Assembly retrieval techniques enable product-level model reuse, and are relevant for accelerating product design and improving industrial efficiency. Previous work involved describing the assembly using part parameter vectors and solving the problem using the modified Hausdorff distance (MHD). However, this assembly description approach does not provide an in-depth consideration of the part composition of the actual product, nor does it provide a convincing description of the choice of distance paradigm for Hausdorff distance. We proposed a compressed part-parameter vector model to represent the assembly. By searching and removing identical and standard parts that exist within the assembly and between matched assemblies, the original part parameter vector model is compressed into an assembly model that requires less representation space, thereby directly reducing the time consumption of the retrieval process. At the same time, we used a correction coefficient to correct the retrieval results to prevent the model distortion caused by the deletion of parts in the assembly and to affect the retrieval accuracy. In addition, in order to further reveal the relationship between the assembly compression ratio and the time-improvement efficiency, we developed a rule-based approach to generate more assemblies that meet the experimental requirements, which compensates for the lack of sufficient or non-compliant assemblies in the real production. Regarding the choice of distance metrics, we studied the commonly used metric paradigms of Euclidean and Manhattan distances in a unified MHD framework. Relevant experimental verifications have demonstrated that our method can effectively improve the efficiency of assembly retrieval.

An excellent machining performance of electroplated diamond wheel is suitable for trimming process of CFPRs (carbon fiber reinforced plastics) where unnecessary portions of material are cut off. However, chip loading is one of serious problems caused with grinding wheels during the process. In this study, through the experimental investigation under various conditions, it is demonstrated that machining conditions determine the degree of chip loading on the wheel. Experimental results show that the chip loading immediately reaches at very early stage of process and is obstructive to the continuous trimming process. In addition, an in-process laser dressing is proposed as a solution to suppress and the chip loading and to enable highly efficient machining of CFRPs. Influences of laser irradiating parameters are investigated and the feasibility of proposed method is examined.

Motorcycle assistance systems have been an increasing focus of research in recent years to promote safer riding practices and mitigate motorcycle-related accidents. While there is substantial interest in evaluating the beneficial aspect of these systems in improving driving safety, it is equally essential to clarify their unintended adverse effects on riders' driving behaviors and the overall interaction between riders and such systems. In this study, we designed alert and continuous real-time feedback HMIs that convey information about lateral deviation from the lane center based on our previous research results. Experiments were then conducted on a riding simulator to investigate whether variations in information presentation could alter rider-system interaction and ultimately affect riding performance. Fifteen young motorcyclists (mean age = 22.1 ± 1.87 years) were exposed to dummy and risky scenarios at uncontrolled intersections while interacting with alerting or real-time feedback systems separately. A path model was constructed, and the impacts of HMIs on riders' reaction time, information observation time, weighted workload, and risk of collision were clarified. Cluster analysis was subsequently utilized to categorize participants into confident and methodical riders, predicated upon their riding styles. Independent t-tests revealed that, except for Obs-time, there were no significant differences between confident and methodical riders in RT and TTCmin when using an alert HMI. However, when real-time feedback was displayed, significant differences were found for TTCmin and Obs-time. In general, participants exhibited superior performance with the Alert HMI, characterized by a shorter RT (1 second), longer TTCmin (0.79 seconds), reduced information observation time (0.42 seconds), and lower perceived workload compared to when real-time feedback was used. The authors believe that this research will contribute to the development of more effective and rider-centric HMI designs.

