A method for both quality improvement and design efficiency is required to meet diversifying customer requirements and human resource development. Especially in the case of configure-to-order production systems, a design method that can appropriately evaluate the diversity of modules in advance is necessary to suppress customization. Currently, such evaluation is implicit, and technical transferability is also an issue.

In this paper, we propose a method to visualize and analyze the diversity of modules and evaluate them in advance and show its effectiveness when applied to servo motors. The proposed method is expected to standardize and formalize the conventional methods.

The realization of a sustainable society requires the reduction of waste resources and the lengthening of product lives. For this purpose, it is necessary to promote component standardization through product modularization and to design product architectures that are easy to upgrade and repair, and that are highly sustainable. Previous studies on product architecture design have included design methods that modularize products with close relationships among their components, design methods that focus on profitability to develop product families by sharing some components, and design methods that focus on sustainability in the supply chain. In this study, as one of the product architecture design methods focusing on sustainability in the supply chain, we consider the production capacity of each supplier, the delivery capacity of each means of transportation, and the on-time delivery rate of each supplier, which have not been considered in previous studies. Specifically, it derives values for supply chain quality, cost, environmental load, lead time, and on-time delivery rate for multiple product architecture candidates and compares each index on a one-to-one basis to select a product architecture and its supply chain that meets the designer's objectives. The results of applying the proposed method to the architecture design of laptops confirmed that the method can suggest to the designer the appropriate architecture and its supply chain according to the number of products to be manufactured and their means of transportation.

Green design is becoming a one of the major concerns of product design due to growing impact of global warming and to attain carbon neutrality. The Tsugite which can be seen in historical buildings in Japan is traditional Japanese joinery designed to enable replacement of worn parts to extend the life of temples and pagodas. It is inherently sustainable technique. In this paper, a new joint design method based on concepts of the Tsugite, which targets enabling replacement of parts for frame-structure automobile, is proposed. The weighted spectral clustering is selected to design a joint since a characteristic of the minimum-cut of spectral clustering is suitable for guiding and designing matching-surfaces of the Tsugite joinery. To apply this method to more complex shaped mechanical joints, coordinate transformation based on the principal axes of the stress-strain tensor is introduced. By selecting the shear stress in the plane defined by the two maximum principal axes as weights for clustering, an appropriate clustering results can be obtained, and boundaries of clusters show good guidance for designers to design mechanical joints for frame structures.

Automobile suspensions and aircraft main landing gears, which are critical safety components, require extremely high strength and reliability. Therefore, a seamless design using high-carbon steel is necessary. Conventional designs have been manufactured using sand casting, making it difficult to incorporate rib structures into the products. In this study, we utilized ultra-fine spheroidal graphite cast iron, developed through joint research, to enable mold casting and the application of rib structures. This allowed us to establish a new design approach that avoids the generation of hard inclusions resulting from high carbon content, thus maintaining fatigue strength. As the subject of our research, we used the steering knuckle of an automobile and designed models with rib structures of 2, 3, 4, and 5mm under various manufacturing conditions. We conducted structural analysis under severe driving conditions and investigated the heat dissipation performance of the steering knuckle, as well as the thermal impact on the wheel hub bearing due to the addition of other components using heat transfer analysis. From the structural analysis, we found that increasing the rib thickness enhances rigidity, while heat transfer analysis revealed that reducing the rib thickness provides better heat dissipation. Thus, indicating a trade-off relationship between structural integrity and thermal performance is specified and supported for balanced design.

It is critical for companies and other organizations to balance exploration and exploitation so that they can adapt to rapid changes in the business environment and develop and design products in a sustainable manner. However, no methodology has been established to balance them. This paper proposes a method to support the directional determination of exploration and exploitation in the product design and development. This method identifies functions and attributes that can be explored and exploited in new product design and development, based on past product information and large-scale external information resources. The functions and attributes are presented to designers to direct their exploration and exploitation. Furthermore, the proposed method was validated by applying it to the design of a camera. The results showed that the proposal for exploration directions included functions that lead to designers' ideas, indicating the potential effectiveness of the proposed method. In addition, the proposed exploitation direction includes known trends in attribute value changes, indicating the method is feasible.

