• 산업공학
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• 제25권1호
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• pp.71-78
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• 2012

Most manufacturing companies participate in various types of active collaboration to enhance competitive advantages in their arenas. In this paper, we introduce a data warehouse system that is designed for manufacturing collaboration. Just as enterprise information systems, collaboration support systems also need functions of performance measurement and monitoring. For this reason, we devise a new approach to measuring and evaluating performance of manufacturing collaboration. Specifically, we first present a concept of process warehouses for manufacturing collaboration. Next, we design a data schema of collaborative process warehouses to store and monitor collaboration performances. Finally, we implement a prototype system to support performance management of manufacturing collaboration. The proposed system can be used to effectively maintain and continuously improve collaboration in manufacturing industry.

• Maxillofacial Plastic and Reconstructive Surgery
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• 제40권
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• pp.2.1-2.7
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• 2018

With the development of computer-aided design/computer-aided manufacturing (CAD/CAM) technology, it has been possible to reconstruct the cranio-maxillofacial defect with more accurate preoperative planning, precise patient-specific implants (PSIs), and shorter operation times. The manufacturing processes include subtractive manufacturing and additive manufacturing and should be selected in consideration of the material type, available technology, post-processing, accuracy, lead time, properties, and surface quality. Materials such as titanium, polyethylene, polyetheretherketone (PEEK), hydroxyapatite (HA), poly-DL-lactic acid (PDLLA), polylactide-co-glycolide acid (PLGA), and calcium phosphate are used. Design methods for the reconstruction of cranio-maxillofacial defects include the use of a pre-operative model printed with pre-operative data, printing a cutting guide or template after virtual surgery, a model after virtual surgery printed with reconstructed data using a mirror image, and manufacturing PSIs by directly obtaining PSI data after reconstruction using a mirror image. By selecting the appropriate design method, manufacturing process, and implant material according to the case, it is possible to obtain a more accurate surgical procedure, reduced operation time, the prevention of various complications that can occur using the traditional method, and predictive results compared to the traditional method.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 추계학술대회 논문집
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• pp.377-378
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• 2012

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 춘계학술대회 논문집
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• pp.787-788
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• 2007

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 춘계학술대회 논문집
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• pp.789-790
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• 2007

• 산업경영시스템학회지
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• 제44권4호
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• pp.12-22
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• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

• International Journal of Safety
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• 제2권1호
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• pp.45-49
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• 2003

In this paper, we investigated the relationship between the variables of the industrial accident factors and the manufacturing performances such as production quantity, quality, cost, and delivery. For this investigation, we collected the real data from 30 small/medium-sized manufacturing industries by performing a questionnaire survey and a on-site inter-view with the workers. Thirty industries were made up of 10 from each of the following three industries: metal processing, machinery manufacturing, and chemical products manufacturing. The data analysis was made using SPSS PC+. Based on the result of the analysis, we came to the tentative conclusion that only two variables such as work skill and load affected all four manufacturing performances and the rest of them two or three performances.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.118-122
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• 2005

Data mining is the process of autonomously extracting useful information or knowledge from large data stores or sets. For analyzing data of manufacturing processes obtained from database using data mining, source data should be collected form production process and transformed to appropriate form. To extract those data from database, a computer program should be made for each database. This paper presents a program to extract easily data form database in industry. The advantage of this program is that user can extract data from all types of database and database table and interface with Teamcenter Manufacturing.

• 한국정밀공학회지
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• 제16권3호통권96호
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• pp.91-102
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• 1999

Fieldbus provides real-time data communication among field devices in the manufacturing automation and process control systems. In this study, an experimental model of fieldbus-based manufacturing automation system is developed. Experimental model consists of two robots, two conveyor belts, NC machine, PLC, sensors and operator station. These machines are interconnected into the Profibus network, and exchange their data through the services provided by FMS(Fieldbus Message Specification), which is the application layer protocol of Profibus. The experimental model is used to measure the network-induced delay of variable and file data transmitted through FMS services. Network-induced delays are collected and analyzed on each sublayer of Profibus protocol stack. The results obtained from the experiment of this study can be effectively utilized when fieldbus is implemented on the practical manufacturing automation systems.

• 항공우주시스템공학회지
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• 제15권6호
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• pp.26-32
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• 2021

The global aircraft-manufacturing industry ecosystem is characterized by the international division of labor through the worldwide supply chain and by the concentration of value added at the top of the supply chain. As a result, the competition for entry into the top supply chain and for order expansion is becoming increasingly intensive. To increase their orders, domestic aircraft manufacturing enterprises need to enhance their competitiveness by evaluating and analyzing it. However, most domestic aircraft manufacturing companies are unaware of the need to quantitatively evaluate their competitiveness. It is challenging to perform such an evaluation, and there are few research cases. In this study, we quantitatively evaluated and analyzed the competitiveness of domestic aircraft manufacturers by using data mining techniques. Thereby, implications for enhancing their competitiveness could be identified.