• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.170-174
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• 2004

Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO$_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO$_2$ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.3_1spc
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• pp.587-592
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• 2013

Until recently, the primary users of wheelchairs were people with lower body disabilities. However, the number of patients recovering from accidents or surgery, as well as the number of elderly people using wheelchairs, is constantly increasing. This study examined the design and manufacture of standing and gait assist wheelchairs that assist temporary gait disturbed patients to take rehabilitation training and elderly people to engage in walking exercise. A kinematic analysis was used to select a drive motor and design a four-bar linkage mechanism for lifting the backrest vertically. Using a multibody dynamic simulation, detailed design was performed taking into consideration the spatial motion and partial interference, and the necessary push force and stroke of the linear actuator were also calculated. To ensure structural safety, the von-Mises equivalent stresses of the upper and lower brackets of the linear actuator were verified through a finite element analysis. The manufactured wheelchair was shown to operate successfully as intended, using the developed controller for the drive motors and linear actuator.

• Journal of the Korea Safety Management & Science
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• v.14 no.3
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• pp.183-191
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• 2012

Since June 25th 1950, the beginning of the cold war (Korean war), Korean peninsula is still in a state of war. Officially South and North Korean government call a truceafter three years from the beginning day, however both countries are still having several combats in these days. So every Korean citizen male has duty for serving military duty and this lasts even after the serving regular military force, as reserved military. Although South Korea is very small country, the size of military is very large so informing all reserved military takes some time. Since this nation is confronting the enemy and considering the global potential threat, South Korean military needs expedite informing system to call up the reserved military to active duty. In this project, the current informing system has been analyzed and compared with the new method which is using social network service such as Twitter. However mobilization order is very critical. So in our new model there are two ways combined. Using twitter to inform and then use traditional ways to finish the order. This method will provide more efficient and accurate way to cover the call ups.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.381-389
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• 1999

One of the objectives of any task design is to provide a safe and helpful workplace for the employees. The safety and health module may include means for confronting the design with safety and health regulations and standards as well as tools for obstacles and collisions detection (such as error models and simulators). Virtual Reality is a leading edge technology which has only very recently become available on platforms and at prices accessible to the majority of simulation engineers. The design of an automated manufacturing system is a complicated, multidisciplinary task that requires involvement of several specialists. In this paper, a design procedure that facilitates the safety and ergonomic considerations of an automated manufacturing system are described. The procedure consists of the following major steps: Data collection and analysis of the data, creation of a three-dimensional simulation model of the work environment, simulation for safety analysis and risk assessment, development of safety solutions, selection of the preferred solutions, implementation of the selected solutions, reporting, and training When improving the safety of an existing system the three-dimensional simulation model helps the designer to perceive the work from operators point of view objectively and safely without the exposure to hazards of the actual system.

• Journal of the Ergonomics Society of Korea
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• v.27 no.4
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• pp.65-71
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• 2008

This study conducted two surveys to establish an educational system to prevent work-related musculoskeletal disorders. The first survey investigates the factors influencing the recognition of job specifications for risk factor analysts of work-related musculoskeletal disorders. The respondents of the first survey were selected from 173 different small-sized manufacturing enterprises having less than 300 workers and had experienced the inspection of risk factors. The results of the first survey shows that better recognition of job specifications are followed by the increase of education time, but regardless of the positions and periods of job experience. Among the respondents with a high level recognition, the second survey was conducted. According to the results, it is presented that 2, 8, 16 hours are the most suitable for educating workers, superintendents, and risk factors analysts. Based on the results of the second survey, educational contents have also been suggested. This research will provide the basic information when forming education systems to prevent musculoskeletal disorders.

