The increasing complexity of modern manufacturing systems demands advanced decision support app; iDSS-ProLean, that can process vast amounts of real-time data for optimal performance. This paper introduces a modular sensor architecture aimed at enhancing decision support systems (DSS) revolving a manufacturing environment through the seamless interchange of IoT data. The proposed architecture enables flexible integration of multiple sensor types to capture critical operational parameters, such as cycle time and total processing time, in a semiconductor production line. By modularizing sensor configurations, the system ensures the adaptability, allowing it to respond dynamically to changing production requirements. The interchangeability of sensors further supports the system's ability to collect data from diverse sources, facilitating comprehensive data analysis for an informed decision-making. Through integration with a cloud-based DSS, this architecture provides real-time minitoring into production efficiency, resource allocation, and process optimization. The paper demonstrates the utility of this approach based on the semiconductor manufacturing setting, highlighting its potential to optimize lean manufacturing processes through DSS, improving operational efficiency via the data-driven decision-making.

Increasing the organisation's productivity can directly affect operational and financial performance and is regarded as a crucial tactic for achieving industrial excellence. Productivity is a fundamental metric that affects work-in-progress inventory, production, process utilisation, product cost, and on-time delivery. Hence, engineers and managers are under a lot of pressure to improve organisational performance. The Man-to-Machine-Ratio (MMR) was changed from 1:1 to 2:1 to improve machine coverage and increase the Mark, Scan, and Pack (MSP) process performance for product line X. However, this modification has resulted in a lower productivity output of only 4,000 pieces per man-hour compared to the management objectives of 6,000 pieces per man-hour. In this study, the researchers have concentrated on increasing the Personnel Efficiency (PE) at the MSP process while identifying Non-Value-Added (NVA) activities. To increase productivity, the researchers used the Define, Measure, Analyse, Improve, and Control (DMAIC) technique of the Lean Six Sigma process. A detailed examination of work procedures, motion, and time was carried out to classify jobs as Value-Added (VA) or NVA using work study tools and Kaizen worksheets. The results showed an increase in production from 4000 to 6000 pieces per man-hour by reducing labour steps and improving the MMR from 2:1 to 1:1. The study's findings demonstrate that lean Kaizen techniques and work element analysis have been successfully used to enhance PE and productivity in manufacturing operations.

Small and medium-sized manufacturing companies need to improve their productivity, but their managers do not have the time to analyze their actual business conditions. Therefore, we developed a tool to easily visualize how lead-time reduction through improvement activities leads to corporate profits and to support investment decisions, and provided it to managers of small- and medium-sized manufacturing companies. Profitability was analyzed by calculating KGI (return on sales and return on invested capital) using lead time (LT) reduction as a KPI, and investment decisions based on the net present value (NPV) method were verified in an actual manufacturing company. The tool was used to simulate the planned improvements: a 14% reduction in LT would increase operating margin from 3.6% to 7.4%, return on invested capital (ROIC) from 4.5% to 7.7%, and a payback period of 2.8 years. These results are generally consistent with the experience and intuition that have been the criteria for decision-making by management, and suggest the effectiveness of this tool. Therefore, we conclude that this simplified tool can be an effective tool for managers of small- and medium-sized manufacturing companies to support “visualization of profitability” and “capital investment decisions” by shortening LT.

This bibliometric review explores the development and trends of lean warehousing (LW) research from 2004 to 2024, investigating 333 publications indexed in Scopus. Using the VOSviewer software and Scopus database, the study examines research clusters, publication patterns, geographical distributions, and contributing authors. The findings highlight two developmental stages: a preliminary foundational phase (2004–2012) and a growth surge (2013–2024). Six key research clusters are identified, focusing on LW, lean manufacturing, lean principles, 5S execution, picking processes, and warehouse management. Peru, the United States (US), and India dominate publication output, while Southeast Asian nations show notable research gaps. Even though the field has matured in leading nations, gaps remain, requiring further investigation. This study underscores opportunities for advancing LW theoretical frameworks, especially in Malaysia, where publication output remains limited.

