An algorithmic design is a shape design approach that takes advantage of elegant geometry generated by mathematical functions or numerical algorithms. In the current study, an optimal design approach of truss structure based on an algorithmic design is discussed. For the sake of efficient calculation, we conduct a gradient-based procedure. Nodal displacement due to a given nodal force is adopted as the objective function. The nodal positional differentiation of stiffness matrix, that is given in the previous work, is applied to the calculation of the gradient. Example designs of truss structure are conducted; the feasibility and characteristics of the proposed approach are discussed based on the obtained optimal designs.

The optimal shape design of solid structures must satisfy two key objectives: high mechanical performance and low volume or mass. For machines in motion, vibration behavior is crucial for machine life and reliability, requiring optimal time response characteristics depending on the objective function. Time response problems arise not only in solid structures made of single materials but also in composite structures composed of heterogeneous materials. Heterogeneous composite structures can exhibit excellent mechanical behavior that is unattainable with single-material structures. For example, by using dissimilar materials with different thermal expansion coefficients, it is possible to control thermal displacement through optimal shape design. Optimal shape design is also beneficial for reducing the dynamic compliance of solid structures within a limited volume. Bimetals are a type of composite structure consisting of two different adhered metals. In recent years, metal additive manufacturing technology has rapidly advanced, allowing for precise fabrication of metal parts. This study aims to develop a gradient-based optimal shape design method for minimizing the dynamic compliance of 3D bimetallic composite structures. First, we formulate the design problem, where the time-dependent dynamic compliance is set as the objective function to be minimized, with the volume constraint serving as the constraint condition. Then, we theoretically derive the shape gradient function (i.e., the sensitivity function) and construct the optimal shape design system. Furthermore, the effectiveness and feasibility of the proposed optimal shape design method are validated through design examples.

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. Furthermore, the material constant united, it was crowded and the performance was considered.

The PSO algorithm uses the positions of personal best and global best to achieve results in single-objective optimization. To solve multi-objective optimization problems, this study introduces DEA to estimate Pareto Optimality and superior sets for each individual. With the Lagrange multiplier, we can calculate the target positions that may be close to the Pareto frontier, and use these positions as the global best for each individual. Then the efficiency of the current iteration result can be obtained by calculating the current best position of each individual with the position of the corresponding target on the Pareto Optimality. In this study, we employ the CCR model, a classic DEA model, and the Ranking method to solve the problem.

To demonstrate the effectiveness of the proposed method, we use several benchmark functions and modify the DEA-PSO model to suit the test function.

In forging processes, it is necessary to prevent damage to dies caused by high forging loads and to avoid the underfilling of billets within the dies. Therefore, it is essential to suppress forging loads while meeting the dimensional requirements, and it is important to optimize both the billet and die shapes. However, as the shapes of forged products become increasingly complex, determining the shapes of billets and dies becomes more challenging and is often resolved through trial and error. In this study, computer-aided engineering (CAE) integrated with sequential approximate optimization is used to simultaneously design the optimal billet and die shapes. As a result, the optimal conditions are obtained to suppress the maximum forging load and meet the target size of the forged product.

In our living environment, various noise reduction techniques exist, utilizing acoustic devices with functions such as sound absorption and sound insulation to mitigate unwanted sounds. Recently, acoustic metamaterial and acoustic metasurface, which offer capabilities beyond conventional structures, have garnered significant research interest. Due to the complex geometries of these advanced acoustic devices, designing them through trial and error is challenging. Consequently, topology optimization, which provides the highest degree of design freedom among optimization methods, has been proposed for their design. However, structures designed via topology optimization often contain floating elements that are impractical to implement. This study proposes a novel method to address this issue by applying an external force to the structures during optimization and imposing an upper limit constraint on their displacement. This constraint prevents the generation of floating structures, which exhibit larger displacements than those supported by fixed boundaries. The effectiveness of the proposed method is demonstrated through its application to the design of acoustic diodes, which allow sound to propagate in one direction while blocking it in the opposite direction. The results confirm the viability and effectiveness of the proposed approach, providing practical insights for the design of advanced acoustic devices.

In recent years, due to the metaverse boom, communication in VR space is commonly performed in various situations. Moreover, it has also been reported that personal space plays an important role in this VR space as well. The size of this personal space differs for each person depending on various factors such as gender, personality, and so on. In other words, even if one person feels that the communication distance with the other person is “favorable”, the other person often feels that this communication distance is “not favorable”. Based on this fact, we have proposed a communication environment in VR space that satisfies the personal space of both people without changing the physical distance between them. In particular, we developed a screen display method that changes the user's distance perception from the other person by controlling the focal length of the camera in VR space. In this research, we investigate the influences of the proposed method on information transmission in VR space. As a result of the experiment, in case the user's personal space is large, it is desirable to adjust the camera’s focal length to a shorter value (apparent distance becomes longer). On the other hand, in case the user's personal space is small, it is desirable to adjust the camera's focal length to a longer value (apparent distance becomes shorter).

