The Proceedings of Manufacturing Systems Division Conference (2025)

The urgency to responsibly use limited natural resources in the face of global warming and environmental concerns necessitates a shift in manufacturing paradigms towards sustainable practices. While quality-focused approaches like Lean manufacturing (LM), Six Sigma (SS), and Lean Six Sigma (LSS) have made strides, their impact on environmental sustainability remains under-addressed, contributing to environmental degradation. This study examines the rise of Green Lean Six Sigma (GLSS) as an augmentation to operational strategies, emphasizing resource efficiency, environmental mitigation, and reduced carbon footprint. Despite increasing attention, comprehensive studies on GLSS are scarce, warranting an in-depth analysis. Employing bibliometric analysis, this study scrutinizes GLSS trends, publication sources, clusters, and potential research areas. Findings highlight a growing trend in GLSS publications, with India, the UK, and Morocco leading in contributions. Key journals and prolific authors in this field are identified. Keyword co-occurrence analysis reveals thematic clusters emphasizing CSFs, sustainability, and methodologies. The study underscores the need for further empirical investigations and theoretical definitions in GLSS to bridge existing gaps and operationalize sustainable initiatives effectively.

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Overall Equipment Effectiveness (OEE) is a lean manufacturing (LM) method that focuses on availability, performance, and quality to identify areas of waste and inefficiency. However, Industry 4.0 has begun to pose challenges to the system due to its data complexity and unique interoperability concerning the part of fundamental handling. As a result, the goal of this study was to combine the two ideas into a single, functional system that incorporates the costing aspect. The system is known as an OEE Simulation-based Decision Support Tool (OEE-SDST) for, and it was modelled after the original research of Nakajima (1988). The primary innovation in this entire work was the integration of the average cost by linking the efficiency of the OEE with a simulation application. The foundation for both designs and the development of the OEE-SDST was Microsoft® Visual Basic 2010. The OEE-SDST features a graphical user interface (GUI), and the study assessed its software usability through criteria such as understandability, learnability, operability, and attractiveness, adhering to ISO/IEC 25010:2011 or ISO/IEC 9126 standards. Approximately 78% of the 50 respondents affirmed the capabilities of the OEE-SDST. To sum up, the OEE-SDST has been proven to be effective in improving the performance of LM.

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Electric vehicles (EVs) revolutionized the automotive industry, necessitating revolutionary structural integrity and performance solutions. In this Paper, A strut bar is required in electric vehicles to improve both driving stability and safety. This study investigated the crucial function of strut bars in improving the structural behaviour of EVs, where there was a scarcity of data on the relationship between the geometry of the EV strut bar and its structural performance under static loading. The data was required as a reference for improving the design of similar components. This had the potential to impair driving dynamics and safety. The purpose of this research was to investigate the structural performance of strut bars in EVs under static loading circumstances. Begin optimising the strut bar design by identifying the load using vector analysis, then define the geometrical specifications based on the BMW i3 baseline component. After that, choose AISI 1018 as the material, create a 3D geometrical model in CATIA V5, and then perform FEA static analysis in the same software to evaluate structural performance. This provided a thorough understanding of their structural behaviour, boundary condition and magnitude load distribution. The maximum von Mises stress, denoted as σmax, is 138 MPa, with a corresponding maximum deformation, ∂max, of 0.531 mm. The maximum stress location is determined, and the safety factor is calculated as 370/138, yielding a value of 2.68. The study's findings can be used as a useful benchmark for establishing safe load limits for electric vehicles. The research validates the structural performances of the EV strut bar. This knowledge enables engineers to create EV strut bar designs that are unique in terms of shape and positioning within the vehicle. The findings serve as a critical benchmark for determining safe load limits for electric vehicles.

