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Welcome to the 2020 11th International Conference on Mechatronics and Manufacturing (ICMM 2020), which was held at Tama Campus, Chuo University, Tokyo, Japan during January 10-12, 2020.

The ICMM 2020 had provided a forum for scientific and technological advances in the theory and practice of mechatronics and manufacturing. In recent years, the development of sensors and actuators promoted many new and challenging research fields of mechatronics, such and robotics, MEMS, IT and so on. Furthermore, new research trends are developing in the field of manufacturing, such as ultra-precision machining, additive manufacturing, multi-material systems and so on. Consequently, holding a highly-professional meeting, in which many academic researchers and industrial engineers could exchange their knowledge and experiences, is getting more and more important.

ICMM is growing with the numbers of participants and increasing in its quality. The latest ICMM 2020 aimed to be one of the leading international conferences in the world, and it provided an exciting environment for researchers and engineers to present and discuss the latest technologies and applications in the field of mechatronics and manufacturing.

In the present conference, a number of academic and industrial works have been submitted and selected after a peer review process by experts among the technical committee members and other international reviewers in the related fields of expertise. We hope all the attendants had experienced a remarkable opportunity for the academic and industrial communities to address new challenges and share solutions, and discuss future research directions.

Finally, we thank all participants who had come to Tama and joined the conference.

Conference Chair of ICMM 2020

Yoshihiko UEMATSU

Gifu University, JAPAN

List of Advisory Committee, Conference Chairs, Program Chairs, Technical Committee are available in this pdf.

List of First name, Surname, Institution, E-mail address are available in this pdf.

All papers published in this volume of IOP Conference Series: Materials Science and Engineering have been peer reviewed through processes administered by the proceedings Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

The aim of this research is to use a full factorial design to determine the significant factors of friction stir welding (FSW) process of dissimilar AA6061-T6 and AA7075-T651 aluminum alloys, on the surface residual stress and microhardness. There are a total of three factors studied: rotation speed, welding speed and workpiece layout. The results showed that the factors that have a statistically significant effect on residual stress occurring are welding speed, workpiece layout and interaction between rotation speed and welding speed. The appropriate parameters for FSW process of dissimilar aluminum alloys are rotation speed of 1400 rpm, welding speed of 50 mm/min and the layout of the workpiece by advancing side (AS) uses AA6061-T6 sheet, while retreating side (RS) uses AA7075-T651 sheet, which will result in the surface residual stress of −34.33 MPa. This compressive residual stress will be beneficial to the welded joint for retard the occurrence of cracks caused by fatigue. The hardness of all workpieces have similar tendency, thermo-mechanically affected zone (TMAZ) area in both AS and RS gave the least hardness, which found that the AA6061-T6 and AA7075-T651 sheets had a hardness of approximately 55HV and 110HV respectively.

In this study, the deformation behavior of two phases in a model dual phase system of copper and a martensitic stainless steel was investigated using in situ synchrotron X-ray diffraction. Due to the different crystallographic structures of copper and martensite, their diffraction patterns are well separated such that the strain distribution and the load partitioning between the phases could be investigated under loading using standard methods. The copper matrix started to yield at low stress levels after a short stage of elastic deformation and the different lattice strain of the copper and martensite indicated that load partitioning took place after yielding of the copper.

In the present-day manufacturing industry, achieving machining accuracy, high production rate, high material removal rate and increasing tool cutting life is becoming more vital and a particularly difficult task. Machining of hard material imposes a lot of challenges, mainly high temperature generation between tool-workpiece interface. Enormous heat generation adversely affects surface roughness, tool life, dimensional quality of the workpiece and deteriorates the tool life significantly. The use of proper lubrication in machining processes is critically important to modify the conditions of contact area by effective control over frictional interaction which should ensure change in tool-chip interface and machining mechanics. In the present work an experimental investigation was carried out on various MQSL parameters on surface roughness and tool temperature in turning EN 31 steel and the results are compared with dry turning. In order to apply lubricants at shearing interface (chip flow at the rake face of the tool) high velocity MQSL set-up is fabricated. The present work has achieved significant results and helped in concluding the influence of lubricant parameters of MQSL in turning process.

The surface modification of Ti-6Al-4V substrate was carried out by plasma spraying method (WC-17%Co). The surface morphology and composition of the coating were analyzed. Then, under the air environment, the surface of the plasma sprayed WC was carried out. Friction test of three pairs of friction materials filled with 15% glass fiber + 5% graphite, PTFE filled with 25% glass fiber, and PTFE filled with 60% tin bronze. The treated material improves the friction characteristics of the substrate. However, due to the large thickness and hardness of plasma spraying, it is difficult to use sandpaper to reduce the surface roughness, which will affect the dimensional accuracy of the parts.