In recent years, with the development of VR technology, VR contents are commonly used in various fields and situations. Previous research has suggested that it is important for users to feel that the avatar is like their own body in the VR experience. In particular, when this avatar physically interacts with objects in the VR space, the perception of the avatar plays an important role. Therefore, it is necessary to design how to display the avatar in physical interactions between the avatar and objects in the VR space. In this research, we focus on the avatar's body parts in VR space and investigate the influences of avatar display conditions (Arm avatar, Hand avatar, and Finger avatar) on task performance and the sense of embodiment with the avatar. As a result of the experiment, there are no significant differences between each avatar from the viewpoint of task performance. In other words, even if fewer body parts are displayed, sufficient task performance can be achieved. On the other hand, it is desirable to display arm avatar and hand avatar from the viewpoint of a sense of embodiment and motivation to use.

Previous research has been reported that children felt like a close friend and were more likely to self-disclose than other humans when interacting with a social robot. Therefore, it is expected that it will be possible to build close relationships and develop trustworthy robots by introducing an active listening attitude to social robots, as if they were communicating with human. We have previously developed Pupiloid, a speech-driven embodied listening system that expresses an active listening attitude using only the eyes based on the talker's speech. Pupiloid can give the talker a sense of attentive listening by generating nodding movements through eye movement and expressing interest through enlarged pupils. In this study, impression evaluations were conducted for 80 upper elementary school students using the developed system. The experimental results demonstrated that Pupiloid was highly rated for friendliness and the sense of attentive listening, forming positive impressions.

In this study, we discussed the generation of anthropomorphic sensations of objects using IoT devices and the elements necessary for generating anthropomorphic sensations. An IoT device was installed on an object, and the subject was observed communicating with an IoT avatar. We evaluated the impact of changes in the IoT avatar's voice and emotional and biological expressions through flashing LEDs on the anthropomorphic feeling that subjects receive from objects. Changes in tone of voice may hide the presence of a remote person from the user when operating an IoT avatar, and involuntary physiological phenomena such as breathing, heartbeat, and body temperature may cause the viewer to imagine the existence of life. We created an IoT device that has these two elements as functions and evaluated how the sense of anthropomorphism that subjects receive from the IoT avatar changes under each condition. From the experiment, it was suggested that expressions using LEDs are effective in expressing the emotions of objects.

As the metaverse expands, virtual communication presents opportunities for global interaction but also poses challenges in conveying nuanced or abstract ideas through language alone. To address this, we developed a real-time visualization system using AI technology to enhance communication within the metaverse. The system employs the multi-space design model, considering value, meaning, state, and attribute spaces to support seamless communication and effective information sharing among users with diverse backgrounds. We conducted two experiments to evaluate the system's effectiveness. The first experiment tested the system's ability to accurately visualize user-expressed concepts, showing improved accuracy through feedback, though initial results indicated a need for further algorithmic optimization. The second experiment assessed the system's role in enhancing communication quality, confirming its utility in facilitating mutual understanding and concept concretization. These findings suggest that AI-driven visualization systems could become essential tools in the metaverse, especially as global collaboration continues to grow. Future research will focus on refining system accuracy, reducing response times, and broadening user applicability.

To reduce energy consumption in a production system, it is effective to preliminarily evaluate energy-saving measures using simulations. However, creating and exploring various measures consumes time in constructing and computing simulation models. To address this challenge, we propose a novel method using 1D-CAE models that automates creating and exploring process. This method automatically presents combinations of production system components by defining connection relationship between components and solving constraint satisfaction problems. Additionally, it enables the automated generation and computation of simulations for these combinations. This method is demonstrated on a 1D-CAE model of an injection molding machine where it is shown a comprehensive search for energy-saving technologies.

We developed an “ Insight CAD system” as one of the design support technologies. The purpose of the Insight CAD system development is to check design rules automatically on 3DCAD, and to provide information about the violation parts or the violation list for designers. In this paper, we developed 3DA model utilization function that obtains the type and size of parts from attribute information and uses them for assembly rule checking. By checking the rules using the 3DA utilization function, the following conclusions were obtained. 1) Compared to the conventional similar shape recognition function, there is no misdetection of material species. 2) Regarding screw fastening, it was confirmed that the check of the diameter and length of the screw hole can be correctly evaluated. 3) Compared to the conventional similar shape recognition function, it was confirmed that the check time was reduced by 75% by applying the 3DA model utilization function.

