Papers - Aoyama, Hideki

Predictive shrinkage error model for selective laser sintering hybrid milling

Nguyen T.N., Aoyama H., Raie I.A.

Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering (Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering)  Vol. 86 ( No. 9 ) 687 - 701 2020.09

ISSN  09120289

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Selective laser sintering hybrid milling (SHM) is an advanced manufacturing technology that combines the flexibility of additive manufacturing with the precision of traditional machining. In SHM, instead of machining the products after they are completely formed using selective laser sintering (SLS), the milling process is conducted alternatively with the SLS. This allows direct fabrication of extremely complicated and high-precision products on the same workstation. However, shrinkage owing to SLS is a fundamental problem of this technology, because it creates significant dimensional errors in the final product. In this study, a method is proposed to calculate shrinkage error in SHM based on equations built from the data collected using F E A . Consequently, the calculation result is used to compensate the SHM shrinkage error. The experimental results of this study confirm that the error prediction model and the proposed compensation technique work efficiently in reducing SHM error.

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Evaluation of turned and milled surfaces roughness using convolutional neural network

Rifai A.P., Aoyama H., Tho N.H., Md Dawal S.Z., Masruroh N.A.

Measurement: Journal of the International Measurement Confederation (Measurement: Journal of the International Measurement Confederation)  161 2020.09

ISSN  02632241

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© 2020 Elsevier Ltd Existing computer vision methods to measure surface roughness rely on feature extraction to quantify the surface morphology and build prediction models. However, the feature extraction is a complicated process requiring advanced image filtering and segmentation steps, resulting in long prediction time and complex setup. This study proposes the use of convolutional neural network to evaluate the surface roughness directly from the digital image of surface textures. This method avoids feature extraction since this step is integrated inside the network during the convolution process. Five loss functions for the prediction models are selected and analyzed based on their suitability and accuracy. The predicted values obtained are compared to the actual surface roughness values measured using a stylus-based profilometer. The performance of the proposed model is evaluated for the prediction of the surface roughness of typical machining operations, such as outside diameter turning, slot milling, and side milling, at various cutting conditions.

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Tool path generation method for high-quality machining of free-form surfaces

Takanashi Y., Aoyama H.

Proceedings of the ASME 2020 International Symposium on Flexible Automation ISFA2020  2020.07

Research paper (international conference proceedings)

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Machining data (NC program) is generated by a CAM system, which generates the tool path from the target shape as a plane approximation surface instead of a free-form surface. Owing to this plane approximation, machining accuracy is reduced.In this paper, we propose a method to process the shape with high accuracy by defining the areas where accuracy is not required as a plane approximation surface and defining the part where accuracy is required as free-form surfaces.

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High-precision forming process based on analysis of selective laser sintering hybrid milling

Nguyen T.N., Aoyama H., Araie I.

Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering (Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering)  Vol. 86 ( No. 7 ) 562 - 571 2020.07

ISSN  09120289

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Selective laser sintering hybrid milling (SHM) is an advanced manufacturing technology that combines the advantages of additive manufacturing and traditional machining. This technology is used to fabricate extremely complicated and high-precision products. The key factor of SHM is the milling process that is performed during the depositing process. However, the thermal shrinkage during the cooling process when depositing new layers on the ones that were machined is a fundamental problem because it creates deformation errors which significantly reduce product accuracy. In this study, the thermal deformation error in SHM was investigated using finite element analysis (FEA) and confirmed experimentally. The mechanism involved in the shrinkage in SHM was studied using three shrinkage rules. Moreover, a compensation technique was proposed with the aim of reducing the thermal deformation error. The results proved that compensation could significantly reduce the deformation error in SHM.

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Basic development of data sharing CNC system (Case study on high accuracy machining of characteristic lines)

Masako SUDO, Hideki AOYAMA

Journal of Advanced Mechanical Design, Systems, and Manufacturing Vol.14 ( No.1 ) 1 - 11 2020

Research paper (scientific journal), Accepted

Simulation method on three-dimensional shape generation process by femtosecond laser ablation

Kawakami H., Nabetani S., Ueda M., Ogawa Y., Yamazaki K., Aoyama H.