Using a design structure matrix (DSM) to model three different automotive design and development processes, rework associated with development was formulated as a sum of geometric progression, and hypothetical schedule and cost risks were analyzed using a probabilistic Monte Carlo approach. The results showed that the schedule and cost risks could be reduced by a process that reduces rework. The study also identified issues with this method, including correction of risk values, reproducibility of DSM creation, obtaining cost tables, and setting acceptable risk.

This paper considers promoting not only recycling through destructive dismantle but also encouraging the reuse of parts and components through disassembly to achieve an environmentally friendly circular economy system. However, in many cases, the high cost of disassembly makes it economically unfeasible, preventing the possibility of reuse even when there is a desire to reuse the parts. In this paper, we propose an evaluation method that incorporates consideration for both environmental impact and economic feasibility. This method aims to maximize the disassembly profit (subtract the cost of disassembly process from the value of removed parts.) while satisfying the constraints of the disassembly sequence. To achieve the above, we propose a method using DSM (Design Structure Matrix) partitioning techniques. This paper evaluates the coffee maker as a case study and propose a disassembly sequence that maximizes business feasibility. Additionally, proposed method is able to provide guideline for design modifications to make disassembly easier.

Social implementation of advanced technologies means innovation using innovative technologies with a TRL of approximately 3 to 4 (achieving TRL 8 or higher), which are mainly researched and developed at universities and other research institutions. Social implementation of advanced technologies related to aircraft, which, along with medicine, is subject to strict safety regulations, commonly requires collaboration with multi-stakeholders, control of uncertainty, and resolution of issues such as institutional development. This study proposes a methodology for innovation management that can serve as a comprehensive countermeasure, along with a discussion of these common issues.

In this study, we propose a new vibration control device consisting of a constraining frame and a viscoelastic layer to realize high vibration control performance. The proposed new vibration control device consists of a viscoelastic gel partially sandwiched between a highly rigid constraining layer with a box cross section and a frame member. It converts vibration energy into thermal energy through damping. In this study, a bead panel with a vibration control device consisting of a constraining frame and a viscoelastic layer is modeled and analyzed numerically by FEM, and the changes in vibration reduction characteristics and sound radiation characteristics are obtained using the MSE method．

STPA (Systems Theoretic Process Analysis) is a hazard analysis that focuses on the interfaces between the constituted subsystems or the components of a system. One of the advantages is the applicability of the method even at the conceptual design stage when the details of the elements have not been decided yet. This study discusses the effect of focusing on the interfaces at the conceptual design stage for the hazard analyses exampled by considering the operations of the space systems such as a cubesat and a space smart antenna systems.

This paper deals with vibration damping analysis using finite element method with Model Strain Energy Method for flat plates having new type acoustic black holes. The new black holes have shapes like half of carters. Viscoelastic vibration damping materials are laminated on the plate at the acoustic black holes. The flat plate is fixed at two edges where the acoustic black hole doesn’t exist. The damping layer is modeled by linear solid finite elements in consideration of complex modules of elasticity. We calculated modal loss factors and eigenmodes of the models. We clarified effects of the thickness profile and radius of new acoustic black hole on the modal damping. As a result, we found that a square steel plate with 200[mm] on a side can have a very large effect on a new acoustic black hole with a radius of about 70-90[mm].

In Japan, where earthquakes have been frequent in recent years, attention has focused on systems and structures that can reduce the damage caused by earthquakes. Therefore, the purpose of this study is to investigate efficient vibration and wave damping methods by focusing on acoustic black hole proposed by Mironov. Since acoustic black holes are dangerous due to their sharp tips, this paper employs a crater-type acoustic black hole in which the thickness of the panels decreases in a circular manner toward the center. And a hybrid vibration analysis using statistical energy analysis (SEA) and FEM for a model of two panels connected in an L-shape and given a crater-type acoustic black hole with a damping layer on one side. We focused on the validation of the proposed SEA method and discuss the effect on the wave propagation characteristics between two panels in an L-shaped panel structure when a crater-type acoustic acoustic black hole is given.

The Real World is changing rapidly. Yesterday, its changes were smooth, so we could differentiate them and predict the future. Now they change sharply, so we cannot predict the future. And our living space is expanding rapidly and booundries disappear and it is now open space. The greatest change is materials are getting softer and softer with the rapid progress of material engineering. Therefore, we could identify a product with our eyes and even from a distance, but today we need to directly interact with it and "feel" with using all body senses. In mechanical field, we have been processing products as hardware, but today they become software. So we need to adapt to its flexible behavior. Thus, somatic sensation or proprioception becomes increasingly important. This talk describes why somatic sensation is important and how we should make the most of it. Practial solution is given in another talk in this D&S 2023 conference.

