The project name is Implementation of Overall Equipment Effectiveness (OEE) in Textile Manufacturing Company. This project acts as a tool for identifying the level of efficiency of the equipment used. This project aims to implement and identify the machine's level of efficiency by using the OEE technique, identify the issues causing the 6 big losses by applying FMEA (Failure Mode and Effect Analysis), and provide recommendations for improvements to increase the efficiency of the machine. This research was carried out on the MCB machine in the department of packaging at a textile manufacturing firm based in Melaka, Malaysia. This MCB machine is used to pack eyes and hooks of the merchandise. The OEE implementation at MCB machine achieved 76.01%, which is less than the OEE standard, 85%. The study was further continued by assessing the 6 big losses using the machine. From the 6 big losses assessment, it is determined that the highest loss is the breakdown loss at 54.17%, and the next highest loss is the speed loss at 29.24%. Both these losses cause low OEE quantities on MCB machines. Thereafter, the Fishbone diagram is employed to find the basic cause of the losses. The reason of the huge loss is because of the 4 parameters: man, method, material, and machine. To increase the efficiency of the machine and decrease the amount of the 6 big losses on MCB, the FMEA is carried out to identify the most significant cause. FMEA is a method that determines potential causes of the product failures. The recommended enhancement to decrease losses is to train the operator to reduce breakdown time. Another recommendation is to perform regular maintenance to keep the machine in excellent condition and prevent breakdown.

In make-to-order production systems, machining tools and processes are not assigned when the orders are received. Therefore, it is difficult to make an accurate production schedule. This study proposes a method to estimate the probability distribution of the make-span from the probability density function of the processing time in the job shop scheduling problem that includes tasks whose processing time is uncertain. Furthermore, based on the expected value and standard deviation of the make-span of the created production schedule, we propose a method for deriving a production schedule that is oriented toward minimizing the value of the make-span and its uncertainty. We also carry out computational experiments to evaluate the effectiveness of the proposed method.

Recently, almost all the quality inspection work in the manufacturing industry has become automated. However, there are many products for which inspection cannot be automated. For example, all parts are slightly different because the tip of a dental treatment rotating tool (Diamond bar) is attached to diamond particles. In addition, judgments of the inspection are different by the operator. Our previous studies challenged the development of the inspection support tool. However, the tool's precision did not improve even if the various parameter adjustments were performed. In this study, we focused on the dependability of the sample data. Moreover, the distinction models are structured and inspect the performance. As a result, the new model using corrected samples was a higher performance than the using all samples.

We address the research objectives and the outline of KAKENHI KIBAN (A) project entitled ``Development of Fundamental Models for Enterprise Collaborations in Smart Supply Chains for Collaboration and Optimization". The studies in our project is introduced. The outline of our research outcomes and future direction will be discussed.

Integration of several machining processes in response to a wide variety of needs in machinery industries has expedited the implementation of robotized flexible manufacturing systems even in the SME manufacturers for the last decade. This study extends our previous work on building a tool allocation problem in parallel-type FMSs and proposes a simultaneous optimization model for cutting tool allocation and job sequencing. We show that the proposed decision-making approach can outperform the conventional two-phase approach in minimizing total completion times through a series of computational experiments.

In the labor-intensive cell production system, it is important to train operators effectively because their skills are essential for productivity. Our previous study proposed a method to classify the skills named "skill index" based on the time required for each task and allocated operators based on this method. However, in actual workplaces, it is assumed that workers accumulate fatigue due to the repetition of work, which affects the operation time. In this study, we propose an operator’s allocation method that considers the effect of fatigue and verify its effectiveness compared with the results of the previous study by computer experiments. In addition, an assembly experiment using a toy is conducted using the worker allocation method obtained by the computer experiments.

