• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.69-70
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• 2010

• The Journal of Information Systems
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• v.26 no.2
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• pp.105-121
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• 2017

Purpose It is a major goal to improve the product yields during production operations in the manufacturing industry. Therefore, factory is trying to keep the good quality materials and proper production resources, also find the proper condition of facilities and manufacturing environment for yields improvement. Design/methodology/approach We propose the hybrid framework to analyze to dataset extracted from MES. Those data is about the alarm information generated from equipment, both measurement and equipment process value from production and cycle/pitch time measured from production data these covered products during production. We adapt a data warehousing techniques for organizing dataset, a logistic regression for finding out the significant factors, and a association analysis for drawing the rules which affect the product yields. And then we validate the framework by applying the real data generated from the discrete process in secondary cell battery manufacturing. Findings This paper deals with challenges to apply the full potential of modeling and simulation within CPPS(Cyber-Physical Production System) and Smart Factory implementation. The framework is being applied in one of the most advanced and complex industrial sectors like semiconductor, display, and automotive industry.

• Advances in Computational Design
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• v.4 no.1
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• pp.43-52
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• 2019

Variation Analysis (VA) is used to simulate final product variation, taking into consideration part manufacturing and assembly variations. In VA, all the manufacturing and assembly processes are defined at the product design stage. Process Capability Data Bases (PCDB) provide information about measured variation from previous products and processes and allow the designer to apply this to the new product. A new challenge to this traditional approach is posed by the Industry 4.0 (I4.0) revolution, where Smart Manufacturing (SM) is applied. The manufacturing intelligence and adaptability characteristics of SM make present PCDBs obsolete. Current tolerance analysis methods, which are made for discrete assembly products, are also challenged. This paper discusses the differences expected in future factories relevant to VA, and the approaches required to meet this challenge. Current processes are mapped using I4.0 philosophy and gaps are analysed for potential approaches for tolerance analysis tools. Matching points of simulation capability and I4.0 intents are identified as opportunities. Applying conditional variations, incorporating levels of adjustability, and the un-suitability of present Monte Carlo simulation due to changed mass production characteristics, are considered as major challenges. Opportunities including predicting residual stresses in the final product and linking them to product deterioration, calculating non-dimensional performances and extending simulations for process manufactured products, such as drugs, food products etc. are additional winning aspects for next generation VA tools.

• Journal of Distribution Science
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• v.20 no.5
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• pp.1-11
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• 2022

Purpose: This study attemptsto investigate the moderating effects of environmental turbulence (ET) between learning orientation (LO) and SMEs' performance. Research design, data, and Methodology: To gain insights and provide implications for manufacturing SMEs in Pakistan, this study adopted simple random sampling to collect 379 valid responses. Data were collected through a self-administrative questionnaire from manufacturing SMEs owners/managers. Partial least squares of structural equation modeling have been used to test research hypotheses by using SmartPLS® 3.0 software. Results: The study's primary finding is that LO has a significantly positive effect on SMEs' performance and this relationship is strengthened under the moderating influence of environmental turbulence (ET). Conclusion: Environmental turbulence (ET) enables SMEs to focus on learning capability to get a more competitive advantage. Moreover, SMEs owner/managers ought to emphasize continuous learning that accentuates the capability to compete with environmental changes. Findings support notifying Pakistan's Small and Medium Enterprise Development Authority (SMEDA) in dealings with Manufacturing SMEs in terms of improving their internal capabilities. This research contributes to the literature as it provides a more detailed and in-depth explanation of distribution management-related issues faced by SMEs. This research carries a significant influence on literature and relevant Resource-based view and contingency theories.

• Asia Pacific Journal of Business Review
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• v.6 no.2
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• pp.1-26
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• 2022

This study uses the human resource management (HRM) practice and ability, motivation, and opportunities (AMO) theory and corporate social and environmental responsibility, to explore the effects of environmental responsible human resource management practice (ER-HRM) on energy-intensive manufacturing's green technology innovation and organizational effectiveness. A self-completed questionnaire was administered to managers of energy-intensive manufacturing in the Lao PDR. The data was collected from 198 managers of energy-intensive manufacturing for analysis. We used structural equation modeling (SEM) by smart PLS 3.0 to test the hypotheses in this research. The findings have shown a strong direct and positive impact of the environmental ability, motivation, and opportunity of ER-HRM practice on green technology innovation and organizational effectiveness. The ability of ER-HRM practice has the highest influence on green technology innovation and organizational effectiveness. The findings also prove the partial mediation of green technology innovation links ER-HRM with organizational effectiveness. This research is expected to identify the influences of ER-HRM in energy-intensive manufacturing to achieve innovation and performance while reducing emissions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.137-143
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• 2022

