In industrialised countries, the competitiveness of the products/services of the manufacturing industries in the global market is declining due to many factors, including commoditisation, rapid technological growth in developing countries and a lack of creativity within manufacturing companies. For this reason, designers in industrialised countries are required to create more innovative products/services whose novelty does not depend on their own original technology alone but on flexible resource integration among diverse actors. In order to design innovative products/services, empathy must be developed among diverse actors around the premise of the design. However, the ways to understand rationalities of each other is still unclear in extant research. As a result, diverse actors with heterogeneous rationalities fail to empathise to others, and designing innovative products/services as intended is difficult. To address this issue, this study aims to develop a practical method that achieve to understand the rationality of other actor in design innovative products/services. To achieve this, a context analysis method is proposed that captures the other actor’s context in formalised procedure. The proposed method includes four steps: conducting an interview to extract information of context, modelling the context, understanding the context and carrying out the depth interview to understand a target actor’s rationality completely based on the context modelling result. The proposed method was applied to therapy robot development, which is in its early stage of innovative service design. Moreover, the application result can encourage an actor to understand the other actor’s rationality by utilising the proposed method.

Dies and molds are necessary elements in the manufacturing of current industrial products. There is increasing pressure to machine high quality complicated surfaces at low cost. The standardization of process planning is said to be a key to improving the efficiency of machining operations in practice. Thus, computer aided process planning (CAPP) systems are urgently needed to reduce the time and effort of preparing machining operations. However, it is difficult to generalize process planning that continues to depend on skillful experts and requires long preparation time for die and mold machining. On the other hand, to overcome issues that are difficult to generalize, it is well known that machine learning has the capability to estimate valid values according to past case data. Therefore, this study aims to develop a CAPP system that can determine machining process information for complicated surfaces of die and mold based on pattern recognition and deep learning, a kind of machine learning. A network architecture called 3D u-net is adapted to effectively analyze whole images by producing segmented regions. Using a voxel model representing targeted shape, it becomes easier to deal with the complicated surfaces of die and mold generally and three-dimensionally, as skilled experts pay attention to whole geometrical features. Cutting tool type and tool path pattern are treated as machining process information determined in a CAPP system. The results of case studies confirm that the developed CAPP system is effective in determining the machining process information even for complicated surfaces according to the implicit machining know-how.

This paper proposes a classification method using Taguchi’s T-method. Collecting sufficient number of training samples at the initial stage of production and at the beginning of the development process is difficult. Because of the small sample size, it is difficult to apply conventional classification methods. In this paper, we propose a classification method that is based on the Taguchi’s T-method for calculations with small sample sizes. The proposed method involves construction of a classification model that quantifies the Taguchi’s T-method and can be applied to small sample sizes. We confirm that the classification accuracy of the proposed method is superior to that of the Mahalanobis–Taguchi method. Through a verification experiment, we examine the change in the classification accuracy when the number of training samples is increased. It is confirmed that the conventional method outperforms the proposed method when the number of training samples is more than thrice the number of parameters.

The practice of 5S principles in manufacturing small and medium enterprises (SMEs) has been widely discussed in scientific studies related to the critical success factors (CSFs) and challenges for improving organizational performance. The importance of performance measurement for sustaining 5S becomes a strategic value in the improvement process and elimination of waste. In previous studies, the performance measurement of 5S practices is commonly viewed as an auditing process continuously. However, the studies of performance measurement and management (PMM) for the sustainability of 5S practices as a control system are still limited. Also, the performance measurement model needs to be designed for measuring the potential barriers of 5S sustainability. Therefore, this study has a motivation to develop a conceptual model in designing PMM for the sustainability of 5S practices that are carried out both empirically and philosophically to enhance quality and productivity. So, the objective of this paper is to conduct the literature reviews on how to propose a model of PMM for 5S sustainability in manufacturing SMEs based on CSFs and the integrated theoretical framework. The review methodology provides a systematic review step that encompasses collecting the database, qualitative analysis of the database, synthesizing the relevant theory, and proposing a conceptual model. This paper found the critical point for developing the PMM model of 5S sustainability including the step of sustainability building, dimensional control system, and sustainability indicators. For further research, it needs an analyzing study to assess the relationship among the factors that contribute to the performance assessment of 5S practices.

In the early stage of vehicle development, it is required to implement a target cascading study by solving inverse problems. However, simulation costs of vehicle dynamics to predict transient responses and frequency responses make the target cascading study difficult. The purpose of this paper is to propose a method to construct a surrogate model which can predict waveform responses and a solution of Bayesian optimization using posterior distribution of trained waveform responses. Replacement of the expensive simulation by the more economical surrogate model can enhance the target cascading study. In this paper, we construct a vectorized training data matrix from the waveform responses which can be evaluated from CAE simulations based on the Design of Experiments. In this proposed method, supervised and unsupervised learning are introduced. The singular value decomposition is used as a feature extraction method (Unsupervised learning) and applied to the training data. Obtained singular vectors are used as feature modes to represent the training data. Gaussian Process model is introduced as a regression model (Supervised learning) and applied to each weight of feature modes which can be obtained by projection of training data to feature modes. The waveform response can be predicted by the superposition of prediction feature values and feature modes. By using the posterior distribution of trained Gaussian Process, Expected Improvement function is evaluated and used in Bayesian optimization to minimize a cost function which is evaluated from the posterior mean of a predicted waveform. The feasibility of the proposed method is illustrated by an application for the suspension design problem of impact harshness phenomenon.