The heat and mass transfer characteristics of the hydrogen membrane humidification system of a 70kW atmospheric pressure proton exchange membrane fuel cell stack were experimentally studied. The shell-and-tube membrane humidifier is applied to the high-power fuel cell stack hydrogen humidification system, which has the advantages of fast humidification rate and good wettability of humidified hydrogen. The liquid hydrogen is humidified by liquid water. After the humidification in the experiment, the hydrogen can always reach the supersaturation state. When the hydrogen flow rate is constant, the temperature difference between the membrane humidifier humidification water inlet and outlet decreases with the increase of the water flow rate, and the hydrogen outlet The temperature approaches the humidification water inlet temperature as the liquid water flow rate increases; increasing the humidification water flow rate can reduce the temperature difference before and after the humidification water passes through the membrane humidifier. When the temperature and flow rate of the humidified water are constant, the fuel cell stack load increases, and the relative humidity of the humidified hydrogen outlet does not change significantly.

The rising demands of more reliable and stable electrical systems attach importance to accurate Remaining Useful Life (RUL) prediction of the lithium-ion batteries. As artificial intelligence and machine learning techniques advance, data-driven methods especially deep learning algorithms have become the rising star in RUL prediction. Recurrent Neural Networks (RNNs) and their variants such as Long Short Term Memory have proven effectiveness in various sequential tasks. However, due to its iterative nature along the time axis, RNNs take much time for information to flow through the network for prediction. Inspired by recent advance brought by Transformer in sequence transduction tasks, we proposed the attention mechanism based Convolutional Neural Network (CNN) with positional encoding to tackle this problem. The attention mechanism enables the network to focus on specific parts of sequences and positional encoding injects position information while utilizing the parallelization merits of CNN on GPUs. Empirical experiments show that the proposed approach is both time effective and accurate in battery RUL prediction.

This paper deals with selected contact type stylus method and non-contact type machine vision method using laser speckle for components prepared by grinding of AISI 1040 steel with a variety of wheels and varied depth of cut. In this interactive study, Optical method based on statistical properties of binary images is proposed for machined surfaces. Grounded metal surfaces are used to develop a binary digitized speckle pattern by a beam of He-Ne laser light source. High end camera is used to capture the image of a speckle pattern. White to black pixels ratios is computed from the binary images using image processing toolbox in Matlab. The correlation is developed between white to black pixels ratio and measured two-dimensional surface roughness parameter. Two-dimensional surface roughness parameters are recorded using a contact-type surface profilometer. The results which opted, clearly supports that these parameters have a relationship with a degree of surface roughness. A linear relationship is observed between parameter obtained from proposed technique and measured value of surface roughness using surface profilometer. The statistical analysis represents the performance of maximum relative error in prediction of surface roughness is 9%.

During the last decade, agricultural sector has witnessed a wide adoption of cutting edge technology not only to improve the quality and quantity of the crops but also to ease the labour burden. These technological oriented and innovative agricultural solutions have opened new horizons for opportunists, researchers and investors along with the creation and development of new markets. Robotic and automation technologies are now complementing farmers in various ways. In this paper, we present the mechanical and electrical design of multi-purpose agricultural robot which has the capabilities to move in three Cartesian coordinate axis along with the functionality of harvesting small sized fruits with the help of grippers. Our developed robot prototype can be controlled via remote control. Prototype testing results show that the developed robot can reach to the target by passing any small sized obstacle and harvest the fruit. We believe that such low cost, less complex and indigenously developed multipurpose agricultural robots can help farmers by reducing their labour cost and human effort.

The dynamics of rotating machinery have been extensively investigated during the past century. To date, longer rotating shafts, higher rotating velocity, higher fluid pressure, and generally high performances are required. For this purpose, a deep understanding and a more accurate modelling of the rotating machinery dynamical behaviour is needed, especially in terms of the resultant vibrations and stability issues. In this paper, a mathematical model was developed for a simple rigid rotor-bearing system using finite element method. Several natural frequencies and corresponding mode shapes of the systems were presented. In order to validate the model, some experimental tests were performed using a small test rig. The response of the system in the frequency domain and the direct orbit of the shaft were plotted as well.

In this paper, we conducted RGB incident light intensity of the high power white lighting LED. The current control 50% duty cycle pulse signal source with appropriate semiconductor carrier frequency response are supply to the RGB white lighting LED. Although, the carrier relaxation time of the red blue and green light in the white lighting LED are not the same value which depend on the carrier different of the energy level. The measurement show that the lowest duty cycle appropriate pulse signal for only blue or UV base white LED give more relative light intensity than normal (low frequency) signal within 11%.