Procedia CIRP (Procedia CIRP)  95   921 - 926 2020

ISSN  22128271

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Ablation processing using a femtosecond laser has the advantage of processing materials that are impossible to machine by conventional methods. However, it is difficult to predict the three-dimensional removal shape owing to a femtosecond laser irradiation and the current processing method, repeating through trial and error, is inefficient and expensive. For this reason, for processing materials to desired shapes using a femtosecond laser, development of a simulation system and a CAM (Computer-Aided Manufacturing) system are indispensable. In this paper, a simulation method for processing materials using a femtosecond laser is proposed and conducted to nano-polycrystalline diamond (NPD). It is confirmed that the proposed method can precisely simulate the femtosecond laser processing for three-dimensional shape generation. Therefore, the proposed method enables to predict the removal shape identified as the virtual tool shape for calculating a tool path on a CAM system. In addition, an automation process method including an on-machine real-time measurement system of the proposed method is described.

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Rapid estimation of die and mold machining time without NC data by AI based on shape data

Takizawa H., Aoyama H., Won S.C.

2020 International Symposium on Flexible Automation, ISFA 2020 (2020 International Symposium on Flexible Automation, ISFA 2020)   2020

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Rapid estimation of machining time is necessary for flexible production scheduling and early responses regarding delivery date. It is also important for selecting the most suitable of a factory's many machine tools. Usually, machining time is estimated based on an NC program. However, this takes time to generate and its estimation accuracy is not ideal because it cannot consider the control characteristics of the machine tool. This study proposes a new method for rapidly estimating die and mold machining time without generating an NC program: inputting curvature and machining depth distributions into AI as color information.

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High efficiency molding by real-time control of distance between nozzle and melt pool in directed energy deposition process

Aizawa K., Ueda M., Shimada T., Aoyama H., Yamazaki K.

JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing, LEMP 2020 (JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing, LEMP 2020)   2020

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Laser metal deposition (LMD) is an additive manufacturing technique, whose performance can be influenced by a considerable number of factors and parameters. Typically, a powder is carried by an inert gas and sprayed by a nozzle, with a coaxial laser beam passing through the nozzle and overlapping the powder flow, thereby generating a molten material pool on a substrate. Monitoring the evolution of this process allows for a better comprehension and control of the process, thereby enhancing the deposition quality. As the metal additive manufacturing mechanism has not yet been elucidated, it is not clear how process parameters affect material properties, molding accuracy, and molding efficiency. When cladding is performed under uncertain conditions, a molded part with poor material properties and dimensional accuracy is created. In this paper, we propose a method for high efficiency molding by controlling the distance between the head nozzle and the molten pool in real time. The distance is identified by an originally developed sensor based on a triangulation method. According to the distance, the head nozzle is automatically controlled into the optimum position. As a result, an ideal molding process can be generated, so that high efficiency molding and high-quality material properties can be obtained. Experimental results show that continuing deposition at the optimum distance assists in achieving deposition efficiency and dimensional accuracy. According to the specific experimental results of this method, the modeling efficiency was increased by 27% compared to the method without correction, and the modeling was successful with an error within 1 mm.

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Basic study on development of integrated CMC system -productivity improvement by feedback mechanism of machining technology information-

Sudo M., Aoyama H.

Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering (Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering)  86 ( 10 ) 806 - 812 2020

ISSN  09120289

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In order to achieve high precision machining and high productivity, it is necessary to optimize machining data for a machine tool to be used, its control system, and shapes to be machined. Machining data are usually not optimized because it is generated by the CAM system as NC data based on general information. In the conventional process chain, even if the machining data is properly improved at the production site, there is no mechanism to reflect that in the previous process, so that the improvement information is not utilized. In this study, a shared database and a data model to share and inherit information in all processes from design to production is proposed in order to generate optimized machining data for the target machine tool and machining shape. The shared database and the data model enable non-expert operators to generate high-quality machining data like experts. In addition, it is also possible to reuse the information on the modified cutting conditions improved by experts at the machining site via a shared database. The effectiveness of the proposed shared database and data model is confirmed by experiments.

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Advanced linear encoder calibration system with sub-nanometer resolution

Taniguchi K., Tamiya H., Enomoto T., Aoyama H., Yamazaki K.

CIRP Annals (CIRP Annals)  69 ( 1 ) 437 - 440 2020

ISSN  00078506

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Calibration of linear encoders has conventionally been performed using an optical interferometer for reference. Calibration using the optical interferometer, however, cannot fulfill the accuracy and stability requirements when the linear encoders exhibit sub-nanometer resolution. This study describes a new calibration method developed using the picometer resolution laser holographic scale as a reference, employing a range-dependent and minimum error-variance algorithm. The method has been evaluated via comparison tests with conventional optical-interferometer-based methods. Moreover, it has also been verified to achieve higher resolution, accuracy, and higher stability during the measurements, even in the atmospheric environment.