The cathode material of lithium-ion batteries contains magnetic foreign material, which is separated by magnetic separating. However, because the cathode material and magnetic foreign material are weakly magnetic, they are all adsorbed by a high magnetic force separator, resulting in inadequate separation. There is a method to separate them by adjusting the magnetic force by clarifying the difference in magnetic susceptibility, but it is difficult to measure the magnetic force because of the small quantity of samples. Vibration of the measuring instrument also caused a lot of variation in the measured values, resulting in a large error margin. A system was needed to measure a small amount of magnetism from the small quantity of samples, remove the noise, and correct it to derive a value with a small error. In this study, the magnetic field analysis by the finite element method is used to form a uniform high-gradient magnetic field to measure the slight weight change caused by the magnetic field, and the measurement accuracy is improved by suppressing the noise during the measurement of the electronic balance with Generative adversarial network (GAN) from the aging data to suppress the variation, and the magnetic susceptibility is evaluated from a small amount of sample.

Microscrews measuring M2.0 or smaller are widely used in the assembly of smartphones, tablets, and wearable devices. To assemble delicate components, screw-tightening torques of less than 100 mNm and accuracies of ±5% are typically required. In recent years, high-performance electric screwdrivers have been developed to satisfy these requirements. However, even under strict torque control, the actual screw-fastening force remains unclear. Hence, screw tightening must be performed under various conditions using the torque method to understand the axial-force distribution of the screw and to determine the tightening conditions that satisfy the required fastening force. Directly measuring the axial force in microscrews is challenging because of difficulties in embedding strain gauges or other devices, which consequently complicates the optimization of screw-fastening conditions. Hence, this study proposes a system that accurately estimates the axial force during microscrew tightening by utilizing IoT sensing technology and a machine-learning-based regression prediction model. We design a system that senses and captures multivariate time-series data and develop a machine-learning regression prediction model based on acquired data to accurately estimate the axial force of the screws. Experimental verification using M2 screws shows that the proposed system achieves an axial-force-estimation accuracy of approximately 3%.

In recent years, there has been a demand for more efficient design of mechanical products, and research into automatic design systems using AI has been progressing. Ideally, by constructing a surrogate model that learns from actual data that combines required specifications and product structure, it is possible to create a system that presents optimal design proposals when required specifications are input. As a basic verification, a surrogate model was constructed that inputs the analysis conditions for topology optimization and infers the resulting structure. The inference accuracy evaluation showed that the topology optimization results could be faithfully reproduced in approximately 90% of cases, with an interesting trend of improvement in the grayscale region.

Due to the changing needs of the market toward carbon neutrality, rapid and flexible development of vehicles is required. Therefore, it is necessary to clarify the optimal performance solution by utilizing simulation in the early development stage. However, since simulation requires calculation time and resources, it is not easy to use it to consider the performance solution accompanying the plan change. Therefore, a data-driven surrogate model construction method using the results of simulation is applied. In this paper, we apply reduced-order modeling (ROM) using machine learning to three themes in the V-shaped development process of vehicle development. The first is engine cooling performance in the planning phase. The second is the prediction of heat distribution inside the engine combustion in the detailed design phase. The third is temperature prediction of the power converter in the detailed design phase. For each of them, reduced-order modeling using machine learning is implemented and its usefulness is shown.

This paper describes an analysis of previous studies on lattice structure mechanics and discusses the issues and future measures for emerging new tactile sensations. Through cluster analysis, these studies can be classified into four distinct groups: (1) Studies focused on improving the compressive performance of structures and products made from a single material, (2) Studies aimed at obtaining knowledge about the compressive properties in structures composed of a single material, (3) Studies focused on structures composed of multiple materials, and (4) Studies focused on specific materials. From this analysis, six issues in emerging tactile sensations were identified, leading to the following proposed future measures: (I) Apply ideas extracted from research on the mechanical properties of lattice structures to the creation of tactile sensations, and clarify how these relate to sensory impressions. (II) Fabricate samples with various mechanical properties, not limited to compressive strength and stiffness. (III) Fabricate samples by combining multiple materials. (IV) Fabricate samples with gradient properties. (V) Fabricate samples using a wide range of materials, such as hard plastics and elastomers.

