypercube sampling (LHS) with the criterion of maximizing the minimum distance enhances uniformity and representation. The response surface is subsequently constructed by Kriging interpolation, significantly enhancing computational efficiency and FEA accuracy. Innovatively, a stress contour statistical histogram of load transfer is presented to quantitatively analyze the stress lines, supplying support for biomechanical evaluation, which is essential for accurate hip replacement planning. The instance indicates that the proposed RDK facilitates accurate biomechanical evaluations for bone arthrosis morphology, providing a critical theoretical foundation for conceptual design of ergonomic wearable devices, as well as optimization of replacement surgeries.

Point cloud registration for evaluating the shape of 3D bent tubes is a preferred method for improving the forming quality and reducing fabrication costs. In this process, large nonlinear deformations, smooth regions, and low overlap result in massive outliers, making accurate registration for forming iterative optimization a challenging yet indispensable technique. We propose a new registration method based on implicit structural feature compatibility to predict the global-local rigid transform for multi-unit 3D bent tubes, called ISFC. In the two-stage tactic of ISFC for the alignment of the cross-source point cloud, the rigid compatibility in overlap regions and non-rigid compatibility in deformation regions are discriminated by the soft-distance consistency metric for global correspondence initialization. A new implicit axial structure constraint is established by evolution from the surface point to the interior based on the grassfire analogy, which joins faithful anchor points to generate a robust global correspondence hypothesis. Based on the global pose transformation, an innovative multipliers method named PC-ADMM is proposed for sequential local registration, which introduces a processing constraint into the optimization objective of the Lie group to refine tube unit transformation. The robustness and accuracy of the proposed method are confirmed by extensive registration experiments on synthetic and realworld tube datasets.

In response to the growing demand for small-batch bending tube production, traditional bending dies require separate customization for each tube size, resulting in extended design cycles and high costs. To meet bending requirements for tubes of different diameters using a single mandrel, a novel adjustable diameter mechanism (DAM) and its optimization design method are proposed. Initially, the DAM based on a planetary bevel gearscrew transmission set is developed for bending tubes of varying diameters. Subsequently, a domain knowledge-integrated optimization design framework is introduced. To reduce the cost of acquiring training samples for training surrogate models, a monotonicity-constrained neural network based on cascade boosting architecture (CB-MCNN) is introduced that enhances prediction accuracy while maintaining monotonicity. To improve the optimization speed and quality of Evolutionary Algorithms (EAs), a domain knowledge-guided EA (DK-EA) method is proposed, incorporating domain knowledge into the population initialization phase. The results indicate that: (1) CB-MCNN outperforms traditional methods and shows excellent performance on smallsample datasets. (2) DK-EA accelerates optimization processes and produces better outcomes. As a result, the domain knowledge-integrated optimization design framework enables the DAM to achieve a wider diameter variation range and enhanced reliability. The optimized DAM demonstrates the capability to bend tubes with diameters of 46-60 mm.

Purpose This work aims to provide a rapid robust optimization design solution for parallel robots or mechanisms, thereby circumventing inefficiencies and wastage caused by empirical design, as well as numerous physical verifications, which can be employed for creating high-quality prototypes of parallel robots in a variety of applications. Design/methodology/approach A novel subregional meta-heuristic iteration (SMI) method is proposed for the optimization of parallel robots. Multiple subregional optimization objectives are established and optimization is achieved through the utilisation of an enhanced meta-heuristic optimization algorithm, which roughly employs chaotic mapping in the initialization strategy to augment the diversity of the initial solution. The non-dominated sorting method is utilised for updating strategies, thereby achieving multi-objective optimization. Findings The actuator error under the same trajectory is visibly reduced after SMI, with a maximum reduction of 6.81% and an average reduction of 1.46%. Meanwhile, the response speed, maximum bearing capacity and stiffness of the mechanism are enhanced by 63.83, 43.98 and 97.51%, respectively. The optimized mechanism is more robust and the optimization process is efficient. Originality/value The proposed robustness multi-objective optimization via SMI is more effective in improving the performance and precision of the parallel mechanisms in various applications. Furthermore, it provides a solution for the rapid and high-quality optimization design of parallel robots.