Biocomposite materials have gained significant interest as sustainable alternatives across multiple industries, requiring an effective and systematic selection procedure to guarantee optimal performance and environmental compatibility. This paper analyzes contemporary research on the selection of biocomposite materials in several industries, including automotive, aerospace, marine, electrical, packaging, and safety equipment. These studies utilize several decision-making methodologies to determine appropriate material combinations. The review classifies selection criteria into five principal categories: material properties, sustainability factors, processing characteristics, cost and availability, and application requirements. Each criterion is subdivided into sub-criteria and parameters, demonstrating a detailed and thorough approach to selection. The results reveal that material properties constantly rank as the most prioritized factor, followed by cost and availability, and processing characteristics. Essential sub-criteria encompass weight, mechanical performance, and pricing, with density, tensile strength, and cost recognized as the most prioritized parameters. The findings from the review indicate that despite the growing awareness of sustainability, the selection of biocomposite materials frequently prioritizes performance and economic aspects over sustainability. This is proven by the emphasis on material properties, cost, and processing attributes, with challenges in balancing trade-offs with other criteria. In addition, a lack of standardized sustainability metrics and the perception of biocomposite as inherently sustainable might also contribute to their limited inclusion as selection criteria. Therefore, the development and integration of standardized sustainability metrics into the decision-making process is imperative.

This review paper synthesizes the findings from four significant studies on the integration of Design Thinking (DT) and the Theory of Inventive Problem Solving (TRIZ) in engineering education and practice. Over the past two decades, these methodologies have been explored for their potential to enhance creativity, problem-solving, and innovation. Design Thinking emphasizes human-centered design, empathy, and iterative prototyping, while TRIZ focuses on systematic problem-solving and inventive principles derived from patent analysis. By combining these methodologies, designers can leverage their complementary strengths to overcome psychological inertia, generate diverse ideas, and develop effective solutions to complex problems. This paper provides a comprehensive analysis of the effectiveness, benefits, and challenges of integrating DT and TRIZ, drawing on case studies, experimental research, and practical applications. Key findings include the enhancement of ideation processes, improved design efficiency and effectiveness, and successful practical applications in product development. The review highlights the potential of this integrated approach to foster innovation in engineering and product development, offering a foundation for future research and practice. Despite the benefits, challenges such as the complexity of TRIZ tools and the need for extensive training are noted. Simplified TRIZ processes can help mitigate these challenges, making the methodology more accessible to non-experts. Future research should focus on developing integrated frameworks and tools, as well as conducting more case studies and experimental research to validate the effectiveness of these approaches in different contexts and industries.

Although robots for harvesting fruits for efficient agricultural production are widely studied, there is little attention on collection of fruits scattered on the ground such as camelias. This study aims to develop a robot that can harvest fruits scattered around trees and propose a trajectory generation algorithm for an efficient harvesting motion. The traveling order of the trees which passes each tree only once and returns to the starting point, is determined by using an approximation of the traveling salesman problem. The trajectory for the harvesting motion is designed as a spiral trajectory with equal intervals around each tree, and the trajectories are connected to each other to form the whole trajectory. Experimental results are shown to confirm the harvesting operation of a mobile robot using the proposed method.

The pre-cut construction method, in which the wood used for housing columns and beams is machined in a factory and assembled at the construction site, is widely applied. The wood is processed in such a way that it can be assembled by inserting a tenon (convex part) into a mortise (concave part). This study considers rectangular tenon machining and proposes a method to represent the machining status as a graph whose edges are the number of linear movements of the tool required for machining based on the tool diameter. Next, to avoid non-cutting tool movement, a single-stroke tool path is generated based on the properties of the Euler graph. It was confirmed that an efficient machining path could be generated through simulation.

Upgrade design requires appropriate product architecture, upgrade parts, and upgrade cycle planning. In addition, since parts information changes over time, it is necessary to take future uncertainty into account. However, previous studies have not sufficiently examined methods for determining upgrade plans that take future uncertainty into account. Therefore, the purpose of this research is to evaluate upgrade suitability in terms of environmental load and cost considering uncertainty, and customer dissatisfaction, which expresses product obsolescence over time. The proposed method is also applied to a laptop design problem to verify its effectiveness.