In recent years, the importance of product appearance has been emphasized as a means of product differentiation, and the relationship between image features and sensory evaluation values of aesthetic preferences has been analyzed using optical simulation. However, few studies have been conducted to analyze the relationship between optical simulation videos and aesthetic preferences, considering the movement of the product itself and the movement of the person viewing the product. In this study, we propose a method for evaluating the sensory evaluation of optical simulation videos using a two-dimensional discrete Fourier transform and suggest the applicability of this method to diamond videos.

Good collaborations and emergences between diverse stakeholders are strongly necessary to solve problems and create innovative values in complex situations. However, it is often difficult to achieve consensus among diverse stakeholders on complex situations and issues. It is one of the most essential problems which obstructs collaboration and emergence. To solve such essential problem, the authors have proposed "the interactive systems approach". This approach, which incorporates a dialogue-oriented process to systems engineering, enables people to get the cognitive congruence and consensus on complex subjects effectively. This paper introduces the concept of that approach and examples of social implementation.

In this study, we focused on lower leg swelling resulting from physical loading caused by prolonged sitting posture such as when driving. The objective was to obtain prediction equations for the evaluation value of lower leg swelling using the reciprocal of the biological impedance, which represents the resistance of extracellular fluid, based on the thigh pressure distribution, the participants' physical characteristics, and the seating time. Twenty-three participants （12 males and 11 females） measured the impedance and the thigh pressure distribution in the lower leg at three different inclination angles （8, 0, and -8 degrees from the horizontal plane）. Prediction equations for the lower leg swelling were derived for male participants and female participants, and the accuracy of each equation was checked using the adjusted R².

The preference set-based design method (PSD method) allows for product designs that reflect the designer's intentions by defining preferences within the ranges of quantitative data design variables (dimensions and angular frequency) and required performance (stress and weight). However, in designs that take into account perceived quality, which has become important in recent product design, qualitative data design variables (materials and color) and required performance (customer satisfaction) are often used. In this study, we propose a PSD method that can be applied to both qualitative and quantitative data using rough set theory, which handles qualitative data, and suggest its applicability by applying it to a case study of automobile EC data.

Foreign ships armed with weapons came into the territorial seas in the late Edo period and forced the shogunate to revitalize the general plan for defending the country. Defending with cannons was planned as a strategic choice and manufacturing of cannons was promoted using the technology imported in that period. Material processing especially related to the manufacturing of cannons has already been studied in the series of research on material processing and manufacturing in that period. Structures were required by cannons deployed against the foreign ships. Structures supplied for them were mainly batteries called Daiba, including other fortifications. They were constructed on the sea and by the seashore all over the country. Components and system for defense are incorporated into the structures for defense and larger structures are composed based on their components and system. The aim of this paper is to research how the components and system of structures for defense by deploying cannons has been designed.

This study examines Design for Logistics (DfL), a concept focused on product and packaging design that considers logistics efficiency. We review relevant literature, analyze extended definitions by practitioners, and investigate 26 DfL implementation cases in Japan. The cases are qualitatively evaluated based on seven criteria from a practical DfL definition. Key findings include: 1) DfL implementation can improve communication between design and logistics departments through the use of key performance indicators (KPIs); 2) While many cases showed improvements in volume efficiency, few addressed automation or management process enhancements; 3) Most cases were from logistics companies and manufacturing industries (automotive, machinery), with fewer examples from retail and food industries. Based on these results, we propose two research directions for design support: 1) Developing methods to select appropriate KPIs that facilitate interdepartmental collaboration in DfL implementation; 2) Exploring DfL applications in the food industry, considering its unique characteristics and constraints. Limitations of this study include potential bias from relying solely on publicly available information and focusing on cases explicitly mentioning DfL. Future research should investigate similar concepts like Design for Supply Chain Management to provide a more comprehensive understanding of logistics-oriented design approaches. This research contributes to the growing body of knowledge on DfL and offers insights for practitioners and researchers seeking to improve product design and logistics efficiency.

Maximizing business continuity and competitiveness with limited resources is a challenge for all organizations, and the risk-based approach (RBA) is considered one effective means of achieving this objective. The concept of RBA has been tried and tested in various fields ranging from the guidelines of ISO 9001, the international standard for quality management, to various fields such as aerospace, cyber security, food, and pharmaceuticals. However, existing guidelines for RBA are, in principle, defined based on circumstances specific to the target high-risk industries. Therefore, it is difficult to apply these RBA guidelines directly to other fields or general products. This paper introduces a method and examples of application of risk assessment and countermeasures based on the concept of the development assurance level (DAL), a type of RBA, to quality standards for general products, with reference to ARP4754B, the guidelines for the aircraft development assurance process established for commercial aircraft.