Recently, the demand of complex shape parts has increased mainly in the aircraft and medical industries. In these parts machining, the displacement and vibration of workpiece that strongly affect the machining efficiency are induced as a result of the heavy change of the unmachined workpiece shape and stiffness while rough machining. On the other hand, it is difficult to automatically determine the manufacturing information by using a commercial CAM system because there is a large number of combinations. Thus, in order to improve the machining efficiency of complex shape parts, the authors have proposed a determination method of workpiece transition shapes during a rough machining operation based on topology optimization. However, tool paths are not generated automatically to create the obtained workpiece transition shapes in the previous study. Therefore, in this study, a tool path generation method is proposed considering both static stiffness of workpiece and machining efficiency. The proposed method supposes 5-face milling and in the machining the tool orientation is determined to minimize the change of tool orientations by using the obtained parameters in topology optimization. The tool paths are continuously generated based on not only the design variables of topology optimization but also geometric feature of machining area, target shape, final support. A case study assuming rough machining of complex shape parts is conducted to confirm the effectiveness of the proposed method.

Recently, with the expansion of the Japanese manufacturing industry and increase in local production overseas, recruiting and retaining local employees responsible for local production is a matter of concern because overseas factories have been exhibiting higher turnover rates than those in Japan. Productivity decreases in a manufacturing process when the process is taken over by a substitute due to a decreased worker turnover, and the work time of the process keeps increasing until the work proficiency of the substitute reaches the same level as that of the departed worker. Hence, advance production planning and product inventory management by considering the decrease in productivity due to worker turnover are essential in the manufacturing process. However, there is a paucity of studies on methods to pre-evaluate the effect of worker turnover on productivity. This study firstly clarified the effect of departers, substitutes replacing the departers, and worker substitution on productivity. Subsequently, it proposed and implemented the modeling and simulation of a production system for evaluating the effect of worker turnover on productivity. The effectiveness of the proposed simulation was verified through case studies.

Recently, typing skills especially touch typing is given much importance. However, the typing skill of entrant students tends to be poor, because of the use of smart phone. Although some students try to improve their typing skill using typing software, pre-existing one similarly ignores which fingers are used, and instead emphasizes hitting the correct keys as fast as possible. The goal of this study is to develop a learning support system for touch typing based on fingering detected when typing, and to evaluate the possibility of teaching touch typing to non-touch typists through practicing with this system. In this study, we propose a method to determine which fingers are used to press which keys by using video of fingering made when typing and data of the pressed keys. Furthermore, we conduct evaluation experiments with the system using this method and evaluate the effectiveness of the system. According to the experiment results, estimation accuracy tended to decrease as CPM values increase. We learned that there were cases in which incorrect estimations were made, and we classified the causes into two categories: errors caused by occlusion and errors due to the image sampling being unable to keep up with the fingering. It is evident from the experimental results and discussion that when this system is used for non-touch typists to learn touch typing, it gives correct estimation results and is effective. For further research, we suggest using this system to facilitate the learning of touch typing for non-touch typists. More specifically, learners should enter a sequence of letters shown on-screen. Messages should be displayed on-screen when an incorrect key is pressed or when the recommended finger is not used. We believe that touch typing skills can be easily acquired by making users aware of what fingers they use to press the keys.

The concept of universal design (UD) has received significant attention from researchers, but it remains difficult for UD designers to reflect the diversity of the users in the product. Previously, we proposed a set-based UD method that considered the diversity of a user’s physical characteristics; however, this method did not take into account the requirements of a diverse set of users. Therefore, the present study proposes a new UD concept that quantitatively incorporates the diverse requirements of various users. Exploiting the concept of an evaluation grid and a set-based design method, this paper explains the procedure of our UD method from the determination of user requirements and design variables to the derivation of design solutions satisfying all users. We choose the problem of designing a mechanical pencil as a case study for our proposed method. Pair-comparison tests of mechanical pencils are conducted, and an evaluation structure diagram for a mechanical pencil is constructed on the basis of the results of this experiment. The importance of the design variables is calculated quantitatively based on the evaluation structure diagram, and the key design variables are determined. After the classification of users, we derive the design-ranged solutions for each group of users and the common design-ranged solutions for all users. Our proposed method successfully derives five types of mechanical pencils that together satisfy all users belonging to various user groups.

A user operating a mechanical devise receives sensory information (e.g., sounds and vibrations) generated by the mechanical operation in response to the operation input. With the digitization of products, the mechanical operation response is removed as is the associated sensory information, thus there is no confirmation of the completed operation. Adding a digital sound or vibration could solve this problem. The challenge, however, is designing an operation response without inducing any sense of discrepancy. The purpose of this study was to create a quantitative, objective method to evaluate the sense of discrepancy to the operation response and the habituation to the response. We investigated the possibility of using an event-related potential component (P300), believed to reflect attention to stimuli, as a quantitative evaluation method of the sense of discrepancy. Experiment 1: We studied the sense of discrepancy between the type of operation response (sound, vibration, none), and the shutter operation of a mirrorless single-lens camera, using subjective evaluation and the P300. We confirmed that the P300 amplitude corresponded to the degree of the sense of discrepancy. Experiment 2: With the mouse click being the operation sound, the attenuation of the P300 amplitude by the repeated operation was consistent with the subjective habituation to the response. We conclude, based on these results, that the P300 amplitude could help determine the sense of discrepancy to the operation response and the habituation to the response.