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Automatic Generation System of Apparel Design Based on Tacit Preference of Customer

Yuki Inoue, Hideki Aoyama

Proceedings of The 8th International Conference of Asian Society for Precision Engineering and Nanotechnology (ASPEN 2019) USB   1 - 4 2019.11

Research paper (international conference proceedings), Accepted

Basic Study on Dressing Simulation Using Mechanical Cloth Properties Obtained by Simple Measurement

Yuna Akatsuka, Hideki Aoyama

Proceedings of The 8th International Conference of Asian Society for Precision Engineering and Nanotechnology (ASPEN 2019) USB   1 - 4 2019.11

Research paper (international conference proceedings), Joint Work, Accepted

Toolpath Generation Method for High Accuracy Machining Based on Acceleration/Deceleration Characteristics of Machine Tools

Than Trong Khanh DAT, Hideki AOYAMA AOYAMA

Proceedings of The 8th International Conference of Asian Society for Precision Engineering and Nanotechnology (ASPEN 2019) USB   1 - 4 2019.11

Research paper (international conference proceedings), Joint Work, Accepted

Development of a three-dimensional encoder for highly accurate positioning

Hideaki TAMIYA, Yoshiyuki MITERA, Kayoko TANIGUCHI, Hideki AOYAMA, Kazuo YAMAZAKI

精密工学会 Vol.85 ( No.10 ) 891 - 895 2019.10

Accepted

ナノ多結晶ダイヤモンドエンドミルの超硬合金加工性とフェムト秒レーザによる工具成形

小河誉典，片平和俊，島田浩之，山崎和雄，青山英樹

砥粒加工学会誌 Vol.63 ( No.9 ) 470 - 477 2019.09

Research paper (scientific journal), Accepted

Development of Linear Scale Calibration System with Sub-nanometer Resolution

Kayoko Taniguchi, Hideaki Tamiya, Tsutomu Enomoto, Hideaki Aoyama and Kazuo Yamazaki

Proceedings of the 14th International Symposium on Measurement Technology and Intelligent Instruments (ISMTII2019) USB   1 - 4 2019.09

Research paper (international conference proceedings), Joint Work, Accepted

Surface roughness estimation and chatter vibration identification using vision-based deep learning

Rifai A., Fukuda R., Aoyama H.

Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering (Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering)  Vol. 85 ( No. 7 ) 658 - 666 2019.07

ISSN  09120289

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© 2019 Japan Society for Precision Engineering. All rights reserved. This study presents the development of an automatic, low-cost, fast, and reliable non-contact system for surface roughness estimation and chatter vibration identification based on machine vision. The developed system focuses on predicting two critical problems in the machining process, which are surface roughness and chatter vibration, using images as input. Deep learning with convolutional neural networks is integrated into the system to bypass the feature extraction method traditionally used in conventional vision-based roughness and chattel predictions. Two systems are proposed: Separate models that work specifically for each problem, and a combined model Thai aims to predict both problems in one process. Four deep learning architectures are proposed for both systems. The proposed models are built and tested on turned and milled surface datasets. A set of machining experiments are performed with various cutting conditions to generate the training and testing data. The result of the prediction model is then analyzed and compared with the measured data using a contact-based profilomctcr. The results indicate that the proposed system performs favorably in terms of accuracy and processing time, thus offering a promising alternative for quick inspection of surface quality.

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Development of Simulation Method on Three-Dimensional Shape Generation Process by Femtosecond Laser Ablation

Shunsuke Nabetani, Hideki Aoyama, Kazuo Yamazaki, Masahiro Ueda, Yoshinori Ogawa

Proceedings of MTTRF/IAM-CNC ANNUAL MEETING 2019  2019.06

Research paper (international conference proceedings)

Image Based Identification of Cutting Tools in Turning-Milling Machines

Achmad Pratama RIFAI, Ryo FUKUDA, Hideki AOYAMA

精密工学会 Vol.85 ( No.2 ) 159 - 166 2019.02

Research paper (scientific journal), Joint Work, Accepted

Basic Study on Rapid Estimation of Machining Time Based on AI with two-Dimensional Data (Trihedral Figures)

Hiroki Takizawa, Hideki Aoyama, Song Cheol Won

(International Journal of Materials, Mechanics and Manufacturing)  Vol. 7 ( No. 6 ) 235 - 239 2019

Research paper (scientific journal)