The Real World is changing rapidly. Its changes become sharp, so we cannot predict the future. Our living space is expanding rapidly and it becomes an open world without boundary. What is changing rapidly is materials are getting softer and softer with the progress of material engineering. Thus, we need to directly interact with the Real World and we need to cope with it in real time. Current approach is very compatible with the 0-1 based computing. So, DX, AI and brain are getting wide attention. In short, we are paying our main effort on digitalization. But we should remember that most objects are analog. The environments and situations change rapidly, extensively and in an unpredictable manner. So, instead of digital inlelligenc, we need Analog Intelliegec to cope with this unexperiened Real World. The emerging issues of this new world are discussed and we conclude that there is no other way but to make the most of Instinct. Therefore, we developed a new approach to assist Instinct.

The Industrial Society produced current Industrial Society. Yesterday, we worked for ourselves to make our dreams come true. This is true Engineering. But the Industrial Society focused on product, paying little atention to humans. It introduced Division of Labor, so we started to work for others. This is an external reward society, so we cannot secure the maximum happiness and feeling of achievement. The current Industrial Society is getting close to its end, so we need to develop the next society. In this paper, it is emphasized that we need to work for ourselves and achieve self-actualization to satisfy our human needs and make our life happy and Mechanical Engineering can serve a great deal to establish such a human-centered society.

The authors attempted to create a manual on mechanical design drafting and 3D-CAD operation using Onshape for elementary and junior high school students. The manual was reviewed through the actual verification work of junior high school students and university students in the author's laboratory. Using the extrusion feature of Onshape, we constructed classical foundation drafting such as cylinders, V-shaped blocks, connecting rods, and spanners. These drawings of cylinders, V-shaped blocks, and connecting rods were combined with Onshape account registration, setup, printing 2D-CAD drawings from 3D-CAD drawings, and assembly drawings to complete a total of seven lectures. From the results of the study, it was confirmed that even junior high school students who cannot read three-view-drawing can learn how to operate Onshape. In addition, the effectiveness of Onshape was further verified for students in the author's laboratory by actually employing Onshape in an engineering design exercise.

To leverage the diversity of team members and enhance decision-making quality, a “Doubt-person” (an individual who resists peer pressure and expresses doubts or opposing views) must be present, depending on the situation. This study surveyed students from the 2019 academic year enrolled in the “Teamworking Exercise” course at the Faculty of Creative Engineering, Kagawa University. The course introduced an educational program centered on “collaborative design via Type Utilization Education, drawing from Jung’s Type Theory.” This examination assessed whether students exhibited “Doubt-person” behavior during the exercises. Responses from 234 students in the analysis were statistically analyzed to investigate the association between exhibited “Doubt-person” behavior and the hypothesized psychological type. Findings indicated that differences in proactivity, attributed to “differences in judging function type,” had a more sustained impact than those from “differences in attitude type.” From an educational perspective, to increase “Doubt-person” without decreasing their numbers, emphasis should be placed on explanations appealing to those with “hypothetical type of thinking” and those that mitigate the averseness for individuals with “hypothetical type of feeling.” Awareness of this difference is necessary and effective concerning training and securing a “Doubt-person.”

The Industrial Society initiated by the Industrial Revolution focuses its main attention to Industries and we work for others. We forgot to pay attention to human needs. The Industrial Society, however, is getting close to its end and now we need to develop the next society. Many issues emerged in The Industrial Society. To solve them and to develop human-centered society, we need to develop new learning system in engineering. We should look back and remember how we enjoyed engineering in old times. We worked for ourselves and we challenged to make our dreams come true. This is what only humans can do. We should enjoy life as humans. To achieve this goal, design learning in engineering should move from expert to amateur, from education to learning, from product to process and self-motivation should be encouraged. Then, diversification and personalization will be brought about. We should remember the potentiality of Mechanical Engineering. We should be aware of how Mechanical Engineering can play a major role in achieving these objectives.