The core extraction mechanism is one of the key components of a mold, and its design accuracy not only affects the production quality of molded parts, but also plays a key role in the service life of the mold. The traditional core extraction mechanism design process has low automation, tedious steps, difficult work, and can lead to a series of problems such as long mold design cycle and low production efficiency. Therefore, this study introduces intelligent CAD technology into the core extraction mechanism design process and develops a complete human-computer interaction design interface for the inclined column side core extraction mechanism, which is applicable to both die-casting and injection molds and greatly improves the efficiency of mold design. Firstly, we designed the corresponding feature recognition algorithm by two methods of "graph-based feature recognition" and "surface-based feature recognition", and developed the intelligent feature recognition subsystem. Secondly, based on the feature data of the molded parts and the principle of core extraction mechanism design, the parametric design module of each component of the lateral core extraction mechanism of the inclined guide pillar is developed. Finally, the reliability and rationality of the parametric drive design of the system are verified by the CAE motion simulation module of UG. The system is applicable to the design of core extraction mechanism of die-casting and injection molds, which greatly simplifies the tedious work of modeling the core extraction mechanism of inclined guide pillar and improves the efficiency of mold design.

Design for Manufacturing (DfM) is becoming increasingly important in computer-aided design (CAD) due to the growing complexity of products. Automatically evaluating design rules for manufacturability from CAD dimensions and geometry is one effective approach for DfM. However, implementing automated design-rule-checks requires coding complex programs on CAD systems and the expertise to implement them, which make it difficult to adopt the approach, or implement product-specific design-rules. In this study, to mitigate this barrier, we developed a visual programming environment for the design-rule programs and a graph neural network (GNN) based recommender system that assists users to create the visual program. The proposed visual programming environment allows users to make design rules by placing and connecting shape recognition and measurement function nodes. This simplifies and improves the programming interface. Furthermore, the proposed recommender system predicts the subsequent function node that users will append, which reduces the user’s cognitive load of choosing the right function nodes. By employing proposed GNN architecture in the visual program recommender system, the input-output relationships between the function nodes and their input arguments are naturally taken into account to produce accurate recommendations. We evaluated the performance of the proposed recommender in the off-line experiment and the experiment in real-world use. The results in both experiments demonstrate that the proposed GNN based visual program recommender system can suggest the function nodes in sufficient accuracy, which contributes to improved productivity of implementing automatic CAD design-rule-checks.

The Direct Energy Deposition (DED) method in wire feed additive manufacturing (AM) has been attracting attention in the industrial field mainly because of its flexibility in shaping and speed of fabrication. Deposited metal accuracy is more susceptible to the effects of gravity and surface tension in molten pool than other AM methods such as Powder Bed Fusion (PBF) which leads to limitations in terms of accuracy and surface quality. Therefore, a novel deposition method using wire material and a hot-wire method as the main heat source is developed. In the hot-wire method, an electric current is passed through the wire and Joule heating occurs in the wire. The objective of this study was to verify the feasibility of a process for continuously depositing metal by applying Joule heating to the wire to improve accuracy and suppress bead width spreading. The first step of research consisted of the fabrication of the experimental device followed by a bead-on-plate test using the device. The wire was melted by Joule heating, and the relationship between electric power and the width of deposited metal was investigated. By using this method, deposition of metal was enabled using a bead with a width nearly equal to the diameter of the wire material used. This is expected to improve shape accuracy.

A novel approach has been proposed to predesign to-be-modified tooth surface of spiral bevel gear with face milling manufacturing method, based on the preset of deviation of discretized points along normal direction. Five typical target geometry topographies have been established to achieve modification of directions of tooth trace, tooth profile and bias. Three typical solving algorithms, including linear and nonlinear methods, have been applied to identify the machine-tool settings to achieve the modification of geometry topography. The instability of numerical values resulting from ill-conditioning problem of the Jacobian matrix using singular value decomposition (SVD) has been investigated. Truncated singular value decomposition (TSVD) has been applied to void the ill-conditioned problem. The Levenberg-Marquardt (LM) was used to solve the machine-tool settings with iteration steps by controlling descent direction and iteration step size. The residual root-mean-square error (RMSE) of the modified tooth surface was computed to evaluate the degree of closeness between the modified tooth surface and predesigned target tooth surface. The results indicate that the maximum residual RMSE with LM algorithm is smaller than 2 μm, also far smaller than machining error. The solved machine-tool settings can be used for practical machining of spiral bevel gear.