Purpose A biomechanical design method of lightweight full contacted insole based on structural anisotropy bespoke (SAB) is proposed, which can better redistribute the stress distribution of SAB designed personalized insole. Design/methodology/approach The reconstructed joint biomechanics are simulated using finite element analysis (FEA) to develop a lightweight full contact insole. Innovatively, the anisotropic properties of the triply periodic minimal surface (TPMS) structure, which contribute to reducing insole weight, are considered to optimize stress distribution. Additionally, porosity and manufacturing time are included as design objectives. To validate the lightweight insole design, FEA is employed to simulate the stress distribution of the ergonomic insole, which can be fabricated by additive manufacturing (AM) with TPU. Findings With a little 0.924% loss in porosity, the maximum stress of lightweight SAB designed insoles is extremely decreased by 19.2917%. Originality/value The biomechanical design of the lightweight full contact insole based on SAB can effectively redistribute stress, avoid stress concentration and improve the mechanical properties of the ergonomic individual insole.

This paper presents a load-bearing optimization method for customized exoskeleton design based on kinematic gait reconstruction (KGR). For people with acute joint injury, it is no longer probable to obtain the movement gait via computer vision. With this in mind, the 3D reconstruction can be executed from the CT (computed tomography) or MRI (magnetic resonance imaging) of the injured area, in order to generate micro-morphology of the joint occlusion. Innovatively, the disconnected entities can be registered into a whole by surface topography matching with semi-definite computing, further implementing KGR by rebuilding continuous kinematic skeletal flexion postures. To verify the effectiveness of reconstructed kinematic gait, finite element analysis (FEA) is conducted via Hertz contact theory. The lower limb exoskeleton is taken as a verification instance, where rod length ratio and angular rotation range can be set as the design considerations, so as to optimize the load-bearing parameters, which is suitable for individual kinematic gaits. The instance demonstrates that the proposed KGR helps to provide a design paradigm for optimizing load-bearing capacity, on the basis of which the ergonomic customized exoskeleton can be designed from merely medical images, thereby making it more suitable for the large rehabilitation population.

This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins (HDT). In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering, the HDT meaning hybridization of physical and digital domains, including deformation and energy efficiency can be built, where the essential parameters can be perceptually predicted in advance, by virtue of the fusion of physical sensors and digital information. The long short term memory (LSTM) can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks, thereby laying a foundation for the HDT. The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation. The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy. The manufacturing efficiency and comprehensive costs are accounted as consideration factors, which are perceptually predicted via LSTM. The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation. The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling (FDM). The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes. The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine (CMM) and 3D optical scanner. The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption, which have been verified with a three-way solenoid valve element, in which the maximum deformation was reduced by 39.78% and the mean absolute percentage error for perceptual prediction was 3.76%.

The vibrations of high-speed-elevators (HSEs) significantly impact the comfort of elevator rides, with horizontal vibrations (HV) being the most sensitive to bodies. Customized elevator products are characterized by high-speed capabilities, customization options, and complex operational conditions. These characteristics often lead to challenges such as difficulties in predicting HV, low prediction accuracy, and limited reusability of prediction models and data. In order to mitigate HV in elevators, its crucial to implement HV prediction during the design phase. To address this need, this paper proposes a method for predicting HSEHV based on a Transferred Digital Twin (TLDT) model. The HSE's design is physically extracted, and an elevator Digital Twin (DT) model with a digital layer is constructed. This model includes product geometry models, dynamics models, and simulation models. A transfer learning (TL) model is developed to integrate simulation data generated by the digital layer with measurement data acquired from the data interface layer, resulting in high-capacity and high-fidelity DT data. Using the support vector regression (SVR) method, a high-dimensional nonlinear HSEHV prediction model is established, and is trained and optimized using DT data to achieve data-driven HSEHV prediction. The validity of this approach is confirmed using measured data from an elevator test tower. Results indicate that the SVR-based TLDT model achieves the highest accuracy and the lowest absolute error values for both peak-to-peak and A95 vibration acceleration when compared to other models such as NN, KNN, decision tree, and lasso models. Furthermore, by incorporating DT data, the model accuracies improve by 16.9%, 14.5%, 8.6%, 14.1%, and 8.6%, and 12.3%, 12.4%, 19.7%, 7.6%, and 8.7%, respectively, for the mentioned models. Finally, the HV of KLK2 elevator car system is predicted. When compared with simulation analysis and the conventional SVR prediction model, obtained results through the proposed method closely align with the measured values.