Electric vehicles, which are expected to become widespread in Japan, can be broadly classified two types based on their charging methods: battery electric vehicles (BEVs) and battery swapping electric vehicles (BSEVs). A common challenge for both types is the environmental impact during manufacturing. One solution to this problem, attracting attention from both environmental and economic perspectives, is the reuse of batteries. Although some studies evaluating the environmental impact of both types of electric vehicles have been reported, these studies have not sufficiently considered the reuse of batter swapping electric vehicles, which have the potential to become widespread as a model that promotes reuse. In this study, the life cycle simulation was used to compare the environmental impact of the two types of electric vehicles, taking battery reuse into account. As a result, under all condition set in this study, it was shown that the environmental impact of battery swapping electric vehicles exceed that of battery electric vehicles.

To contribute to a sustainable society, it is imperative to mitigate the negative environmental impacts. Reducing greenhouse gas (GHG) emissions, resource consumption, and waste generation is essential. As such, extending the lifespan of products through reuse is crucial. In the automotive industry, leveraging recycled parts, especially remanufactured components, offers a promising avenue toward sustainability. However, the environmental benefits of utilizing remanufactured parts remain unclear. This study investigates and analyzes the remanufacturing process of automotive starters, alternators, and compressors. A life cycle assessment (LCA) is conducted to evaluate the environmental load from material procurement to the manufacturing of remanufactured parts. By comparing the GHG emissions associated with new and remanufactured parts, we quantify the GHG emissions reduction potential of using remanufactured components. Furthermore, we develop an estimation equation for the GHG emissions reduction effect based on vehicle engine displacement, weight, and the calculated GHG reduction benefits. In this study, 19 types of parts were investigated and evaluated, including six types of starters and alternators, and seven types of compressors.

In the early stage of life cycle design, detailed product shape and structure cannot be determined due to the lack of a geometric modeling environment, although they are important for selecting optimal life cycle options and determining the whole structure of a product life cycle. In this study, a voxel-based CAD system for the rough modeling and evaluation of product lifecycles in upstream design stages is proposed. A geometric modeling environment for a product and the components represented as a collection of voxels (unit cubes) is introduced in the CAD system. The voxel model allows a designer to examine a variety of shapes, layouts, and joints before creating detailed geometric models. A subsystem for life cycle assessment implemented in the CAD system supports the quantitative evaluation of product life cycle designs based on the integrated model of a product and the life cycle processes.

A new circular production system for reusing general-purpose parts within a region as common parts for various products is proposed. The components of industrial products are classified into two types: general-purpose and specialized parts, of which the general parts are designed to be reused among multiple types of products and combined with the specialized parts manufactured using resources in the region. This paper evaluates the environmental and economic performance of the regional circular production system and clarifies the conditions for this system to be established as a sustainable business. A hybrid simulation system of life cycle simulation and agent-based simulation is developed for the evaluation focusing on the business activities in the circular production in a region. As a result of a case study, it was found that the regional circular production can be profitable even after five years by appropriately managing the sales price and the lifetime of the products.

In the patrol inspection of plants, experts find out more symptoms of anomalies than novices. However, experts are aging and many of them are going to retire in a decade. Novices and robots cannot replace experts easily because it remains unclear that how experts act differently to novices and why they do so. This study presents a method for elucidating the behavior differences between experts and novices by recording inspections of experts and novices to check the behavior differences from two perspectives: inspected objects and inspection methods. The behavior differences are shown to the experts and the novices to clarify why they take such actions. Finally, we try to extract the points that experts focus on and strategy that experts use for inspection planning considering the behavior differences and the reason of the actions. This paper also presents a case study using the method, which revealed 7 distinctive behavior differences that reflects inspection planning strategies of the expert. According to the case study, experts are conscious of potential risk of each device, quality of the information they gain, time efficiency of the inspection, and development of their five senses, in general.

To realize a predictive maintenance strategy, it is necessary to maintain high accuracy in failure prediction. For this, it is essential to update a predictive maintenance system continuously, and previous study proposed an evolution-oriented predictive maintenance framework. In the proposed framework, the knowledge on maintenance and the data of operation and maintenance are accumulated in a dedicated repository and reused in the predictive maintenance system to continuously improve failure prediction accuracy and support maintenance engineers' activities. However, in this framework, the essential knowledge and data to be accumulated remain unclear. To address this issue, this paper aims to design repository, mainly targeting the support of maintenance engineer' activities. We clarified the requirements for the repository by collecting and organizing knowledge in maintenance activities based on interviews with engineers and analysis of current maintenance activity record. Based on the requirements, this study designed repository, that is, clarified knowledge representation, data representation, and the representation of the relationship between the knowledge and data. To verify the developed model, this study implemented a part of maintenance knowledge base based on the defined repository and demonstrated the maintenance engineers’ activity support by utilizing the repository. As a result, the knowledge extraction has succeeded, and this study clarified that the repository is functionable for engineers’ support.