In human-human communication, humans observe each other’s behavioral actions, and infer each other’s internal states such as intentions and emotions to build relationships. In order to establish relationships between humans and robots, robots need to recognize human actions and infer human intentions as well as to indicate robot’s own internal states to be perceived by humans. The studies in robots’ internal states indications are based on the idea of giving robots understandable human-like characteristics that allow human to infer the internal states and encourage the anthropomorphizing in interaction. However, it lacks a basic exploration from the cognitive perspective that how humans interpret robots body languages, and from what kind of bodily movements humans perceive and infer robots’ internal state. In this paper, we designed and developed a CG character to investigate what cues humans take in intention inference. The CG character was of independent and moveable head, eyes, and right arm. The intention inference task was as simple as predicting which cup the character would grasp. We further analyzed the participants’ gaze points in the experiment obtained by an eye tracking device. The results suggested that the CG character’s eyes (gaze) have the strong impact in intention inference, and that humans tend to gaze at CG character’s eyes and take the gaze of the CG character as the cue.

Project schedule management, which involves generating and revising a schedule, is a critical decision-making process in uncertain environments. However, methods that can be used in project schedule management, particularly for large and complex projects, have so far been insufficiently studied. We believe that addressing this issue is essential to enable this process to be successfully adapted to practical projects. This study proposes a scheduling method that can generate a robust schedule insensitive to delays. The proposed method accurately evaluates schedule robustness using simulations, even for complex projects involving multiple decisions. As the simulation element of this process can be time-consuming, particularly for large-scale project instances, it is necessary to accelerate the evaluation process and ensure the search is performed efficiently. The proposed method reduces the computational time required for the evaluation process by employing parallel processing on a graphics processing unit (GPU) and utilizing simple simulations. To search for various solutions efficiently, the kick operation was developed that is specifically tailored to address the target problem. Numerical experiments confirmed the fundamental properties of the proposed method and demonstrated its effectiveness in comparison with previously employed methods in this area.

The workforce scheduling problem for cell production lines has been recognized as one of the significant issues in manufacturing systems. In most of previous studies, complex workers’ operations in a cell such as traveling times of multi-skilled operators in another cell to their workplaces, have not been considered in the workforce optimization model. In this paper, we consider the simultaneous optimization of the product input sequence and workforce assignment scheduling problem for multi-stage multi-item cell production lines. The problem is formulated as a resource-constrained project scheduling problem (RCPSP). We propose an RCPSP formulation of the scheduling problem for multi-product cell production lines where the traveling time of operators and products are considered. The exact solution of the RCPSP formulation is derived by using a general-purpose solver, Gurobi. The validity of the RCPSP model is verified by comparing it with a genetic algorithm (GA) using a commercial simulation software (Siemens Plant Simulation). Experimental results show that the derived exact solution of RCPSP is better than that of the solutions derived by the GA algorithm in the Siemens Plant Simulation.

Omnidirectional wheeled mobile robots, which can drive in any direction without turnabout, could help increase the efficiency of transportation in factories and warehouses. However, most such robots have low load capacity or unwanted vibrations due to the structure of their wheels. To overcome these problems, this paper proposes an omnidirectional mobile robot with a slidable base. The proposed robot achieves omnidirectional mobility using conventional wheels. Its design is based on our previously developed slidable wheeled omnidirectional mobile robot but with a simpler structure and fewer motors. The synthesis of the proposed robot, including the basic structure and basic movement patterns, is first presented. A kinematic model is established, and the singular configuration of the robot is analyzed. An original movement strategy for avoiding singularities is developed considering the periodic input and stabilization of the slidable base. Simulations are conducted to verify the effectiveness of the proposed strategy. Experiments are conducted on a prototype to verify the feasibility of the proposed robot and the algorithms used to realize unlimited omnidirectional movement. The results show that the robot can achieve omnidirectional movement without reaching the singularity. As for the stabilization algorithm, although the position of the slider does not return to the exact specified value due to errors in measurement and the robot itself, it is guaranteed that the slider will not exceed the limit.