• Journal of Fashion Business
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• v.18 no.5
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• pp.144-158
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• 2014

National Competency Standards(NCS) is to systemize the competency that is necessary for performing duties in industrial fields and to utilize synthetically duty, vocational education training, and qualification at the national level. The purpose of this study is to analyze the process of NCS development and verification, which systemizes competency units and competency unit elements through the analysis of duty in pattern field. It is to cultivate competent people to be able to promote the development of pattern industry ultimately through being a complementary to educational circles and industry then to give them practical knowledge that is demanded in the field. Furthermore, it is to be utilized as a fundamental data for reforming the system of duty competency evaluation so as to manage personnel career systematically and to improve their competency. Focus Group Interview(FGI) was adapted as the method of this study, which was proceeded 3 times, and validity of the drawn result is verified through expert questionnaire survey. Research result, which is competency units, is 10 as follows; Fit trend analysis, Analysis of sample garment Specification sheet, Pattern making for sample garment, Pattern making for manufacturing garment, Creation of sewing specification, Instruction of manufacturing technique, Sample garment Inspection for quality control, Grading, Calculation of the required material quantities, Quality control.

• Safety and Health at Work
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• v.9 no.4
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• pp.427-433
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• 2018

Background: The purpose of this study was to identify the influence of workers' perceived workload, accident experiences, supervisors' safety leadership, and an organization's safety climate on the cognitive and emotional risk perception. Methods: Six hundred and twenty employees in a variety of manufacturing organizations were asked to complete to a questionnaire. Among them, a total of 376 employees provided valid data for analysis. To test the hypothesis, correlation analysis and hierarchical regression analysis were used. Statistical analyses were conducted using IBM SPSS program, version 23. Results: The results indicated that workload and accident experiences have a positive influence and safety leadership and safety climate have a negative influence on the cognitive and emotional risk perception. Workload, safety leadership, and the safety climate influence perceived risk more than accident experience, especially for the emotional risk perception. Conclusion: These results indicated that multilevel factors (organization, group, and individual) play a critical role in predicting individual risk perceptions. Based on these results, therefore, to reduce risk perception related with unsafe behaviors and accidents, organizations need to conduct a variety of safety programs that enhance their safety climate beyond simple safety-related education and training. Simultaneously, it needs to seek ways to promote supervisors' safety leadership behaviors (e.g., site visits, feedback, safety communication, etc.). In addition, it is necessary to adjust work speed and amount and allocate task considering employees' skill and ability to reduce the workload for reducing risk perception.

• Journal of the Korean Society of Safety
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• v.38 no.4
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• pp.23-31
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• 2023

Revising the Occupational Safety and Health Act led to enacting and revising related laws and systems, such as placing fire observers in hot workplaces. However, the operating standards in such cases are still ambiguous. Although fire accidents occur through multiple and multi-step factors, the hazards of fire accidents have been identified in this study as individual rather than interrelated factors. The aim has been to identify multiple factors of accidents, outlining fire and explosion accidents that recently occurred in the domestic manufacturing industry. First, major keywords were extracted through text mining. Then representative accident types were derived by combining the main keywords through the co-word network analysis to identify the hazards and their relationships. The representative fire accidents were identified as six types, and their major hazards were then addressed for improving safety measures using the identification of hazards in the "Risk Assessment" tool. It is found that various safety measures, such as professional fire observers' training and clear placement standards, are needed. This study will provide useful basic data for revising practical laws and guidelines for fire accident prevention, system supplementation, safety policy establishment, and future related research.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.108-111
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• 2022

Applying deep learning to machine vision systems for defect detection of products requires vast amounts of training data about various defect cases. However, since data imbalance occurs according to the type of defect in the actual manufacturing industry, it takes a lot of time to collect product images enough to generalize defect cases. In this paper, we apply a Siamese neural network that can be learned with even a small amount of data to product defect detection, and modify the image pairing method and contrastive loss function by properties the situation of product defect image data. We indirectly evaluated the embedding performance of Siamese neural networks using AUC-ROC, and it showed good performance when the images only paired among same products, not paired among defective products, and learned with exponential contrastive loss.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.513-515
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• 2022

Manufacturing data collected using IoT devices in a smart factory environment is generally reliable except for noises caused by external factors. However, unlike manufacturing data that is collected mechanically, process data manually recorded by field-workers can cause problems such as the misspelled entries or the missing entries. Therefore, process data must be validated before being used as training data for artificial intelligence models. In this paper, based on the fact that which is a linear relationship between the power consumption of the MCT machine and the production of the product recorded by the field-workers, we detect anomalous records of the workers using robust linear regression.