We propose a novel and effective deep learning method, called deep pattern matching, for predicting the energy consumption of complex structures, which helps designers to develop ecological solutions with minimal fabrication energy for additive manufacturing. This new method does not necessitate the real energy consumption values of complex structures for training the prediction model, substantially reducing the cost of training data collection, which can be prohibitively expensive. This novel method exploits simple structures whose real energy consumption values are far cheaper to measure, by matching the similar infill pattern of complex structures from simple structures, and then approximating the energy value of the pattern in the complex structures by the matched one in training phase. This effective algorithm is designed dynamically for allowing us to match patterns with arbitrary shapes. We evaluate our deep pattern matching algorithm on various complex structures, where the highest total energy accuracy is up to 97.3%. The extensive empirical results confirm the effectiveness and robustness of the proposed method, exhibiting a great potential to advance the real usage of deep learning models for energy consumption prediction of complex structures in ecological additive manufacturing.

Vibration signals play a crucial role in mechanical fault diagnosis. However, they are susceptible to various noise disturbances, presenting challenges for reliable fault detection. We propose an end-to-end Cross-task Attention Joint Learning (CTA-JL) model that concurrently denoises and diagnoses faults in noisy signals. This model utilizes a multi-task encoder, composed of task-shared and task-specific feature encoding units, along with a feature information exchange unit with a Cross-task Attention (CTA) mechanism, fostering information exchange across different tasks. By collectively executing diagnosis and denoising tasks and sharing valuable task information, the model enhances prediction accuracy and denoising performance. Under three noise conditions of SNR = −9 dB, −6 dB, and −3 dB, the prediction accuracy of CTA-JL on the rolling bearing datasets reached 91.38%, 97.95%, and 99.69%, respectively. Meanwhile, the result on elevator guide system datasets reached 87.31%, 95.58%, and 99.64%

This paper presents a topology optimization approach for parameterized lattice structures subjected to thermomechanical coupled loads. The proposed approach aims to minimize the compliance of lattice structures while satisfying volume fraction constraints and accurate temperature constraints. A thermomechanical coupled optimization model containing a heat transfer model and a thermoelastic model is utilized for accurate modeling, and the distribution of the temperature field is related to design variables. Numerical homogenization is employed to calculate the effective properties of parameterized lattices, and polynomial interpolation models are used to replace numerical homogenization methods during optimization iterations to reduce computational costs. The proposed method is demonstrated through examples involving battery packs, L-brackets, and machine tool headstocks. Numerical verification results show that the proposed method significantly reduces the compliance of the designed structures compared to traditional solid designs and precisely meets temperature constraints.

AbstractThis study presents an energy consumption (EC) forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning (FTL). To predict the EC of manufacturing products, particularly from scale-down to scale-up, a general paradigm was first developed by categorizing the overall process into three main sub-steps. The operating electrical power was further formulated as a combinatorial function, based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and EC. Parallel-arranged networks were constructed to investigate the impacts of fabrication variables and devices on power. Considering the interconnections among these factors, the outputs of the neural networks were blended and fused to jointly predict the electrical power. Most innovatively, large artifacts can be decomposed into time-dependent laser-scanning trajectories, which can be further transformed into fusionable information via neural networks, inspired by large language model. Accordingly, transfer learning can deal with either scale-down or scale-up forecasting, namely, FTL with scalability within artifact structures. The effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed fusion. The relative error of the average and overall EC predictions based on FTL was maintained below 0.83%. The melting fusion quality was examined using metallographic diagrams. The proposed FTL framework can forecast the EC of scaled structures, which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.