In this article, development of glider control system for movement in a three-dimensional based on position-trajectory control laws is considered. To ensure control, a model of spatial motion of the glider is used, taking into account the component of the variable buoyancy. As a result of the simulation, the trajectories of the glider were obtained at points set in the horizontal plane, considering the parameters of the trajectory for movement in the vertical plane. The results obtained make it possible to evaluate the adequacy of the developed control system. The deviation of the system when the glider reaches the set point is minimal, and using the trajectory parameters in the vertical plane, it is possible to control the depth of immersion and speed. The presented results of the simulation of coating elements prove the possibility of practical use of the control system when planning glider-type AUV missions.

This study classified the defects that were encountered on single-bead welding, which was made by MIG/MAG welding machines in Hürsan Press company. 2250 images were taken on weldings that were made by five different welders. This study classified defects into three classes as good welding, porosity, and discontinuities. The images in the dataset, which have three classes, were classified by two stages. In the first stage, the texture features of the welding area were extracted. In the second stage, Artificial Neural Networks (ANN) method classified the extracted features. Sensitivity, specificity, accuracy, precision, and F-score metrics were used to measure the classification performance. 1500 images were used to train the system and training and validation performances were obtained as 94.03% and 94.19%, respectively. 750 images were used to test the performance of the proposed method and the test performance was obtained as 94.31%. The proposed method detected a defect on single-bead welding in 0.98 seconds.

This paper presents a non-destructive fertility detection of multiple chicken eggs in incubation industry based on image processing technique and convolutional neural network (CNN). The aim of this research were to design and implement the system for simultaneously distinguish multi eggs infertility from fertility one. LED light source setting up for illumining the 48 eggs, consisting of both egg types and randomly placed on a tray in dark box. In addition, a pre-trained CNN is performed to classify fertile and infertile eggs. All eggs is captured and processed to extract a region of interest (ROI) for each egg, generating large ROI egg images dataset which used to train and test the CNN model. A designed system is programmed using Python, operating on Windown7-64bit supported by OpenCV and Keras. Experimental results showed the accuracies for fertile incubated eggs detection between day 7 and day 9 reaches 100%. Meanwhile, eggs location has 100% of accuracy is also observed. Hence, the proposed technique are high reliability, high accuracy system and suitable to use in real application.

This study aims to solve a multi-objective tourist trip design problem (MO-TTDP) in order to simultaneously maximize tourist satisfaction and minimize total traveling cost. The goal is to identify optimal routing for tourist under time window and time horizon constraints. The mathematical model is first presented to find optimal routing under limited budget. However, the exact algorithm cannot find solution with multiple objectives simultaneously. Therefore, this study applies a metaheuristic, called Global Local and Near-Neighbour Particle Swarm Optimization (GLNPSO), to solve the MO-TTDP. Several local search strategies are also proposed to enhance the solution quality. The proposed algorithm is implemented on the real-case study in Chiang Mai City, Thailand. The experimental results show that the proposed algorithm yields a set of diverse and high quality non-dominated solutions.

This study aims to present the procedure developed for detecting deterioration of oil lubricant under variation of electromagnetic field which was generated by a ferrite-core solenoid coil. The overall diameter of coil was 18.5 mm and the inductance was 22.421 mH from direct current stimulation. The results were then compared to those from frequency stimulation. The optimal efficiency of measurement systems was then assessed by real-time analyzing the metal particle contamination of 3 oil lubricants categorized by grade namely ISO 32, ISO 46 and ISO 68 as well as the viscosity under specific conditions which were flow rate and temperature ranging from 25 °C to 100 °C. The analysis was performed to compare the results from experiments of unused and used (approximately 6 months) oil lubricants. From experiments, the viscosity of oil lubricant was reduced when the temperature exceeded 50 °C. However, the stimulation of coil using 0.9 A direct current could clearly distinguish the difference between the unused and used oil lubricants with metal particle contamination comparing to other values of direct currents applied with 0.42 % of error. Also, the 0.5 kHz of frequency would be the most appropriate value for frequency stimulation with 0.38 % of error.

This paper proposes a novel microfluidic system for a lab on a chip device which includes three major systems: a microchannel, micromixer and a droplet generator. The novel system proposes electroosmotic fluid flow control combining droplet generation and immunocapturing based CTC separation. ANSYS Fluent is used to optimize the fluid flow parameters, droplet size and justify the mixing capability of the micromixer. COMSOL Multiphysics simulations justify the integration of the electroosmotic fluid flow control for precise generation of droplets and optimization of dimensional parameters of microchannels, followed by a fabrication method for the microfluidic system.