Against the backdrop of the challenges facing Japanese agriculture, such as the declining birthrate, aging population, and lack of successors, the objective of this research is to introduce digital transformation (DX) using a small- to medium-scale cucumber farm as a case study to improve the efficiency and sophistication of sorting operations. In particular, the aim of the project was to build a sorting system that can be handled even by newcomers to the farm by transforming manual cucumber sorting work, which relies on the experience and tacit knowledge of skilled workers, into formal knowledge using AI's image recognition technology. By using image recognition technology, we verified the possibility of automating the sorting work that has traditionally relied on the manual labor of skilled workers, and of improving work efficiency and sorting accuracy even for new farmers.

As a result of a demonstration experiment using the prototype sorting machine, it was possible to reduce sorting time by up to 57.6% and improve sorting accuracy by up to 80.4% compared to conventional manual work. The results of this research provide a new approach that contributes to improving the management efficiency and reducing the workload of small and medium-sized farmers.

This is an activity report in 2024 of the industry-academia-government-finance collaborations for manufacturing DX that has been jointly planned and promoted by Musashino Bank and Toyo University and developed in Saitama area since 2018. As reported in IIP2023 and IIP2024, this academy is based on the combination of lectures, exercises, factory visit tour and discussions among participants. Participants from companies join in this group for solving the company’s own technical issues. Final goal of each participant companies is to improve productivity or to develop preventive maintenance by learning in this academy. In this report, I briefly report its activity in 2024 with some considerations on how the “space for the collaborative creation” are promoted for engineers in different companies.

A total of approximately 6,400 PowerPoint slides covering manufacturing technology, production management, data science, etc. were used in the "Production Systems Design" course at Tokyo Metropolitan College of Industrial Technology from 2020 to 2024. Student reports on these lecture slides were analyzed, and the following trends were found: 1) There is high interest in 3D printers, CNC, laser processing, and Gigacast. 2) 70% of students had experience using IoT, and 50% of students expressed a desire to be actively involved with it after employment. If negative attitudes are included, 90% of students were positive. 3) Regarding AI, there is a high level of interest in AI after employment. 55% of students expressed a desire to be actively involved, and if negative attitudes were included, 95% of students were positive.4) The social issues that generated the greatest interest were the energy crisis, global warming, global population trends, soil pollution, and marine plastics.5) The fields of application of IoT proposed by students were smart factories (35%), agriculture and forestry (20%), food and beverage/logistics (20%), and when medical and welfare are added, service work accounted for 35%, showing an expansion in the field of IoT applications.6) Video lecture materials were easy to understand (40%) or somewhat easy to understand (45%), so the use of explanatory videos of about 3 minutes was effective.

Currently, there is a strong need for developing a system for monitoring psychological stress monitoring system at work to improve the quality of life (QoL). This study proposes a system that records and analyzes work stress based on autonomic nervous system activity estimated from daily ECG measurements. The ECG signals are continuously recorded and transmitted to a server terminal, where they undergo preprocessing, including noise reduction to eliminate motion artifacts. The processed signals are then analyzed to extract heart rate variability (HRV) and compute the ratio of low-frequency (LF) to high-frequency (HF) HRV which is used as a stress indicator. The system was evaluated by measurements under stress stimulus and showed satisfactory performance. This real-time monitoring system enables continuous assessment of stress levels in a work environment.

Robots are required to automate the inspection of underground infrastructure structures. Therefore, a high-precision self-position estimation technology is needed that does not require line measurements and has a margin of error of only a few millimeters per several tens of meters. In a previous study, visual odometry (VO), which estimates self-position from captured video images, was investigated. The accumulated error between the camera mounting angle and VO processing was identified as an issue. The objective of this study was to increase accuracy by eliminating mounting angle errors and modifying VO processing. This paper reports the results of the study on the calibration function and the change of feature points used.

Nondestructive testing is beginning to be introduced to inspect spot welds in the assembly process of automobile bodies and parts. To automate the inspection process, a spot welding inspection robot system integrating MatrixeyeTM and the industrial robot have been developed. In the conventional system, the position at which the ultrasonic probe contacts the weld is pre-determined by teaching. In this development, a function to correct the contact position of the ultrasonic probe based on the detection of weld marks was added to the conventional system. In this paper, we introduce the weld mark detection method, and the results of the verification of the position correction function of the ultrasonic probe on a real robot system.

