• Smart Structures and Systems
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• v.22 no.2
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.333-339
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• 2010

Data mining is the process of finding and analyzing data from a big database and summarizing it into useful information for a decision-making. A variety of data mining techniques have been being used for wide range of industries. One application of those is especially so for gathering meaningful information from process data in manufacturing factories for quality improvement. The purpose of this paper is to provide a methodology to improve manufacturing quality of fuel tanks which are auto-parts. The methodology is to analyse influential attributes and establish a model for optimal manufacturing condition of fuel tanks to improve the quality using decision tree, association rule, and feature selection.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.187-201
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• 2009

Semiconductor industry is based on equipment industry and timing industry. In particular, semiconductor process is very complex and as semiconductor-chip width tails and is becoming equipment gradually more as a high technology. Equipment operation is primarily engaged in semiconductor manufacturing (engineers and operator) of being conducted by, equipment errors have also been raised. Equipment operational data related to the error of korea occupational safety and health agency were based on data and production engineers involved in the operator's questionnaire was drawn through the error factor. Equipment operating in the error factor of 9 big item and 36 detail item detailed argument based on the errors down, and 9 big item the equipment during operation of the correlation error factor was conducted. Each of the significance level was correlated with the tabulation and analysis. Using the maximum correlation coefficient, the correlation between the error factors to derive the relationship between factors were analyzed. Facility operating with the analysis of error factors (big and detail item) derive a relationship between the model saw. The end of the operation of the facility in operation on the part of the two factors appeared as prevention. Safety aspects and ergonomics aspects of the approach should be guided to the conclusion.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

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.

• The Journal of Bigdata
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• v.1 no.2
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• pp.9-19
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• 2016

Since ICT convergence became a major issue, German government has carried forward a policy 'Industry 4.0' that triggered ICT convergence with manufacturing. Now this trend gets into our stride. From this facts, we can expect great leap up to quality perfection in low cost. Recently Korean government also enforces policy with 'Manufacturing 3.0' for upgrading Korean manufacturing industry with being accelerated by many related technologies. We, in the paper, developed a custom-made operational big data analysis platform for the implementation of operational intelligence to improve industry capability. Our platform is designed based on spring framework and web. In addition, HDFS and spark architectures helps our system analyze massive data on the field with streamed data processed by process mining algorithm. Extracted knowledge from data will support enhancement of manufacturing performance.

• Journal of the Korean Regional Science Association
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• v.39 no.4
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• pp.19-32
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• 2023

Using extensive job posting big data, this study investigates the industrial and locational characteristics of decent work from the supply side. The analysis revealed that manufacturing is pivotal in supplying decent work, accompanied by a stark regional disparity, most notable in the Seoul Metropolitan Statistical Area (MSA), which constitutes nearly half of all decent work opportunities. The study further uncovered that the distribution of decent work varies significantly across MSAs, with a pronounced inclination towards a higher supply in peripheral rather than central areas. These findings bring to light the critical need for policies that bolster manufacturing, aiming to enhance the availability of high-quality jobs and to bridge the job quality gap between the Seoul MSA and other regions. Moreover, the results emphasize the necessity for customized job supply strategies in each MSA, prioritizing strategies that account for the proximity between workplaces and living areas in the job supply process.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.383-392
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• 2014

The research for coatings is one of the most popular and active research in the polymer industry. For the coatings, electronics industry, medical and optical fields are growing more important. In particular, the trend is the increasing of the technical requirements for the performance and accuracy of the coatings by the development of automotive and electronic parts. In addition, the industry has a need of more intelligent and automated system in the industry is increasing by introduction of the IoT and big data analysis based on the environmental information and the context information. In this paper, we propose an optimization model for the design of experiments based coating formulation data objects using the Internet technologies and big data analytics. In this paper, the coating formulation was calculated based on the best data analysis is based on the experimental design, modify the operator with respect to the error caused based on the coating formulation used in the actual production site data and the corrected result data. Further optimization model to correct the reference value by leveraging big data analysis and Internet of things technology only existing coating formulation is applied as the reference data using a manufacturing environment and context information retrieval in color and quality, the most important factor in maintaining and was derived. Based on data obtained from an experiment and analysis is improving the accuracy of the combination data and making it possible to give a LOT shorter working hours per data. Also the data shortens the production time due to the reduction in the delivery time per treatment and It can contribute to cost reduction or the like defect rate reduced. Further, it is possible to obtain a standard data in the manufacturing process for the various models.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.133-152
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• 2017

Smart factory has been regarded as a big opportunity for manufacturing industries. However, little literature has been studied for the current status of Korean smart factory. Our paper tries to find gaps between research and real world by summarizing the recent literature and cases in Korean context. As the present level of smart factory introductions in Korean small manufacturing firms is lower than what a variety of literature says, our study points out that more efforts, investments and government support are required to catch up with the knowhow and technologies of developed countries although real-time control, enhanced productivity have been obtained. In future research, we will continue the smart factory study with the accumulated real data.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.583-589
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• 2019

One of the core pillars of the Fourth Industrial Revolution is the innovation of the manufacturing industry. From the beginning, the 4th Industrial Revolution appeared in Germany under the concept of 'Industrial 4.0', which means a radical change in the manufacturing industry that can provide super intelligent product production and services based on artificial intelligence and big data. Since manufacturing innovation is a change in the industrial character of the entire nation, the initial role of government is important. Korea also has various policies related to the 4th Industrial Revolution, but there are still various problems to be solved. Therefore, this paper monitors and analyzes the public R&D projects of the advanced countries on manufacturing innovations in the background mentioned above, and through this, the policy implications are drawn.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.6
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• pp.370-376
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• 2016

Manufacturing data analysis and its applications are getting a huge popularity in various industries. In spite of the fast advancement in the big data analysis technology, however, the manufacturing quality data monitored from the automated inspection system sometimes is not reliable enough due to the complex patterns of product quality. In this study, thus, we aim to define the level of trusty of an automated quality inspection system and improve the reliability of the quality inspection data. By correlation analysis and feature selection, this paper presents a method of improving the inspection accuracy and efficiency in an SVM-based automatic product quality inspection system using thermal image data in an auto part manufacturing case. The proposed method is implemented in the sealer dispensing process of the automobile manufacturing and verified by the analysis of the optimal feature selection from the quality analysis results.