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Headrests are an essential safety feature in automobile seats as they are not only for comfort, but more importantly prevent, or reduce the severity of whiplash injuries caused by rear-end collisions. In addition to functional obligations, headrests must also adhere to sustainable design principles, which encompass the reduction of emissions, conservation of resources, and environmental friendliness, for more ecologically aware future. This paper presents a problem modelling process based on the Theory of Inventive Problem Solving (TRIZ) method, starting with Product Analysis of an adjustable type of automotive headrests. Three steps have been performed in Product Analysis that are Component Analysis, Structural Analysis, and Function Analysis. Then, Trimming process was done, where the selected components materials were change to biocomposite for a more sustainable automotive headrest. The functions with disadvantages or can cause harmful functions were also reduced or eliminated during trimming process. The trimmed function diagram reveals that the biocomposite headrest structure have a main problem that is insufficient holding function. Based on this problem, a Cause-and-Effect Chain Analysis was performed to identify the key disadvantages. This is important to avoid mistake by implementing ineffective solutions, due to wrong root cause identification. The result is the key disadvantages together with proposed countermeasures. The outcome of the study can later be used for future work on contradiction analysis.

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Disassembling is an important process for machinery, in cases of such as its maintenance or disposal. Several approaches of disassembling of machinery are proposed and discussed so far, the examples dealt with in the studies are rather specific; that is, there are no such things as general problems to be dealt with. In order to evaluate the performance of a disassembling process, a general-purpose disassembling problem generator should be conducted. In the current study, we develop a disassembling problem generation based on random number generator. A random placement process based on a random walk is adopted to generate block-pattern-based disassembling problems. A generated problem and its disassembling process obtained based on the positional relations matrix are demonstrated.

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Microscopic work done under a microscope can be a very inefficient process. There are many delicate tasks where unskilled operators often make mistakes. For this reason, time is wasted in learning how to use microtools that are suited to the size and characteristics of microscopic objects. In this study, a powerless displacement reduction mechanism using magnetic force have been developed. The developed mechanism can reduce fingertip displacement from a few centimeters to several tens of micrometers with a single mechanism, and the full stroke and displacement reduction rate can be adjusted by the gap between the magnetic poles. The usefulness of precision tweezers constructed with this device for microscopic work has been investigated. -

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In this study, piezoelectric thin-film earphone speakers were designed, fabricated, and evaluated. To enhance the acoustic output, the device design was optimized by finite element analysis. The two types of prototype speakers were fabricated using the piezoelectric PZT thin film deposited on a stainless-steel substrate on which polyethylene films were covered as a diaphragm membrane to improve the acoustic output. The vibration displacement and sound pressure of the speakers were measured. We confirmed that the sound output was improved by using polyethylene film as the vibrating film in both of the two shapes. The speaker with the taper cantilevers achieved a sound pressure level of 90 dB at 4 kHz in the rubber tube.

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In manufacturing equipment, there are high expectations for so-called in-line monitoring technology, which detects damage while the original function of the equipment is operating from the viewpoints of real-time performance and speed. This study proposes a non-contact acoustic emission (AE) method for in-line monitoring. AE is weak elastic wave generated by cracks, rubbing and other damages in materials. We focused on the fact that AE waves originating from the bearings of rotating rollers propagate through the cylindrical body as guided waves and also propagate slightly into the air. The experiment showed that an air-coupled ultrasonic sensor inclined to the object could detect AE waves resulting from damage inflicted on the outer ring of a rotating bearing in a non-contact manner. This technique is promising for an inspection technology for manufacturing equipment or industrial machinery where direct sensor installation is impossible.

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There are currently 730,000 bridges in Japan. Although in 2018, 25 % of the road bridges had more than 50 years old, an indicator of the aging of the transport infrastructure, this indicator will reach 63% in 2033. Furthermore, human resources, such as professional workers and engineers working on the inspection and maintenance, are gradually decreasing. Therefore, adequate inspection and maintenance are not always conducted; this is considered a serious social problem in Japan. To solve this problem, we have previously proposed a self-powered vibration-based structural health monitoring system. The core part of the system is the novel vibration-sensing device discussed, which implements a giant magnetostrictive material. In this paper, the structure of the sensor is investigated to solve the problems of the prototype device before. The bridge girder on which the sensor is installed is tilted by several degrees, so the pressure does not act evenly on the material. Therefore, we propose a structure using concave-convex spherical contact. The experimental results showed that the bending stress could be reduced and the generated power could be improved compared to the previous prototype sensor.