Recently, there has been a demand to reduce the number of prototypes when developing knit products to realize a sustainable society. In response, attention has focused on knit-fitting simulators. Simulators have now modeled a single knit with a spring-mass model and mapped it onto a virtual garment shape. However, simulating knitwear, which can have several million knits, requires enormous computation time and has been challenging. Therefore, this study focused on the multi-fidelity multi-scale method as a top-down approach. It aimed to propose a framework for a simulator that efficiently calculates the shape of an entire knit product and the algorithms required for it. The results revealed the possibility of calculating the approximate shape of a garment shape with large-scale shape changes by changing the fidelity and scale in the simulation process.

In the vehicle development, it is important to solve a feasible design set where all multidisciplinary constraints are satisfied. By solving feasible design set instead of a single optimal design, critical rework due to uncertainties of design conditions can be avoidable. In this paper, we propose a novel set-based method based on Differential Evolution Based Adaptive Sampling (DEBAS) which can solve feasible design set of an optimization problem with high-dimensional design variables. In the proposed adaptive sampling strategy, the mutation scheme in differential evolution (DE) is introduced. For objective function used in the DEBAS, Kreisselmeier-Steinhauser function is introduced to evaluate dominant violation value among constraint functions. Besides, the objective function has a term which can generate samples throughout feasible region. To show the effectiveness of the proposed method numerical examples including practical design problem are demonstrated.

This study proposes a method to develop an “Engineering Navigation System (ENS),” which navigates engineers' activities based on the Digital Triplet (D3) approach. The method converts skilled engineers' Engineering Processes（EP）, modeled using the D3 framework, into ENS that supports beginners. The proposed method first identifies individual engineering activity and there sequences from the EP model and identifies various digital tools to be used within ENS by executing an EP analysis. Subsequently, the specifications of the identified digital tools are determined, and the tools are implemented and integrated into the ENS. Based on this proposed method, an ENS was developed to provide Kaizen support for assembly lines, effectively confirming the usefulness of this approach.

The tower-type solar thermal power generation plant comprises towers, receivers, and heliostats. Heliostat reflects sunlight to the receiver, and the receiver collect reflected light from the heliostats and converts it into heat. The tilted angle and azimuth angle of heliostat change according to the solar orbit, and it tracks the sun. Therefore, the position of the heliostats is important in tower-type solar thermal power generation plants. This study examines a heliostat equipped with a mirror instead of a heliostat that drives a photovoltaic array with multilayer film that can control broadband oblique incident light. We propose a new heliostat placement method based on the radial staggered layout method. The layout of heliostats is optimized by the genetic algorithm to maximize the total annual radiation of heliostats and tower receivers. This study also considers shadowing, which creates shadows with heliostats, and blocking, which blocks reflected light from the heliostats. As a result, the layout of the heliostats is obtained that maximizes the yearly total amount of light received, considering the site's shape, shadowing, and blocking.

Japan is experiencing an extremely low birthrate and aging population, and it is expected that the number of bicycle accidents involving the elderly will increase in the future. There are cases where elderly people cause accidents while riding bicycles due to misidentification. Therefore, there is a need for a system to reduce the number of bicycle accidents caused by the elderly. This study investigates the development of a duct fan-based assist system to support the elderly in bicycle riding and reduce the number of accidents.

For construction of driver model, the research in recent years train driver agent composed of the neural network in virtual space and run courses. Learning while accumulating memory isn’t easy for the neural network due to the catastrophic forgetting. This paper aims to construct a driver model can run multiple courses like an expert human driver by devising the method of setting the objective function and the structure of the neural network.

Farming-type solar power generation involves installing solar power generation panels in the upper farmland space, generating solar power, and cultivating crops. Due to the structure of the farm, there is a trade-off between power generation and the amount of sunlight allocated to growing crops. In addition to weather conditions, solar panel installation conditions also affect this trade-off. Finding a way to install solar panels that efficiently use sunlight is necessary. In this study, we solve a multi-objective optimization problem using NSGA-II to optimize the installation parameters of solar panels to clarify the trade-off relationship between the amount of light received by solar panels and the amount of light received by farmlands. A method to optimize the panel installation parameters according to the growing season of crops is also proposed in this study. The effect of both on increasing the amount of received light is examined.

Dynamic FEM analysis is valid for designing rotating machinery to reduce its vibration when we may ensure enough accuracy of the analysis. Surrogate multiple objective optimization method is one of the most effective methods for structural identification improving the FEM analysis model of a structure to adjust the natural frequency analysis results to the experimental results. However, since convergency had a subject, in this research, the technique of having newly combined the Newton's method based on Adjoint variable method is newly proposed.