Composite materials, being light-weight and of high mechanical strength, are increasingly used in various industries such as the aerospace, automobile, sporting-goods manufacturing, and ship-building industries. Recently, manufacturing of composite materials using 3D printers has increased. 3D-printed composite materials are made in free-form and adapted for end-use by adjusting the fiber content and orientation. However, research on the machining of 3D printed composite materials is limited. The aim of this study is to develop a machine learning method to select machining conditions for machining of 3D-printed composite materials. The composite material was composed of Onyx and carbon fibers and stacked sequentially. The experiments were performed using the following machining conditions: spindle speed, feed rate, depth of cut, and machining direction. Cutting forces of the different machining conditions were measured by milling the composite materials. PCA, a method of machine learning, was developed to select the machining conditions and will be used in subsequent experiments under various machining conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.78-81
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• 2021

Through the smart factory construction project, sensors can be installed in manufacturing production facilities and various process data can be collected in real time. Through this, research on real-time facility anomaly detection is being actively conducted to reduce production interruption due to facility abnormality in the manufacturing process. In this paper, to detect abnormalities in production facilities, the manufacturing data was applied to deep learning models Autoencoder(AE), VAE(Variational Autoencoder), and AAE(Adversarial Autoencoder) to derive the results. Manufacturing data was used as input data through a simple moving average technique and preprocessing process, and performance analysis was conducted according to the window size of the simple movement average technique and the feature vector size of the AE model.

• International Commerce and Information Review
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• v.14 no.3
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• pp.113-138
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• 2012

This study examined the determinants of export performance of small and medium sized manufacturing companies in Korea. Depending on the existing research, taking entrepreneurship and product differentiation capabilities as antecedents of export performance. In addition, the product differentiation capabilities examined whether the role of the mediating effects between entrepreneurship and export performance. Thus, the purpose of the study is look at the integrated model of entrepreneurship, product differentiation capabilities and export performance building and their relationship. On 152 domestic companies, empirical analysis was performed. Empirical analysis was conducted using the PLS(Partial Least Square). And analysis tools were used SmartPLS2.0. In the results of the analysis, risk tolerance and innovativeness of small and medium sized manufacturing businesses, entrepreneurs and product differentiation competence have positive impact export performance in both. Product differentiation capabilities also confirmed that it have a positive impact on the export performance of small manufacturing export enterprises. In analysis of the mediated effect in product differentiation capacity showed a significant mediated effect between innovativeness and export performance. But mediated effects did not indicate a significant risk tolerance. these results suggests the need to actively pursue innovation that more product differentiation capabilities required in export companies to small and medium-sized manufacturing. In other words, when attempting to highlight product differentiation, based on innovation, rather than risk tolerance. In terms of analyzing the mediated role of product differentiation capabilities, this study has theoretical implications for the future research to look at the antecedents of export performance from the perspective of dynamic capabilities and competitive advantage. Also, practical implications in this regard as the innovativeness and taking risks to all important to CEO of small manufacturing enterprises but, stranger in the foreign market competition environment, the role of innovation product is required on raising product differentiation capabilities are presented.

• Smart Structures and Systems
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• v.24 no.5
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• pp.659-668
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• 2019

In digital image correlation, speckle image is closely related to the measurement accuracy. A practical global evaluation criterion for speckle image is presented. Firstly, based on the essential factors of the texture image, both the average particle size and image sharpness are used for the assessment of speckle image. The former is calculated by a simplified auto-covariance function and Gaussian fitting, and the latter by focusing function. Secondly, the computation of the average particle size and image sharpness is verified by numerical simulation. The influence of these two evaluation parameters on mean deviation and standard deviation is discussed. Then, a physical model from speckle projection to image acquisition is established. The two evaluation parameters can be mapped to the physical devices, which demonstrate that the proposed evaluation method is reasonable. Finally, the engineering application of the evaluation method is pointed out.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.1
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• pp.110-115
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• 2021

A multi-band, compact, and complex vehicle roof antenna has become important in terms of car exterior design and multi-functions which include Radio, DAB/DMB, SXM, GNSS, Telematics, and V2X. In this paper, we propose a compact multi-band V2X pole-type roof antenna. Using impedance change characteristic, a single pole antenna which has multiband such as radio, DAB/DMB, telematics, and V2X band is proposed. With two patch antennas for GNSS and SXM, the dimension of a multiband roof antenna is 131x63x37mm only.