Optimizing structural designs, especially for complex systems like turbine blade cooling structures, requires efficient strategies for handling categorical configurations alongside computationally expensive simulations. This article presents a Bayesian optimization strategy tailored for integrated tasks involving categorical configurations and high-dimensional continuous design variables. A Gaussian process with Con-Cat kernel is proposed in order to merge response datasets from diverse configurations effectively, capturing inter-configuration and intra-configuration correlations seamlessly. Additionally, a supervised dimension-reduction scheme is developed based on subspace activation, utilizing a half-Cauchy distribution. Remarkably, the Con-Cat kernel represents a generalization of standard kernels, achieving equivalence in scenarios solely involving continuous variables. The subspace activation scheme enhances surrogate modelling performance without introducing extra model parameters, which is particularly beneficial for sparse datasets. Numerical evaluations, including three mathematical functions, a supported beam problem, and turbine blade cooling structure design optimization, demonstrate the superiority of the proposed Bayesian optimization strategy, exhibiting up to an 85% improvement over five alternative approaches, especially in scenarios with sparse data.

Depth estimation provides an alternative approach for perceiving 3D information in autonomous driving. Monocular depth estimation, whether with single-frame or multi-frame inputs, has achieved significant success by learning various types of cues and specializing in either static or dynamic scenes. Recently, these cues fusion becomes an attractive topic, aiming to enable the combined cues to perform well in both types of scenes. However, adaptive cue fusion relies on attention mechanisms, where the quadratic complexity limits the granularity of cue representation. Additionally, explicit cue fusion depends on precise segmentation, which imposes a heavy burden on mask prediction. To address these issues, we propose the GSDC Transformer, an efficient and effective component for cue fusion in monocular multi-frame depth estimation. We utilize deformable attention to learn cue relationships at a fine scale, while sparse attention reduces computational requirements when granularity increases. To compensate for the precision drop in dynamic scenes, we represent scene attributes in the form of super tokens without relying on precise shapes. Within each super token attributed to dynamic scenes, we gather its relevant cues and learn local dense relationships to enhance cue fusion. Our method achieves state-of-the-art performance on the KITTI dataset with efficient fusion speed.

Purpose The layer section of laser additive manufacturing (AM) can be rasterized. Subsequently, the rasterized layer section can be converted into sparse matrix. However, large storage space is occupied due to the high manufacturing resolution. In order to reduce the storage space, the purpose of this research is to propose a lossless compression method to compress the sparse matrix. Design/methodology/approach A lossless compression method for additive manufacturing is proposed. According to manifold and irregularity feature of the object of laser AM, a lossless compression method called continuous rows compressed storage (CRCS) based on continuous rows is innovatively proposed. In particular, the better direction strategy of compression method is selected based on the side-projected area per layer. Findings Take human teeth as an example, compared with compressed sparse row (CSR), the CRCS has advantage up to 98.88% in storage space. Compared with block compressed sparse row (BCSR), the CRCS has advantage up to 60.04% in storage space. Originality/value The proposed CRCS could be employed to compress the sparse matrixes of rasterized layer sections of laser AM. Compared with common lossless compression method of sparse matrix, the compression ratio of CRCS is greater. CRCS is propitious to reduce the storage space usage, thereby improving transmission efficiency.