In this study the structural identification method is applied to stator of an induction motor to determine the Young’s modulus of the principal components on the FE model minimizing the analysis errors of the natural frequencies of the 2-lobe and 3-lobe circular modes to the corresponding experimental results.

As awareness of environmental problems increases, EVs (electric vehicles) get more attention, and high-performance EVs have been developed. Since motors are one of the components that determines running performance of EVs, many studies have been carried out to improve motor performance. Average torque is often used as an index for evaluating motor performance, and it has a strong relationship with the internal structure. Therefore, structural optimization has been used to obtain the design which can achieve high average torque. Structural optimization is a design method which uses numerical calculations based on mathematical and physical reasons to find a design that maximizes the desired performance, and is applied not only to motors but also to structural design in various fields such as dynamics, heat and fluids. While, in the motor using permanent magnets, the drive generates temperature rising and it decreases the average torque without appropriate cooling. This phenomenon is caused by copper loss which is the loss caused by the current in the coil, iron loss which is caused by changes in the electromagnetic field in the magnetic material, and high-temperature demagnetization which is the property of permanent magnets. Therefore, decreasing heat losses is an important problem in the design of permanent magnet motors to respect with efficiency. In this paper, a design method for permanent magnet motors which minimizes heat losses keeping the torque performance based on topology optimization which is one of structural optimization methods is proposed, and the validity of proposed method is shown with numerical examples.

As acceleration of worldwide electrification, companies are competing in research and development of motor system, which are used as the main power source of electric vehicles. The performance requirements such as high power, high efficiency, and high density make efficient design and development of motors essential by combining multi-physics simulation with analysis problems from various fields such as structure, electromagnetic field, mechanism, fluid, and acoustics. The motor performance is affected by not only magnetic circuit but also materials properties. It is required a coupled analysis of the magnetic field and heat transfer in the motor design. Levels of mechanical vibration and noise usually are varied with weight and efficiency of motor. In this study, a Halbach array motor with high power density and high efficiency was employed, and a multi-objective optimization design was performed using JMAG and modeFRONTIER with bi-directional coupled magnetic field and thermal analysis, and then, vibration and acoustic analysis was conducted for the preferred solution with high power density and high efficiency. As a result of the optimization, a preferred solution was obtained that increased power density and efficiency and suppressed temperature rising as compared with the base model. Vibration and noise were also estimated.

Finite Element Analysis (FEA) has been widely used for evaluating the performance of products in design field. Industrial motors, generators and transformers consist of stacking thin electromagnetic steel plates to reduce eddy-current losses. Therefore, each steel plate is much smaller than the equipment itself and it’s necessary to simply model those steel plates as a single material with anisotropic property. For obtaining accurate analysis results, we need to set appropriate values for these material constants. In this study, we propose a solution to material constants identification problems based on natural frequencies. The proposed method is incorporated Newton’s method based on Adjoint variable method in Multi-Objective Surrogate Optimization’ flow. The results of numerical experiments showed that the proposed method stably obtained high accurate solution, utilizing the characteristics of these two methods.

Topology optimization often requires solving multimodal and highly nonlinear opitimization problems with a huge number of design variables to achieve structural design with high degrees of freedom using complex physical models. However, conventional evolutionary algorithms, which are effective for multimodal and nonlinear optimization problem, cannot solve such high-dimensional problems. This study proposes a data-driven evolutionary topology optimization framework to overcome this challenge with dimensionality reduction. The proposed framework is based on data-driven multifidelity design and latent crossover, where design candidates generated by low-fidelity optimization are iteratively updated through high-fidelity evaluation and crossover in a low-dimensional latent space. We introduce a novel selection operation to address the issue that the diversity of solution distributions in the objective space and the design variable space does not align for multimodal multiobjective optimization problems. We utilize persistent homology to extract topological characteristics of structures, such as the number of connected components, voids, and their sizes, into persistent diagrams. By quantifying the differences between structures using the Wasserstein distance between corresponding persistent diagrams, we can quantitatively evaluate the topological differences among the structures. Combined with the conventional approach of ensuring diversity in the objective space, it provides a selection method that also preserves diversity in the design variable space. We apply the proposed method to a 2D structural design problem, and the results demonstrate that the selection based on persistent homology improves the search performance of the framework. Furthermore, the optimized structures outperform the performance of those obtained by conventional gradient-based optimization.