In the manufacturing industries, the individualization of design, complex product designs becoming dependent on only a few expert designers who has tacit design knowledge, has acknowledged as a serious issue. This is because non-expert designers often lack the capacity to fully understand the expert designers’ knowledge to conduct design of target products. To address this issue, it is pivotal to explicitly describe the design process of expert designers, with the aim of enhancing understanding among non-skilled designers. Therefore, this paper focuses on the inherent act of design, in which the designers use their own recognition to predict the physical behavior of target products. This paper proposes a design process analysis method, which clarifies how designers use their own knowledge and make decision in design process. The proposed method adopts and combines two methods: Process Modeling Language for D3 (PD3), a digital triplet-based process description method that focuses on the intellectual activities of engineers, and Parameter network, a method of expressing designers’ recognition of the physical behavior of the target product. The proposed method was applied to a case study of pump impeller design to verify its usefulness. The application result showed the proposed method has an effectiveness in making designers’ knowledge explicit.

The design of new artefacts is based on hypotheses whose truth or falsity is uncertain (design hypotheses), and uncertainty is a factor in the risk of design rework. One type of information that can be used to evaluate such uncertainty is the degree of certainty of the design hypothesis, and a method to quantify this is expected. The degree of certainty is considered to be determined under complex factors such as prior knowledge, but it is not easy to take all of these complex factors into account. In order to solve this problem, this paper reports the results of the construction of a systematic method for evaluating the certainty of design hypotheses by taking into account composite factors through the application of evidence theory, and the consideration of the effectiveness and usefulness of the method.

As the focus of product value shifts from value-in-exchange to value-in-use, assessing value-in-use becomes essential for enhancing product design. However, value-in-use varies among users, changes dynamically during use, and is perceived across multiple dimensions, making it challenging to capture value-in-use comprehensively. To address this challenge, this study conceptualizes use action quality as one of evaluation criteria for value-in-use and develops a method to evaluate and visualize use action quality, taking into account of heterogeneity, changeability, and multidimensional nature. The proposed method was applied in a case study, demonstrating its validity and usefulness.

There are vibration issues, such as noise and performance degradation, that occur in the operation of large motors and transformers. To predict the vibration characteristics of industrial equipment using the finite element method, it is necessary to model components with microscopic structures as macroscopic anisotropic materials. The iron cores frequently used in industrial equipment are constructed by laminating thin electromagnetic steel sheets. Considering this structure, it is anticipated that these materials exhibit isotropic or similar properties in the in-plane direction perpendicular to the lamination, rather than being fully orthotropic. This paper presents a method to identify material properties based on natural frequencies obtained from experiments, while considering the partial isotropy of such anisotropic materials. Furthermore, we consider the selection of objective functions for determining material constants. The methods are tested for the realism of the obtained solutions and the effectiveness, using a case study for a component of industrial equipment.

A driver model that can imitate human driving is indispensable for autonomous driving and driving simulations when develop automobiles. Existing driver models require some information, such as target trajectory and vehicle coordinates, to determine driving operations. However, such driver models can’t be used in environments where the information above can’t be obtained. Therefore, research has been conducted on the driver model that inputs only perceptual information and outputs driving operations. Previous research adopted the Neuroevolution, which used a neural network as the driver model controller and a genetic algorithm as the learning method, but this method didn’t allow the driver model to learn multiple courses with different characteristics and was able to run through all of them. Also, when apply the Neuroevolution, it is difficult to construct a driver model that can drive around sharp curves. In this research, we aimed to resolve these issues.

Tree volume estimation is important for fruit and forest management as it provides information on tree health, estimated growth, and yield. Measuring the volume of a whole tree, including branches, is time-consuming and costly. This study proposes a method to calculate the entire volume of tree trunks and branches by combining 3D reconstructed point cloud data and density-based clustering. The effectiveness of the proposed method is demonstrated based on a quantitative evaluation comparing the tree volume calculated by the proposed method with the volume of a tree mesh model.

This study proposes a topology optimization based on a Darcy equation and turbulent flow model for generating innovative two-fluid heat exchangers. We introduce a pseudo boundary layer to the Darcy flow model to imitate effect of boundary layer in turbulent flow, which is ignored in the original Darcy equation. First, we optimize a design variable field that express the configuration of each fluid region and solid wall region. Then, we evaluate the performance of the optimized result using a turbulent flow model. We apply the proposed method to rocket engine heat exchanger, where liquid oxygen and hydrogen gas should exchange their heat under extreme conditions, to demonstrate the effectiveness of our method against turbulent flow.