The material behavior intrinsically possesses the aleatory uncertainty (i.e., the natural variability). Against uncertainty in a given data set of elastic material responses, this paper presents a data-driven approach to reliability-based truss topology optimization under the compliance constraint. We utilize the order statistics to guarantee the confidence level of the probability that the reliability on the compliance constraint is no smaller than the target reliability, and formulate the truss optimization problem in a bi-level optimization form. By using the duality of linear optimization, we recast this bi-level optimization problem as a single-level optimization problem, which can be solved with a standard nonlinear optimization approach. Numerical examples illustrate the validity, as well as the characteristic of optimal solutions, of the proposed method.

In model-based definition (MBD), product manufacturing information (PMI) semantic representation errors (SREs) are known to hinder the model readability, understandability, and processibility. Existing model quality test tools cannot detect PMI SREs efficiently and reliably; considering ontology's powerful abilities for semantic representation and logical consistency check, an ontology-based systematic methodology is proposed for their automatic and intelligent detection. Through the analysis of typical characteristics of PMI SREs’ content and style, four aspects of PMI SREs were identified, namely, the associated objects, structural integrity, parameters, and symbols. A three-dimensional PMI ontology (3DPMIOnto) framework was established, which included a PMI extraction module, a PMI ontology instance generation module, a rule-based inference module, and a message feedback module. In the Web Ontology Language, the object and data property of these ontology classes, including dimension annotation, geometric tolerance annotation, datum annotation, roughness annotation, and associated object classes, have been thoughtfully designed, which allows an automatic generation of ontology instances. The Semantic Web Rule Language was used to present four types of PMI SRE detection rules as per their classification. The application program interface of the commercial computer-aided design (CAD) software was used to extract PMI from the MBD model and imported to 3DPMIOnto to execute an intelligent inference process, where PMI SREs were filtered and fed back to the CAD software for redesign. A prototype system was developed and two cases study were performed. The results demonstrated the efficiency, feasibility, and openness of the proposed methodology.

This study investigates deposition of different aluminum alloys and the influence of added elements on their mechanical properties. Focusing on wire and arc additive manufacturing (WAAM), this study aimed to address the constraints of conventional fabrication, particularly in the context of multi-material applications. WAAM was utilized for the precise deposition of 5000 series aluminum alloys onto robust 7000 series counterparts, and the results were analyzed to characterize dissimilar aluminum alloy interactions. The deposition process employed an advanced WAAM system equipped with an industrial robot, dual-axis positioner, and cold metal transfer welder, coupled with A5356 wires and A5052/A7075 plates as materials. Controlled manipulation of wire feed speed and torch speed resulted in the formation of 61 layers in a spiral configuration. Elemental analyses, hardness tests, and tensile testing were performed, revealing a distinct transition layer, which indicates an interplay between dissimilar alloys during deposition. Heat treatment interventions not only unveil the recovery of 7000 series alloy hardness but also demonstrate the feasibility of multimaterial parts with enhanced functionalities. The resulting tensile strength surpasses common 5000 series aluminum alloy benchmarks. The study evaluates interfaces under accuracy considerations, highlighting the practical applications of dissimilar aluminum alloy additive manufacturing. The study reveals an intricate interplay of dissimilar aluminum alloys, pushing the boundaries of WAAM applications. The research not only expands the technical understanding of dissimilar aluminum alloys but also provides practical insights into harnessing WAAM's potential for innovative, functional additive manufacturing. We anticipate that the findings can drive advancements in alloy fabrication techniques and has implications for industries reliant on aluminum alloys across diverse applications.