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In this paper, we propose a direction estimation method using a self-rotating inertial sensor and a distance estimation method using an LSTM-based deep learning model to improve the positioning accuracy of two-dimensional moving objects such as AGVs. By rotating the inertial sensor, the direction is accurately calculated by correcting the sensor offset. With this method, it was confirmed that the direction error was reduced from 52° to 5° in 30 minutes of evaluation in a stationary state. In addition, we developed a distance estimation method using a deep learning model based on the information from the inertial sensor. We evaluated it using an AGV on a rectangular path and confirmed that it can estimate the moving distance with an accuracy of 0.7%.

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Analysis methods for non-Gaussian stochastic processes are required for various applications in the areas of reliability analysis, signal processing, system identification, and so on. On the other hand, most of the conventional studies in terms of non-Gaussian random excitation have been limited to theoretical vibration response analysis, and non-Gaussian vibration experiments using actual systems are still in the developing stage. In this paper, a non-Gaussian random exciter using a waveform generation method based on the Cai and Lin model is developed, and the probability density shapes and power spectrum of the waveforms output from the actual shaker were evaluated. From the evaluation results, it was found that the developed experimental setup can output waveforms that accurately follow the given probability density function and power spectrum, confirming the basic operation as a non-Gaussian random exciter.

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Infrastructure built in 1950s has exceeded its service life and has become a social problem because of its deterioration. To solve the problem, it is important to expand inspection technology and to make efficient repair plans for infrastructure such as water pipes and bridges. Especially research and development of inspection technology related to digitization using ICT is expected. In a previous study, we conducted experiments using a IoT sensor to determine the mean square value of acceleration for rings which have different pipe thicknesses and observed that the mean square value of acceleration changes as the pipe thickness changed. Thus, we will attempt to expand the area of application of the inspection method using the mean square value of acceleration for structural components such as beams and others. In this study, we conducted a formulation of the mean square value of acceleration for several structural components and discussed its physical importance in terms of sensing. As a result, it was confirmed that the spatial distribution of the mean square value of acceleration for simple support beams converges to a certain shape as the upper frequency limit increases. It was also confirmed that the magnitude of the absolute value of mean square value of acceleration is greatly affected by the maximum mode order.

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The 1495 PowerPoint lecture materials on processing technology, AI, and IoT in the subject of "Production System Design" were classified into diagrams, images, and videos, and the maximum evaluation rates in the questionnaire were 17%, 46%, and 48%, respectively. Videos and images were effective as lecture materials. The student IoT usage rate was 70%.90% of students were positive about using AI after employment, indicating that AI will play a supporting role in the future. We predicted that students' presentation themes regarding the use of IoT would be categorized into manufacturing (34%), agriculture, forestry and fisheries (25%), and lifestyle/environment (31%).Social issues and personal interests played a large role in theme selection. As a result, students' interest in IoT technology has greatly expanded from mechanical engineering to other fields.

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This is an activity report in 2023 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, this academy is based on the combination of lecture series for collecting new technology information and exercise series of basic technology mainly concerning on IoT for manufacturing as well as presentations and discussions among participants. In this report, I briefly introduce its activity in 2023 with some considerations on gained knowledge through running this kind of collaborative works.

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Situations that surround the manufacturing industry have been changing drastically in recent years due to societal issues. In the Industry 5.0 vision, Sustainability, Resilience and Human-Centric are key perspectives for the manufacturing systems. Additive Manufacturing (AM) has a potential to enable waste reduction and flexible operations and is expected to contribute to the next-generation manufacturing industry that Industry 5.0 is aiming for. In this study, the enterprise strategy in the manufacturing industry driven by societal issues is investigated and we propose a manufacturing system with AM and data sharing platform combination to address those challenges.

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