Quantitative Evaluation of Machined-Surface Gloss Using Visual Simulation and its Application to Sensory Test

Motohiro Ihara; Iwao Yamaji; Atsushi Matsubara

doi:10.20965/ijat.2022.p0167

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IJAT Vol.16 No.2 pp. 167-174

doi: 10.20965/ijat.2022.p0167

(2022)

Paper:

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Quantitative Evaluation of Machined-Surface Gloss Using Visual Simulation and its Application to Sensory Test

Motohiro Ihara, Iwao Yamaji, and Atsushi Matsubara^†

Department of Micro Engineering, Graduate School of Engineering, Kyoto University
Kyotodaigaku-katsura, Nishikyo-ku, Kyoto 615-8540, Japan

^†Corresponding author

Received:

October 22, 2021

Accepted:

December 21, 2021

Published:

March 5, 2022

Keywords:

machined surface, sensory parameter, quantitative evaluation, gloss, visual simulation

Abstract

In the machining field, the quality of a machined surface is characterized using both quantitative and sensory parameters. It is important to quantitatively evaluate sensory parameters to automate the evaluation of machined surfaces and determine the machining conditions. In this study, we quantitatively evaluate the gloss degree, which is a sensory parameter, via visual simulation. The gloss degree is evaluated based on an angular luminance distribution for machined surfaces cut using different tools. Using the quantitative evaluation result, observation is conducted to predict the appearance of the machined surface, and a sensory test is performed. The result shows that the quantitative evaluation results are consistent with the sensory test results.

Cite this article as:

M. Ihara, I. Yamaji, and A. Matsubara, “Quantitative Evaluation of Machined-Surface Gloss Using Visual Simulation and its Application to Sensory Test,” Int. J. Automation Technol., Vol.16 No.2, pp. 167-174, 2022.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] H. Oktem, T. Erzurumlu, and H. Kurtaran, “Application of response surface methodology in the optimization of cutting conditions for surface roughness,” J. Materials Proc. Tech., Vol.170, pp. 11-16, doi: 10.1016/j.jmatprotec.2005.04.096, 2005.

[2] [2] B. Umroh, Muhathir, and Darianto, “The Optimum Cutting Condition when High Speed Turning of Aluminum Alloy using Uncoated Carbide,” 2019 IOP Conf. Ser.: Mater. Sci. Eng. Vol.505, 012041, doi: 10.1088/1757-899X/505/1/012041, 2019.

[3] [3] M. Mia, P. R. Dey, M. S. Hossain, M. T. Arafat, M. Asaduzzaman, M. S. Ullah, and S. M. Tareq Zobaer, “Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition,” Measurement, Vol.122, pp. 380-391, doi: 10.1016/j.measurement.2018.02.016, 2018.

[4] [4] S. Yamashita, T. Furuki, H. Kousaka, T. Hirogaki, E. Aoyama, K. Inaba, and K. Fujiwara, “Investigation of Optimum Grinding Condition Using cBN Electroplated End-Mill for CFRP Machining,” Int. J. Automation Technol., Vol.15, No.1, pp. 4-16, doi: 10.20965/ijat.2021.p0004, 2021.

[5] [5] M. Yonehara, K. Kitahara, Y. Kagawa, H. Isono, and T. Sugibayashi, “Effect of topography on glossiness and surface color for a 5052 aluminum alloy,” J. Japan Institute of Light Metals, Vol.55, No.1, pp. 15-19, doi: 10.2464/jilm.55.15, 2005.

[6] [6] S. Fujimoto, T. Aizawa, and H. Suyama, “Study on Iridescent Surface,” J. Japan Soc. of Prec. Eng., Vol.32, No.6, pp. 409-413, doi: 10.2493/jjspe1933.32.409, 1966.

[7] [7] S. Fujimoto, “Microscopic Gloss of White Iridescent Surface,” J. Japan Soc. of Prec. Eng., Vol.41, No.8, pp. 758-763, doi: 10.2493/jjspe1933.41.758, 1973.

[8] [8] M. Ihara, I. Yamaji, and A. Matsubara, “On Classification and Quantification of Sensory Parameters in Judging Mirror Surface: Appearance of a Turned Aluminum Alloy Surface,” Proc. of the 2020 Int. Symp. on Flexible Automation, 2020 Int. Symp. on Flexible Automation, V001T08A001, doi: 10.1115/ISFA2020-9607, 2020.

[9] [9] R. Sato, Y. Sato, K. Shirase, G. Campatelli, and A. Scippa, “Finished Surface Simulation Method to Predicting the Effects of Machine Tool Motion Errors,” Int. J. Automation Technol., Vol.8, No.6, pp. 801-810, doi: 10.20965/ijat.2014.p0801, 2014.

[10] [10] P. Beckmann and A. Spizzichino, “The Scattering of Electromagnetic Waves from Rough Surfaces,” Artech House, Inc., Vol.22, 1987.

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[12] [12] M. Ihara, I. Yamaji, K. Fujii, T. Watanabe, M. Yauchi, and A. Matsubara, “Development of a Visual Simulation Method for Quantifying Sensory Evaluation of Machined Surface,” J. the Japan Soc. Prec. Eng., Vol.87, No.11, pp. 894-902, doi: 10.2493/jjspe.87.894, 2021.

[13] [13] A. C. Chadwick and R. W. Kentridge, “The perception of gloss: A review,” Vision Res., Vol.109, pp. 221-235, doi: 10.1016/j.visres.2014.10.026, 2015.

[14] [14] ISO2813: 2014, “Paints and varnishes – Determination of gloss value at 20°, 60° and 85°,” 2014.

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[17] [17] A. Nishio, T. Shimokawa, N. Goda, and H. Komatsu, “Perceptual Gloss Parameters Are Encoded by Population Responses in the Monkey Inferior Temporal Cortex,” J. Neuroscience, Vol.34, No.33, pp. 11143-11151, doi: 10.1523/JNEUROSCI.1451-14.2014, 2014.

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[19] [19] H. Hasunuma, “Gloss,” Tokyo, Corona Publishing, Co., Ltd., 1960 (in Japanese).

[20] [20] Y. Sato, T. Nakanishi, R. Sato, K. Shirase, M. Oda, and N. Nakayama, “Study on the evaluation method for finished surface based on human visual characteristic,” Proc. of 2016 Int. Symp. on Flexible Automation (ISFA2016), pp. 428-431, doi: 10.1109/ISFA.2016.7790201, 2016.

[21] [21] T. Yatagai, “Optics,” Tokyo, Asakura Publishing, Co., Ltd., 2017 (in Japanese).

[22] [22] CIE, “An Analytic Model for Describing the Influence of Lighting Parameters Upon Visual Performance (2nd Ed.),” CIE Publishing, 1981.

Quantitative Evaluation of Machined-Surface Gloss Using Visual Simulation and its Application to Sensory Test

Motohiro Ihara, Iwao Yamaji, and Atsushi Matsubara†

Motohiro Ihara, Iwao Yamaji, and Atsushi Matsubara^†