In recent years, the aging of social infrastructure has been accelerating. In particular, pipeline inspections in confined spaces face challenges such as time and labor-intensive manual inspections and inconsistencies in inspection quality, making the automation of inspections using robots highly anticipated. This study discusses the results of improvements made to the mobility mechanism of a 30mm-diameter pipeline robot, developed in previous research, to enable its application in branch pipes. Additionally, it presents the outcomes of efforts aimed at miniaturizing the mechanism to accommodate even smaller-diameter pipelines.

It is important to perform diagnosis of buried pipes using nondestructive testing and reflect the results in any renewals or repairs. To establish a method to learn data on graphitization corrosion of water main and generate a large amount of data on the thickness distribution of corroded pipes, we create training data under the distribution of the interface roughness model. Using the training data, we build a generative adversarial network, which is one type of generative AI. This allows us to obtain images representing various types of corrosion and generate samples whose statistical properties can be obtained. However, in order to do so, we need to ensure that the samples are accurate and diverse. We propose to generate samples using a conditional generative adversarial network with gradient penalty. This enables the generation of diverse and accurate images and image generation under physical constraints. Here, we compare and evaluate the statistical properties of the parameters of the interface roughness model after resizing and generation by CWGAN-gp for the original image.

To prevent aging in buried water pipes that have exceeded their service life, it is crucial to assess pipe conditions using non-destructive testing and apply the findings to pipe renewal and repair strategies. This study investigates the coupled vibration of a water pipe with an attached bar-like structure. The pipe and bar are modeled as a circular ring element and an elastic bar element, respectively, connected via spring elements. The experimental modal analysis (EMA) was performed to reveal the coupled vibration phenomena between the in-plane bending mode in water main and the longitudinal mode in elastic bar. As the results, the existence of the coupled mode between the in-plane bending mode and the bar deformation mode around the 1 kHz. In addition, the isolated longitudinal mode of bar structure was obtained the high frequency band over 3 kHz.

A simple evaluation system for the grain size distribution of rocks has been developed using an RGB-D camera. The system uses a camera to acquire color and depth images of rocks stacked randomly, and then estimates the surface area and volume of individual rocks within the angle of view through image area segmentation and shape completion processing to obtain a detailed grain size distribution. The accuracy of the average diameter estimation was evaluated by comparing the diameter obtained by proposed method with the diameter experimentally obtained by the conventional methods. As a result, the error in the volume equivalent diameter was 3.4 mm from the value obtained by the conventional method (39.1 mm), and the error in the surface area equivalent diameter was 0.5 mm from the value obtained by the conventional method (42.3 mm). It was confirmed that the average diameter obtained by developed method agrees with that by the conventional method within the expected systematic error of 5 mm for the entire developed system.

75% of machinery failures are attributed to surface and contact-related issues, making the early diagnosis and maintenance of surface damage in machine elements a critical task. In recent years, the importance of equipment maintenance has increased due to the aging of facilities, necessitating continuous monitoring and maintenance through the use of sensors and information processing. In this study, Acoustic Emission (AE) was employed to monitor the operating conditions of grease-lubricated thrust bearings. It was confirmed that the peak positions of adhesive wear varied due to changes in the grease distribution inside the bearings, suggesting that AE sensors could be used to monitor grease distribution.

Wire ropes are widely used in industrial machinery, such as elevators, and it is important not to overlook wire breaks on maintenance. As for elevator wire ropes, legal inspections are required to be conducted by qualified inspectors to visually check the condition, and the use of digital technology as represented by Internet of Things (IoT) is desired for streamline management. In elevator maintenance, magnetic inspection device as a non-contact rope tester is used to inspect wire ropes, where the condition of the rope is determined by the amount of magnetic leakage. However, since the rope tester scans the rope surface one side at a time, there is a risk of missing a broken strand depending on the scanning timing, considering the rotation of the rope. Therefore, there is a need for a technology that can scan the entire circumference of the rope at the same time. This study proposes a contact-type damage detection system using an Acoustic Emission (AE) technology to detect damage on the wire rope surface. AE is observed as a wave with a high frequency component. We have developed a probe that make contacts along the circumference of the wire rope. When the probe approaches the damaged part of the rope, it is deformed by being caught in the damaged area. In the process of returning to the original position, AE is emitted from the probe to be detected as damage. This system was implemented in the actual elevator. We report on the results of detecting three sample damage points scattered on the surface of a wire rope.