In 3D reconstruction, there are problems when reconstructing irregularly and complexly overlapping objects such as trees. This research aims to reconstruct three-dimensional linear structures such as trees, bent reinforcing bars, and tangled wire waste. We propose a method to identify the tree-shaped structure of a linear three-dimensional structure by combining 3D reconstructed point cloud data using moving images and voxel-based denoising with density-based clustering. We report the results of the 3D reconstruction of the tree structure of the entire deciduous fruit tree using the proposed method and demonstrate the effectiveness of the method. In addition, the branching structure of the linear 3D structure could be represented from the tree diagram of voxels.

Designers design based on “quantitative information,” while users express product evaluations in “words”. In order to eliminate this fundamental gap, it is necessary to understand the depth of the "words" on the surface that users utter. To do so, it is necessary to extract the target "words". However, there are things that have similar meanings or are similar in writing but have different meanings, making it difficult to extract. Therefore, we investigated the trade-off relationship between “words”. In this research, we applied the multiobjective optimization and satisfaction trade-off method to the results of the “kansei” questionnaire on golf glove packages and analyzed the relationship between each "kansei” item.

Sense of agency is the sense that I am the one who is causing or generating an action. Sense of agency is a factor of the pleasure of operating a machine. In recent years, machines have become increasingly autonomous. While machine autonomy increases task performance, it decreases the sense of agency. It is useful to construct a mathematical model of sense of agency and investigate the effect of the model variables on performance to balance sense of agency and performance. The mathematical model that we constructed predicts the interaction between prediction error and prediction uncertainty in both sense of agency and performance. In a simplified system of an automobile, prediction error was manipulated using the difference between the average of delay in learning and the average of delay in measurement, and prediction uncertainty was manipulated using the standard deviation of delay in learning. The interaction between prediction error and prediction uncertainty was examined in the measured values of sense of agency and performance. This interaction means that when an unexpected delay occurs, both sense of agency and performance are higher when one is not accustomed to the previous delay.

In fields such as mechanical engineering and traditional crafts, skill succession is an important issue. However, the embodied knowledge, such as the practical tips and intuition of skilled workers, cannot be verbalized and requires an enormous amount of time to be passed on. On the other hand, avatars have the characteristic of being both subjective and objective. This characteristic suggests that the use of avatars may be effective in the acquisition of embodied knowledge. Therefore, in this study, we conducted an experiment using mirrors and avatars for the acquisition of the embodied knowledge contained in pantomime and evaluated its effectiveness through a questionnaire. In the future, it is expected to lead to the proposal of a new educational approach to solve the problem of knowledge management.

In recent years, remote communication using avatar robots and other devices has attracted much attention. This research aims to enable remote users to communicate not only with robots but also with any other objects by using them as avatars. Specifically, we used a 360° camera and voice to investigate whether the remote user feels that he or she has been transported into an object and whether the people around the object feel that the object is an avatar when it is used as an avatar. This aims to provide a new perspective on avatar-human interaction.

In this research, we developed and evaluated a listening system that guides the line of sight to images presented by speech using a familiar robot as a dialogue agent. The presentation image has a function of nodding in accordance with the utterance rhythm. In this system, we conducted experiments using two modes. One is that subject communicate with the listener’s character on the display and guide the listener’s character to gaze at the image. The other that subject communicate face-to-face with the robot and guides the robot to gaze at the image. In both modes, the presented image either nods in accordance with the utterance rhythm or does not nod at all. The experiment was conducted on 31 elderly people. As a result of two-factor analysis of variance, significant differences were observed between the listener’s character and the robot in the items of enjoyment, familiar and ease of speaking and the robot was evaluated highly.

In primitive communication where the roles are divided into speaker and listener, not only verbal messages but also various non-verbal behaviors such as facial expressions, gazes, pupil responses, and body movements are mutual synchronized. This synchrony of nonverbal behaviors through their bodies creates senses of unity and emotional empathy in an embodied communication. In particular, it was reported that an active listening attitude in a listener brought feeling of acceptance and empathy to a speaker. Therefore, it is expected that speaker's emotions can be drawn out by designing an active listening attitude of a listener. In this study, a speech-driven pupil response robot with uplifting backtracks using an interaction-activated communication model was developed. This robot generates uplifting backtracking according to the volume of speaker's speech and expresses a listening attitude as a pacing skill.