Recently, there has been a demand for compliance with European environmental regulations and initiatives towards zero carbon dioxide emissions, necessitating the consideration of environmental impacts during product development. In particular, it is essential to explore the use of recycled materials, the reuse of components that make up products, and the ease of disassembly. In this context, we propose a search method using graph convolutional networks to identify candidates for parts that can be made from recycled materials. We have developed a technology to search for potential sites for the application of recycled materials based on the required specifications. By searching for candidates using the parts list, we can reduce the amount of work needed for environmental evaluation. This report discusses the technology to explore potential sites for the application of recycled materials and present them as design proposals.

In the manufacturing industry, due to the increasing awareness of environmental issues and stricter regulations, there is a demand for low environmental impact manufacturing. However, when aiming for low environmental impact manufacturing, there is a problem of not being able to utilize environmental impact indices in front-loading design, preventing a fully optimized product design. Therefore, this paper proposes a system that uses CAD data, which is the de facto standard in design, as an input and interface to estimate the amount of carbon dioxide emissions during product manufacturing, thereby enabling the consideration of environmental impact indices at the design stage. When applied to a test model using four kinds of processing (surface polishing, MIG welding, SFW, and electrochemical processing), the system was able to visualize the amount of carbon dioxide emissions during manufacturing in a matter of seconds, showing promise for utilizing the proposed system in front-loading design.

In recent years MBSE (Model Based Systems Engineering) has gained popularity as a useful methodology for overviewing the whole structure, functionality and requirements of products and identifying the impact range of design changes. However, construction of system models for MBSE is time-consuming, which prohibits the usage of MBSE in actual product design scenes. Our goal is to reduce human effort in constructing MBSE models by automatically generating SysML (Systems Modelling Language) diagrams from design documents. In this study, as an advancement of the automatic generation technology for the requirement model and component structure model, we create a functional model from the functional definition document and develop the automatic generation technology for the MBSE composite model that links the functional model, requirement model, and component structure model. The following summarizes the key points of the obtained results. We have constructed a prototype that automatically generates the MBSE composite model by creating a functional model from the functional definition document and linking it with the requirement model and component structure model. We have developed a function to automatically generate sub-models composed of specific related data of requirements, functions, and component structures in response to the designer's query..

In innovative product development, in addition to the direct product development team, it is necessary to consider a complex system of multidisciplinary and multistakeholder groups, including the organizations and related businesses that are the main actors in product development, such as research, prototype production, mass production, and business, as well as users who utilize the products, regulatory authorities that socially guarantee user safety, and local governments that promote industry as a result of social implementation. In addition to the organization and its related businesses, there are users who utilize the product, regulatory authorities that socially guarantee user safety, and local governments that are involved in industry promotion in conjunction with social implementation. Since such development exhibits high uncertainty in which many factors affect product specifications, it is necessary to synchronize and coordinate the product development process with stakeholder interests. This paper presents the results of a study of collective impact practices based on the ISO 15288:2023 System Life Cycle Process in order to set up a process for proceeding with such development.

Product line design based on module commonization is a strategy to expand the number of product types while maintaining mass production effectiveness. Although there have been several studies on product line design, it is a multi-objective optimization problem, including evaluation of the commonization ratio and performance, which makes it difficult to find an optimal solution, and the number of optimal solutions tends to be large. Therefore, it is difficult to derive a single optimal design solution. In addition, as the design process progresses, uncertainties are introduced, and it is not appropriate to seek detailed design solutions at the stage of broadly defining a product line policy. In this study, we attempted to support the formulation of product family design strategies by automatically extracting and presenting trends in the structural characteristics of optimal solutions. In addition, we improved the solution search efficiency by limiting the solution search to a specific region while checking the trend of the solution space.

The semiconductor manufacturing industry, particularly the etching process, faces significant challenges in achieving optimal line balancing due to variability in process times, equipment availability, and material handling intricacies. This study addresses these challenges by developing an advanced Decision Support System (DSS) that integrates Data Analytics (DA) and Internet of Things (IoT) technology. The primary objectives are to enhance productivity by integrating DA with the DSS and to improve decision-making through real-time IoT systems. By incorporating real-time data analytics, the DSS provides actionable insights to streamline operations, reduce downtime, and enhance process efficiency and product yield. The comprehensive framework developed forms the backbone of the DSS, ensuring robust and informed decision-making. Additionally, the DSS incorporates predictive analytics to forecast equipment maintenance needs, preventing unexpected breakdowns and minimizing downtime. This predictive capability is crucial for maintaining continuous and efficient operations. The system also facilitates better resource allocation by analyzing historical data to predict future demand and adjust production schedules accordingly. Case-based validation demonstrates significant productivity improvements and enhanced decision-making capabilities, highlighting the potential of integrating data analytics and IoT technology to transform line balancing in semiconductor manufacturing. This integrated approach addresses current challenges and sets a foundation for future advancements in manufacturing efficiency.

