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Building an Analytical Platform of Big Data for Quality Inspection in the Dairy Industry: A Machine Learning Approach
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Hyunseok Hwang (Business School, Hallym University)
Sangil Lee (Maeil Dairies Co.)
Sunghyun Kim (Big Data Center, National Information Society Agency)
Sangwon Lee (Department of Computer & Software Engineering, Wonkwang University)
Vol. 24, No. 1, Page: 125 ~ 140
10.13088/jiis.2018.24.1.125
Keywords
Big Data, Quality Inspection, Dairy Industry, Platform Building, Process Control
Abstract
As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.
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유제품 산업의 품질검사를 위한 빅데이터플랫폼 개발: 머신러닝 접근법
황현석 (한림대학교 경영학과)
이상일 (매일유업 청양공장 공무환경팀)
김성현 (한국정보화진흥원 빅데이터센터)
이상원 (원광대학교 컴퓨터소프트웨어공학과(융복합창의연구소))
Keywords
빅데이터, 품질검사, 유제품 산업, 플랫폼 개발, 공정관리
Abstract
품질검사는 중간상품이나 최종상품을 품질관리 표준을 만족하는 양품과 불량품으로 분리하는 일을수행한다. 대량생산체계에서 품질을 수작업으로 검사하는 것은 일관성과 효율성을 저하시키므로 대량으로 생산되는 상품의 품질을 검사하는 것은 다수의 공정에서 기계에 의한 자동 확인과 분류를 포함하게 된다. 생산공정에서 발생하는 데이터를 활용하여 공정을 개선하고 최적화하려는 선행 연구들이 많았음에도 불구하고, 실시간에 많은 데이터를 처리하는데 있어서의 기술적인 한계로 인해 실제 구현에서의 제약이 많이 있었다. 최근 빅데이터에 관한 연구에서는 데이터 처리기술을 개선하였고, 실시간에데이터를 수집, 처리, 분석하는 과정을 가능하게 하게 하고 있다. 본 논문에서는 품질검사를 위한 빅데이터 적용의 단계와 세부사항을 제안하고, 유제품 산업에 적용사례를 제시하려고 한다. 먼저 선행 연구들을 조사하고, 제조 부문에 적용할 수 있는 빅데이터 분석절차를 제안하며 제안된 방법의 실현가능성을 평가하기 위해서, 유제품 산업 분야의 품질검사과정 중하나에 회선신경망(Convolutional Neural Network) 기술 및 랜덤포레스트(Random Forest) 기술을 적용하였다. 품질검사를 위해 제품의 뚜껑 및 빨대의 사진을 수집, 처리, 분석하여, 결함 여부를 판단하고, 과거 품질 검사결과와 비교하였다. 제안된 방법은 과거에 수행되었던 품질검사에 비해 분류 정확성측면에서 의미 있는 개선을 확인할 수 있었다. 본 연구를 통해, 유제품 산업의 빅데이터 활용을 통한품질검사 정확도 개선 가능성을 확인하였다.
Cite this article
JIIS Style
Hwang, H., S. Lee, S. . Kim, and S. . Lee, "Building an Analytical Platform of Big Data for Quality Inspection in the Dairy Industry: A Machine Learning Approach", Journal of Intelligence and Information Systems, Vol. 24, No. 1 (2018), 125~140.

IEEE Style
Hyunseok Hwang, Sangil Lee, Sunghyun Kim, and Sangwon Lee, "Building an Analytical Platform of Big Data for Quality Inspection in the Dairy Industry: A Machine Learning Approach", Journal of Intelligence and Information Systems, vol. 24, no. 1, pp. 125~140, 2018.

ACM Style
Hwang, H., Lee, S., Kim, S. ., and Lee, S. ., 2018. Building an Analytical Platform of Big Data for Quality Inspection in the Dairy Industry: A Machine Learning Approach. Journal of Intelligence and Information Systems. 24, 1, 125--140.
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@article{Hwang:JIIS:2018:716,
author = {Hwang, Hyunseok and Lee, Sangil and Kim, Sunghyun and Lee, Sangwon },
title = {Building an Analytical Platform of Big Data for Quality Inspection in the Dairy Industry: A Machine Learning Approach},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2018},
volume = {24},
number = {1},
month = Mar,
year = {2018},
issn = {2288-4866},
pages = {125--140},
url = {http://dx.doi.org/10.13088/jiis.2018.24.1.125 },
doi = {10.13088/jiis.2018.24.1.125},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Big Data, Quality Inspection, Dairy Industry, Platform Building and Process Control },
}
%0 Journal Article
%1 716
%A Hyunseok Hwang
%A Sangil Lee
%A Sunghyun Kim
%A Sangwon Lee
%T Building an Analytical Platform of Big Data for Quality Inspection in the Dairy Industry: A Machine Learning Approach
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 24
%N 1
%P 125-140
%D 2018
%R 10.13088/jiis.2018.24.1.125
%I Korea Intelligent Information System Society