This study attempts to explore the possibility of user-test related to hands and fingers with low-cost physical mockups that enable easy quantitative usability evaluation at the upstream stage of design, while wearing no devices on the body. To achieve this, we propose a cyber physical mockup that integrates the position and posture of the physical mockup by video analysis with an infrared hand motion capture device. Mockups are made of transparent plastics and thin color tapes are put on the characteristic edges of mockups, which are detected in video images and used to calculate three-dimensional position and orientation so that motion captures of hand and fingers and the mockup are aligned together. We applied this method for a simple rectangular solid intended as a mockup for smart phone. Operating the smartphone mockup is shot with video and hand capture devices, then the position of mockups and hands are analyzed.

In this study, a level-set based topology optimization analysis is performed for a bilinear elasto-plastic structural model, and the numerical results is compared with the topology optimization analysis results for an elastic structural model. The performance function is defined by the strain energy, and the governing equation for bilinear elastoplastic deformation is introduced as the constraint condition of the performance function. The finite element method using iso-parametric elements is applied to the structural analysis, and the reaction-diffusion equation is employed to update the structural shape in the topology optimization analysis. In the result of the topology optimization analysis for bilinear elasto-plastic structural model, it was confirmed that the support members are generated around the support boundary.

There is a requirement to improve machining performance in machine tools. Machining is necessary to achieve both highspeed operation and precise positioning. In the usual development process of machine tools, we have to repeat prototyping and improvement to achieve the spec of machine tools; therefore, the development term is prolonged. In this paper, we constructed a coupling analysis model of a flexible multibody dynamics model and control model with reduced study time to shorten the development term of machine tools. Using the constructed coupling analysis model, we simulated the operation of the machine tool and compared it with the experimental results. As a result of the comparison, it confirmed that the constructed coupled analysis model can reproduce the operation of the actual machine. In addition, we propose a machine tool development process that can shorten the development term by using the constructed coupling analysis model.

There are specific shapes which have not been realized due to process limitations, even though they are known to be optimal for obtaining the best component performance in terms of metal product design. The application of metal additive manufacturing eliminates some of these manufacturing restrictions and allows for greater design freedom. However, the cost of metal additive manufacturing is higher than conventional manufacturing methods and the manufacturing time may increase. Therefore, its application in product design is still in the research stage. In this research, we introduced a method and system that allows us to search for parts and components that can be re-designed to improve performance and reduce cost based on the concept of structures such as reinforcement that will be added using metal additive manufacturing. Using a gear pump as the sample application, static and thermal analyses were performed by setting up the reinforcing force at various points on the cover and the casing of the thin-walled gear pump using 3D CAD. Based on the analysis results, the optimum conditions for reinforced structure at the current stage were derived. Static analysis was performed on the gear pump designed by applying these conditions, and a comparison was made with the original model. The maximum composite displacement and maximum principal stresses were reduced by placing the reinforcement where the deformation in the tensile direction is large, while heat dissipation is improved by placing the reinforcement near the heat-generating area. The trade-off model between structure and heat dissipation using metal additive manufacturing enabled the improved model to achieve better performance than the existing model.

This study proposes a framework for capturing and formalizing knowledge systematically in real-time. In the design process, engineers face various issues and manipulate some alternatives to solve them. However, the engineering knowledge used throughout the process remains implicit and tacit. The proposed framework utilizes a log of design operations and spoken annotation, while each engineer verbally expresses his/her thoughts, and all their computer screen activities are recorded. The framework manages and reuses such engineering knowledge by mapping respective activities to design operation templates, which leads to a systematic approach to understanding the design process, considering 1D modeling as the means of functional design. To represent the logical structure of the design process, a graphical text format called gIBIS is used. It serves as an argumentation model for design rationale. This real-time knowledge capture method is nonintrusive for engineers. The framework is applied to the modeling process of a spinach harvesting machine and its effectiveness is validated through an analysis of the captured knowledge. The results demonstrate that the framework can effectively capture knowledge and replicate engineers’ thought process.

In this study, we investigated the creating of Modelica library for model changes in 1D-CAE and the management of models using Git. Focus on the parts of the model that have been changed in 1D-CAE model, we prepare abstracted and extended elements for these parts. And focusing on changes to the model, we create the branches for using, building, verifying, and extending the model. The library's structure allows parallel version control of model changes.