In order to solve the labor shortage in manufacturing sites, full automation of machining, including NC program generation and other setup work, is required. In this study, an automatic cutting condition determination system was developed to achieve fully automated NC program generation. The cutting conditions that served as the teacher data were the conditions that calculated to make the machining time minimize through repeated simulations, and the relationship between the cutting conditions and the geometric features of the product shape (e.g., cross-sectional area and curvature of the curve) was machine learned to create a learner. Automatic determination of cutting conditions based on geometric features for an unknown product shape was performed, and its usefulness was confirmed by the rate of correct answers.

A dynamic shape prediction method for knitted fabrics is proposed. To improve the productivity of knitted fabrics, it is required to predict the shape of finished knitted fabrics and determine the kind of yarns and knitting parameters without actual knitting. The shape of knitted fabrics reflects mechanical properties of yarns composing them. In addition, interaction between yarns such as friction influences that shape. Therefore, in this study, we propose a method to predict the dynamic shape of knitted fabrics by considering stretching, bending, twisting deformation and friction. First, a knitted stitch is modeled based on the differential geometry at yarn level. Next, we use it to formulate the equations of motion. Furthermore, we discuss the interaction between the yarns. Finally, a numerical example using the proposed method is presented.

Using the design of cold press work as a case study, this research investigated a system that can determine the countermeasure corresponding to the difference data using machine learning by mapping the difference between the analysis data and the countermeasure taken for the difference data in multiple structural analysis data generated in the development cycle. The authors examined two types of classification: multi-label classification and multi-class classification using distance learning, and confirmed the prediction accuracy of each and the limitations of the system. In addition, based on the above results, we examined the type of analysis data that should be used for learning, and confirmed that the accuracy is better when the type of input data is narrowed down.

本研究の金型製作方法は，従来の除去加工による金型製作に比べ，製作期間が短くなり，金型の設計モデルがあれば，最短で1日で金型が完成した．短納期でプラスチック成形を行う技術として，紫外線硬化樹脂製AMでプラスチック射出成形用樹脂型を製作する技術も報告されているが，樹脂型では不可能であった成形温度が高い成形材料の射出成形も可能になる．

紫外線硬化樹脂製AMで造形した雄型（以降、反転型）と粉末冶金技術を用いたプラスチック射出成形用金型製造技術を開発した．金型の製作に必要な基礎データの取得を目的とし，粉体材料をFe100％として実験を行った．反転型の形状を設計する際に重要な圧縮応力とひずみの直線関係を「密封状態におけるAM造形物の圧縮試験」で明らかにした．「焼結温度の評価」から，この金型製作技術に必要な焼結温度が973K（700℃）以上であることが分かった．「焼結前粉体圧縮圧力の評価」を行い，粉体圧縮成形時に必要な圧力が250 MPa（約2.5 t/cm²）で金型の製作が可能であることが分かった．

Kinematic calibration of industrial manipulators is required for accurate work. Moreover, it is necessary to measure the environment around the manipulator for motion planning. However, it takes long time for kinematic calibration and measurement of the environment around the manipulator. We study SLAM-integrated kinematic calibration, which can simultaneously calibrate the kinematic parameters of an industrial manipulator and create a map of the environment around the manipulator. This method can perform efficient kinematic calibration and measurement of the environment around the manipulator. However, when errors occur in the kinematic parameters due to changes over time or accidents, it is necessary to stop the work and calibrate. In this paper, We eliminate the need to stop the work for calibration by performing SLAM-integrated kinematic calibration online during operation. The effectiveness was confirmed by verification in a virtual environment. Verification in the real environment revealed that there is a room for accuracy improvement.

We focus on the method to generate the order of parts to assembly products considering the work difficulty in the parts assembly process. The quantitative evaluation value of work difficulty is created by using motion analysis ‘MTM’ for the parts assembly process and the mathematical model is constructed to generate the order of parts to assembly. In this study, we explain the characteristics of the quantitative evaluation value of the work difficulty and the mathematical model developed to generate the order to minimize the total work difficulty. Numerical experience is performed to evaluate the performance of the mathematical model, and the results obtained by the mathematical model are investigated by comparing the resultant operation time obtained from actual parts assembly work.