This paper presents a predictive defect detection method for prototype additive manufacturing (AM) based on multilayer susceptibility discrimination (MSD). Most current methods are significantly limited by merely captured images, disregarding the differences between layer-by-layer manufacturing approaches, without combining transcendental knowledge. The visible parts, originating from the prototype of conceptual design, are determined based on spherical flipping and convex hull theory, on the basis of which theoretical template image (TTI) is rendered according to photorealistic technology. In addition, to jointly consider the differences in AM processes, the finite element method (FEM) of transient thermal-structure coupled analysis was conducted to probe susceptible regions where defects appeared with a higher possibility. Driven by prior knowledge acquired from the FEM analysis, the MSD with an adaptive threshold, which discriminated the sensitivity and susceptibility of each layer, was implemented to determine defects. The anomalous regions were detected and refined by superimposing multiple-layer anomalous regions and comparing the structural features extracted using the Chan-Vese (CV) model. A physical experiment was performed via digital light processing (DLP) with photosensitive resin of a non-faceted scaled V-shaped engine block prototype with cylindrical holes using a non-contact profilometer. This MSD method is practical for detecting defects and is valuable for a deeper exploration of barely visible impact damage (BVID), thereby reducing the defect of prototypical mechanical parts in engineering machinery or process equipment via intellectualized machine vision.

This study presents a robustness optimization method for rapid prototyping (RP) of functional artifacts based on visualized computing digital twins (VCDT). A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built, where thermal, structural, and multidisciplinary knowledge could be integrated for visualization. To implement visualized computing, the membership function of fuzzy decision-making was optimized using a genetic algorithm. Transient thermodynamic, structural statics, and flow field analyses were conducted, especially for glass fiber composite materials, which have the characteristics of high strength, corrosion resistance, temperature resistance, dimensional stability, and electrical insulation. An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP. Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution. A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT. Moreover, manufacturability was verified based on a thermal-solid coupled finite element analysis. The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.

This paper presents an in situ monitoring method for numerical controlled manufacturing of large conceptual prototype based on multi-view stitching fusion (MSF). With the extensive application of numerical controlled manufacturing, like additive and subtractive manufacturing (ASM), the forming size tends to increase. Nevertheless, limited by optical lens and affiliated machine vision parameters, it is difficult to avoid the existence of blind areas when detecting defects including scratch, crack, pore, and thermal deformation by machine vision. To overcome the obstacles, multi-view information, including visible and depth sequences, were captured during the manufacturing progresses to expand monitoring scope. The overlapped regions of various frames were confirmed by stitching fusion of multi-views with homography matrix. The flaws were detected by superposing anomalous regions of multi-views which were extracted via multi-dimension synthetical anomalous degree map. The finite element analysis (FEA) was conducted to extract susceptible regions and locally adjust threshold. Inspired from digital twin visualization of Boundary representation (B-Rep), the numerical controlled manufacturing process of large textureless conceptual prototype can be quantized and visualized synchronously. The numerical outcomes were evaluated and verified that the MSF was effective in distinguishing wide-range defect regions of in situ large parts, thereby generating feedbacks. The physical experiment was implemented on W12 engine upper block and thin-walled shell of engineering machinery with laser stereo lithography appearance (SLA). The physical experiment prove that it is of great significance for fabricating large-scale high reflective objects with low defect and high efficiency, further for prototype verification of conceptual design.

Pose estimation is an essential technology for industrial robots to perform precise gripping and assembly. The state-of-the-art deep learning-based approach uses an indirect strategy, i.e., first finding local correspondence between the 2-D image and 3-D model, and then using the perspective-n-point and RANSAC methods to calculate the poses of ordinary objects. However, the metal parts in industry are reflective and textureless, making it difficult to identify distinguishable point features to establish 2-D–3-D correspondences. To address this problem, in this article, we propose a novel deep learning based two-stage method for pose estimation of reflective textureless metal parts, which accurately estimates the target pose using monocular red green blue (RGB) images. Since contours play an important role in both keypoints prediction and pose estimation stages, our method is named ContourPose. First, an additional contour decoder is adopted to implicitly constrain the keypoints prediction in the former stage, which improves the accuracy of the keypoints prediction. Then, the predicted contour of the previous stage is taken as geometric prior that is used to iteratively solve for the optimal pose. Experiments indicate that the proposed approach for reflective textureless metal parts has a significant improvement over the state-of-the-art approaches.