The objective of this study was to develop and measure the effects of effective level crossing warning signs that raise the temporary stop ratio of automobile drivers ahead of level crossings. For this objective, first, four types of signs were designed based on accident analysis and psychological expertise. The experiment participants drove automobiles on driving simulators and repeatedly crossed the level crossings where one of the four designed signs were placed (or alternatively where no sign had been placed), and the temporary stop ratio ahead of level crossings was measured. Later, they were asked to respond to a questionnaire regarding their impressions (three categories of assessment: risk perception, attitude toward safety, and motivation for implementation) of the four types of signs. The experiment revealed that a sign of “gratitude” showing the message “Thank you for stopping” had a significantly higher temporary stop ratio in comparison to the other signs or to no sign during the condition in which a train is not passing along the tracks. Assessments of participants’ impressions of the “gratitude” sign indicated that, while risk perception was significantly lower than with other signs, the motivation to implement specific safety actions ahead of level crossings was significantly higher. It may be that the desire to implement safety behavior increases due to the norm of reciprocity as a result of showing courtesy to the experiment participants through an expression of gratitude, which results in an increase in the temporary stop ratio.

The kinesthetic illusion (KI) is a phenomenon in which transcutaneous vibration stimulation applied to a tendon induces the illusion of body movement. This phenomenon has potential applications in rehabilitation and virtual reality (VR). To effectively apply KI, precise vibration stimulation parameters (such as sine wave vibration with controlled frequency, acceleration, and pressing force against the tendon) must be delivered to various body parts. Thus, to realize this phenomenon, miniature vibrators must be developed and controlled. In this study, we developed a compact vibrator using a miniature voice coil motor to induce KI. We constructed a control system to regulate the target acceleration even in the presence of variations in the reactive forces from the tendon. Using the developed vibrator, we applied KI to the biceps brachii tendon and determined the optimal vibration stimulation conditions. The results revealed that the optimal vibration stimulation conditions for each subject’s biceps brachii muscle were related to the thickness of the upper arm. This result demonstrates the ability to determine guidelines for optimal stimulation conditions based on the physical characteristics of each individual participant. The increased ease of experiencing KI is expected to pave the way for rehabilitation using vibrators in wearable robots.

A home healthcare support device that can give passive exercise to keep the treated joint moving area has been desired. In the previous study, a Tetrahedral-shaped Flexible Pneumatic Actuator (it is called TFPA for short) using three extension type flexible pneumatic actuators (it is called EFPAs for short) was proposed and tested as a healthcare support device for wrist. As a core training machine while playing video game, the six-legged mobile robot using TFPAs that can translate and rotate as a movable cushion was also developed. However, the tested robot lacks the load capacity to carry the patient on board. In this paper, to increase the carrying load per plane unit area of the robot, a miniaturized TFPA was proposed and tested. The generated lifting force of the improved TFPA was investigated. As a result, it can be confirmed that the improved TFPA increases the generated lifting force by increasing the setting angle of the EFPA and it improves the rigidity due to miniaturization. The 18-legged mobile robot / seat simulator using miniaturized TFPAs was proposed and tested. It was confirmed that the improved robot can translate and rotate by changing driving pattern of each EFPA. By increasing the number of legs per plane unit to get enough carrying force of human, the estimated lifting force of the 18-legged robot is 2700N, that is 3.2 times compared to the previous one. In addition, both translational and rotational motions using the improved robot could be realized.

Non-circular face gear is a new type of gear with variable transmission ratio, which has the advantages of simple closure condition and good interchangeability. However, the lack of effective processing methods limits its application and development. A generating method using a disk cutter on a five-axis CNC machine was presented. According to the principle of gear shaping, the enveloping process of non-circular face gear using a disk cutter was explained and the linkage mathematical model of the machine tool was established. By analyzing the source of errors in the machining process, the tooth surface model of non-circular face gear with machining errors was established. The correctness of the processing method and mathematical model were verified by machining simulation. Furthermore, the influence of machining error on the tooth surface was analyzed by the tooth surface model. It is found that the X-axis and Z-axis error of the machine tool causes uniform deviation on the tooth surface, while the angle error around B-axis causes gradient distribution deviation from the top to the root on the tooth surface.