Repairing products using recycled parts, such as rebuilt and reused parts, has low environmental impact and cost. However, the product value may be inferior to that of replace the car or repair by a new part. Thus, it is difficult to determine which repair option is most appropriate for a user when comprehensively considering the three perspectives of “product value,” “environmental impact,” and “cost.” This study uses a questionnaire to suggest which of four options: “repair by a reused part,” “repair by a rebuilt part,” “repair by a new part,” and “replace the car” in the event of a car breakdown is most appropriate for users from the view point of product value, environmental impact, and cost. Particularly, by analyzing and evaluating users’ respective values for their cars through a questionnaire, we propose an evaluation method to suggest repair options that are appropriate for each user. This paper discusses the effectiveness of the proposed evaluation method using the engine repair of a Kei car and SUV, and the bumper repair of Kei car and SUV as a case study.

Automobile chassis parts such as steering knuckles require high strength and are therefore cast in one piece, eliminating the need of joint. Achieving light weight and high rigidity requires a three-dimensional shape design that enhances structural strength, however it is difficult to produce structures such as 2mm thick ribs using current sand casting methods. It is known that sound and vibration performance generally deteriorates as weight is reduced, resulting in a trade-off with fuel economy. In this study, we attempt a new method of casting using molds to examine a design that can achieve both light weight and high rigidity as well as improved sound and vibration performance by removing the primary resonance frequency from the road noise frequency range. In a design results in this stduy shows a capability to escape the main resonance frequency from the road noise frequency range.

This research proposes a new model for describing interactions among system elements during design changes and a method for determining which elements and in what order should design changes should be performed. The model is based on the system architecture map (SAM) model, which describes the interaction of attribute values that define system characteristics and can describe the possibility of offsetting design change propagations.

To optimize the design change process, a multi-objective optimization was performed using a genetic algorithm, with various design difficulty factors as evaluation functions. The resulting Pareto solution set highlights the trends among superior solutions and supports engineers in decision making.

In recent years, changes in social systems have intensified, and supply chains need to be flexible to the environment. However, supply chains are complex systems and change is not easy because of the possibility of unintended side effects. In this study, we modelled the supply chain in three domains (value, function and actors) and implemented a multi-agent simulation based on the model to support changes in the manufacturing supply chain. The results of the simulation are organized in terms of the bill of materials to quantitatively show the influence relationship between value, function and actor, and to identify which parts and which production process are bottlenecks in the current situation. Based on the influence relationships, we developed a method to compare the proposed changes based on cost performance and to reevaluate the proposed changes by simulation. The proposed method was applied to an virtual household appliance, and the results showed that the proposed changes had a certain degree of validity, suggesting the effectiveness of the method.

This study explores the potential application of Large Language Model (LLM) in the field of mechanical engineering design review. The research highlights how LLM can effectively complement traditional design review processes by improving the accuracy of issue detection using well-crafted prompts. While the results demonstrate the potential of LLM to identify design flaws effectively, particularly in scenarios where hazards are clearly linked, challenges remain when the models are required to address more complex designs involving specialized terminology, materials, and processes. The study suggests that by refining prompts or specifying review perspectives, LLM can improve their accuracy, though further work is needed to develop methods that enable comprehensive design reviews without pre-defined focal points.

In the automotive industry, there is a growing demand for handing down the skills of failure analysis these days. However, failure events are phenomena that occur in a chain reaction, making them difficult for beginners to understand. While a Knowledge Graph (KG) that can structure information is effective in describing failure events, understanding KGs themselves is not easy. On the other hand, there is growing anticipation for the use of Graph RAG, a type of Retrieval-Augmented Generation (RAG) technology that utilizes large language models (LLMs) and KGs for knowledge management. However, when using Graph RAG with an existing knowledge graph for automobile failures, several issues arise because it adopts Semantic Parsing-based Method. This study proposes an optimized Graph RAG pipeline for existing knowledge graphs by adopting Information Retrieval-based Method. By using an original Q&A dataset, the ROUGE f1 score of the sentences output by the proposed method showed an average improvement of 157.9% compared to the existing method. This indicates the effectiveness of the proposed method for failure analysis of automobiles.