Previous studies have shown that the degree of damage to bridges can be estimated by monitoring the number of large vehicles passing over them. Optical counting methods, such as cameras, exist; however, their accuracy is affected by factors such as time of day and weather conditions. Building upon previous research, this study expands the scope to include not only the counting of large vehicles but also medium and small vehicles using a custom-designed vibration sensor. By analyzing output waveforms through machine learning and anomaly detection methods, this approach enables the estimation of overall traffic volume on the bridge, providing further insights into bridge health and lifespan.

We have proposed a soundness evaluation method for RC slabs. In this method, when AE generated on a road surface while a vehicle is running is observed using an AE sensor installed on the underside of the RC slab, the more the RC slab deteriorates, the more the propagation of elastic waves is hindered, and the density of the observed AE source becomes thinner. The source position of elastic waves generated by a vehicle is running is expected to vary depending on the weight and passing position of the vehicle. Therefore, driving tests were conducted under various conditions on a simulated bridge, and the wave source location was mapped. It has become clear that mapping from road tests and compressive strain distribution by FEM are consistent, and a method has been developed to efficiently obtain the strain distribution caused by a vehicle is running and map the predicted wave source location based on modal time history analysis.

This study proposes a method for automatically generating End-to-End (E2E) test code from product documentation using a large language model (LLM). The product documentation refers to materials such as manuals, tutorials, FAQs, and step-by-step operation guides that help users achieve their objectives with the application. The proposed approach aims to generate E2E test code with improved coverage and quality by leveraging specific and detailed instructions in the product documentation. We generate E2E test cases and subsequent test codes through separate prompts to the LLM, taking these documents as input. We used a web application with six functionalities in an experiment and compared different document types when evaluating test code generation. Results showed that product documentation-based tests achieved higher functional coverage, suggesting the advantage of detailed user operation guidance for deriving comprehensive test scenarios. By contrast, requirements documents, and user stories were effective for different functionalities subsets, pointing to each document type's complementary strengths. These findings indicate combining or enhancing documentation sources can produce more robust test coverage and improve overall software quality. This approach offers new insights into efficient software development practices that integrate LLMs with existing documentation resources.

In the manufacturing process of Membrane Electrode Assemblies (MEA) fuel cells, metallic particles buried into the Gas Diffusion Layers degrade the battery performance. In this study, we propose a real-time diagnosing system of Fe particles buried into MEA, namely PFPA-LDS. PFPA-LDS is composed of several units of pore fluid pressure impact generators and permanent magnets, to excite natural oscillations and modulations through magnetic and Lorentz forces in response to metallic contaminants. The excited oscillation is measured using laser displacement sensors, and contamination probability is estimated by the proposed deep learning system. PFPA-LDS was experimentally applied to both samples of MEA with the presence or absence of Fe micro-particles. Consequently, the contaminated MEA was estimated to have a higher contamination probability than the normal one. In conclusion, the proposed method can distinguish the existence of Fe micro-particles, so it has an effective potential as a diagnosing system of Fe micro-particles into MEA.

The authors proposed an automatic diagnosing system, namely PFPA-LDS, of metallic micro-particles buried into Membrane Electrode Assemblies (MEA) fuel cell. This can assess the contamination information according to the spatio-temporary changes in pore fluid pressure excited oscillation under static magnetic field. PFPA-LDS is composed of several units of pore fluid pressure impact generators and permanent magnets, to excite natural oscillations and modulations through magnetic and Lorentz forces in response to metallic contaminants. In this study, the multi-physics simulation was carried out to elucidate the detection mechanism of metallic particles by PFPA-LDS. So, the multi-modal oscillation energy, corresponding to Lorentz force and magnetic force, was visualized by applying mode decomposition of the simulated results. Consequently, the oscillation energy distribution revealed that Lorentz force had the damping effect to natural oscillations, which depended on not only the existence but also the position of Fe particle, additionally oscillation mode and magnetic field distribution.