When optimizing the micro geometry in the development process of a gear set, the deflection behavior under load situations is important to predict the realistic component life span and acoustic behavior. Most common experimental deflection tests are extraordinarily complex and need special preparation of the whole drive unit to be able to measure the deflection precisely. On the other hand, purely simulation approaches need to be validated because the models are getting extensive through many boundary conditions between the different components of the drive unit. To address these challenges, this paper presents a method for measuring the deflection of hypoid gear sets by an evaluation of thermographic contact pattern. This method is based on measuring the contact pattern via a thermographic camera and transferring the optical information of the images into objective parameters to describe the properties of the contact pattern such as position, size and inner temperature. Through experimental study the influence of different load and rotation speed combination on the contact pattern are investigated. With increasing load, the size and the inner temperature of the contact pattern rise and a deflection to the heel is detected for drive and coast flank. Using a linear approach, the load and contact pattern parameters are showing a high correlation (R² > 0.9) coefficient. Although a higher rotational speed did not show any influence on the positioning of the contact pattern, an increased temperature leads to an enlargement contact pattern size. This is considered as disruptive effect which can be compensated by optimizing segmentation threshold in the image processing. As part of the publication, the experimental results are compared to a loaded tooth contact analysis (LTCA) conducted with the software BECAL. After optimizing the deflection for LTCA, the results in the different loading points are showing a high agreement in a direct comparison.

One pulse one wire mode was proposed for butt welding ultrafine 304 stainless steel wire mesh by pulsed microplasma arc welding. The weld morphologies and the resultant microstructure and mechanical properties were investigated. The results show that with an increase in duty ratio, the weld tends to vary from quasi-spherical to elliptical-spherical welds. However, the change tendency of welds is opposite when peak current rises. The microstructures in weld zone are γ austenite and δ ferrite, and grow and increase with a rising in duty ratio at peak current 2.2 or 2.4 A though refine in the δ ferrite at peak current 2.6 A. The weld with peak current 2.2 A and duty ratio 30% performs the better mechanical properties.

The distributed optimization algorithms using consensus control are proposed for solving multi-agent optimization problems. The multi-agent optimization problem has discrete and continuous decision variables to minimize the sum of local cost functions with local and global constraints. The problem is formulated as a mixed integer programming problem. In this study, we propose two distributed optimization algorithms to solve the problem. A feasible solution is obtained by solving the continuous optimization problem using an existing distributed optimization method with consensus control and solving the discrete optimization problem by fixing 0-1 variables. In the proposed method 1, all possible combinations of a binary variables are searched. Since all combinations are searched, the exact optimal solution can be obtained. However, it is difficult to apply to large-scale problems. In the proposed method 2, we derive the solution of binary variables by using Lagrangian decomposition and coordination approach. The proposed method 2 can provide approximate solutions for more complex problems within a practical computation time. These methods are successfully implemented to obtain near-optimal solutions in a distributed environment for supply chain planning problems for multiple companies and multi-agent unit commitment problem. The number of information exchanges in the two proposed methods is evaluated. The information exchange for these methods can significantly reduce the data exchange compared with the conventional centralized optimization method. Computational experiments for the multi-agent unit commitment problems and supply chain planning problems for multiple companies demonstrate the effectiveness of the proposed methods.

Industrial design change task refers to the task resulting from industrial design schemes changing processes due to manufacturing needs in complex product development. In order to make full use of the experience generated in design design change, this paper proposes an industrial design change task allocation method based on implementation intention. Based on the action stage model, the connotation of the implementation intention of industrial design tasks is proposed. Based on the state of the design change, the design team and design task model are constructed through the Extenics theory. A quantitative model of implementation intention and an evaluation method for generalization are constructed. The similarity between the design tasks and the support of the design team for the design tasks are quantified and calculated by the complex network. For change design tasks other than design activities, the collaborative filtering algorithm is improved to achieve design team recommendations based on task similarity. In the process of collaborative filtering, the relationship between tasks is considered, and the time loss caused by the propagation of tasks among different teams is evaluated to improve the effectiveness of the task allocation scheme. Take the change in the internal facility manufacturing requirements of the intelligent cabin as an example. Compared to the design process before applying this method, the task load of design teams was reduced by 18.74 %, which proved this method can support industry design change problems.