This study proposes a method to support design problem formulation in the early stages of the design process by visualizing high-dimensional design solution spaces using specific two-dimensional design variable planes. The proposed process consists of five steps: derivation of the design solution space, partitioning the design variable planes, evaluating the partitioned spaces, classifying constraint networks, and displaying constraint network-performance radar charts. Ward's method is used to partition the regions, and constraint networks are employed to evaluate the partitioned spaces. This paper applies the proposed method to a simulation model of a motor for an electric vehicle as a case study and visualizes the high-dimensional response space from the perspective of design characteristics using specific two-dimensional design variables. The proposed method comprehensively examines design features contained within the design solution space and classifies multiple characteristics for easy interpretation by designers. Visualizing the trends in constraint relationships between design variables is expected to reduce the need for rework in later stages of the design process during the early stages of design.

In the manufacturing industry, collaboration among designers is becoming more difficult due to the increasing scale and complexity of products. In this study, focus on design rationale, which is difficult to be converted into data, and aim to share it with a low burden. Design rationale can be extracted by analyzing the content of speech during design and estimating the designer's state. Automatic extraction method of design rationale information by combining the following states: automatically documenting recorded speech, analyzing the results of identifying important words using TF/IDF. Also estimating the skeletal structure from video recordings, estimating behavior from the data, and estimating physiological states from biometric signal information using smart glasses. We proposed a method for automatic extraction of design rationale information based on a combination of these two states. The results showed that the extraction of design rationale was more accurate than the extraction based on the analysis of speech content alone.

In this research, we extend functional modeling by conversion of materials, energy, and information, and describe not only physical phenomena but also human physical and psychological characteristics as conversion-format models using computer-processable physical quantity expressions and language expressions. We propose a method and software technology for design ideation for new functions and user experiences through systematic exploration of possible combinations of the conversion model elements by their connectivity based on the similarity of their physical-quantity expressions and language expressions. The effectiveness of the proposed method and software technology are examined based on the execution results of the implemented software.

Recently, “what to create” is becoming more important in design than “how to build”. In order to create innovative product and services, ideas for new functions and user experience should be generated from the early design stage. Effective methodologies for supporting design ideation using information technology (IT), however, have not been established. This study aims to obtain effective information for design ideation, such as trends in functions and user experiences, and design strategies applicable to new products and services, through differential analysis of functions and user experiences of existing products and services using language information processing. As a specific approach, a set of sentences of product descriptions obtained from e-commerce sites by web scraping were converted into a set of bag-of-words, and the differences in functions and user experiences were analyzed based on the meaning similarity and IDF. From the results, we verified the effectiveness and potential of the proposed method and discussed issues to be solved.

In recent years, STEAM education, which adds "A" (Art) to STEM (Science, Technology, Engineering, Mathematics), has been gaining attention to foster creative talents. Furthermore, students from art disciplines are attracting interest as individuals who can contribute to solving societal issues. It is believed that they are valued for their ability to recognize unnoticed values and issues, and their power to shape or solve them. In this research, we compare students from engineering and design disciplines with generative AI as the subjects and obtain written descriptions of ideas generated in response to the given tasks. Through language information processing analysis, we aim to examine the similarities and differences in the content of design ideation among each group. By clarifying these aspects, we can explore methods to support everyone in generating ideas like those of exceptional creativity.

In the manufacturing industry, knowledge transfer from experts to beginners has become an important issue because of the decreasing number of experts. To support knowledge transfer, various support systems have been developed. To present appropriate knowledge to the user, the user’s skill level needs to be assessed. The purpose of this study is to develop a technique to quantitatively estimate the user’s skill level using gaze-measurement data. We quantified the skill level of the participants by the number of vortex search errors in the placement test. In the task of searching for vortices from fluid simulation results, we measured the gaze of the participants and built a regression model that learned the relationship with the vortex search errors. As a result, the error of the estimated number of vortex search errors using Random Forest and Multi-Layer Perceptron was 1.12±0.41. These results imply the gaze shows the difference in the users' skill levels and show the prospect of presenting appropriate knowledge to the user of the knowledge transfer support system by using the gaze-measurement result.

JAXA is developing a data assimilation method that treats control factors as "products" and error factors as "operations" based on the concept of quality engineering. In this report, we show a method of data assimilation while interacting with machine learning and designers. The purpose of this method is to enable high-speed and high-accuracy fitting even in nonlinear CAE. In this study, an overview of the method and the target turbomachinery are presented.