• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.19 no.4
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• pp.390-402
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• 2009

This study aimed to examine the general characteristics of individual workers, psychosocial working environment, and ergonomic risk factors which affect the status of musculoskeletal disorders. Self-report was carried out for musculoskeletal symptoms and ergonomic risks in working environment in 856 production workers in 16 small to medium sized manufacturing companies. Musculoskeletal symptoms were examined with a standardized questionnaire, and ergonomic risks were evaluated with a qualitative self-administered instrument for the tasks related to musculoskeletal disorders. Major findings were as follows: 1) Complaint rate for musculoskeletal symptoms was higher in female, aged, married workers with longer working hours, less leisure/hobby activity, longer household working hours and history of disease or accident. 2) Complaint rate for musculoskeletal symptoms was significantly higher in workers with dissatisfaction, difficult tasks, and no self-control at work. 3) Complaint rate for musculoskeletal symptoms was significantly higher in workers involved in tasks with major ergonomic risk factors, and handling heavy equipment. 4) Explanatory power increased the model with the musculoskeletal symptoms as dependent variable and demographic variables, psychosocial working environment and ergonomic risk factors included, and total explanatory power of 18.6% revealed the significant effect. Based on the results, we can conclude that musculoskeletal symptoms in manufacturing workers are associated with individual demographic characteristics, psychosocial working environment and ergonomic risk factors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.805-807
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• 2016

By using ICT in the manufacturing industry with the advent of "Industry 4.0" of Germany, it has been promoted a lot of research to maximize the production capacity. Be particularly increased manufacturing complexity, increase the information delivered to the operator, augmented reality technologies in order to maximize production have been studied. In this paper, in order to solve the deficiencies problem that frequently occurs in the actual site, using the augmented reality technology, provides a system for providing personal training information to the worker. Proposed system, by fusing worker information and process another job, generates educational information of the individual, it is to provide information through the video of the tablet. When using such methods, it is expected that it is possible to improve the production quality of the manufacturing industry to overcome the deficiencies problem that frequently occurs through the continuing education of employees.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.6
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• pp.130-138
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• 2012

There are increasing demands from the industry for intelligent robot-calibration solutions, which can be tightly integrated to the manufacturing process. A proposed solution can simplify conventional robot-calibration and teaching methods without tedious procedures and lengthy training time. iGPS(Indoor GPS) system is a laser based real-time dynamic tracking/measurement system. The key element is acquiring and reporting three-dimensional(3D) information, which can be accomplished as an integrated system or as manual contact based measurements by a user. A 3D probe is introduced as the user holds the probe in his hand and moves the probe tip over the object. The X, Y, and Z coordinates of the probe tip are measured in real-time with high accuracy. In this paper, a new approach of robot-calibration and teaching system is introduced by implementing a 3D measurement system for measuring and tracking an object with motions in up to six degrees of freedom. The general concept and kinematics of the metrology system as well as the derivations of an error budget for the general device are described. Several experimental results of geometry and its related error identification for an easy compensation / teaching method on an industrial robot will also be included.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.5789-5794
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• 2015

Recently, thin and light-weight production technologies are needed in IT industry in accordance with increase of the smart phones and mobile PC products. In order to make light and high rigidity products, engineering plastic and aluminum materials are frequently used in products appearance and frame hat support structure. Especially aluminum extrusion and CNC Brick processes are widely used for high strength and high rigidity technology. But extrusion method has constraints to apply exterior design and CNC Brick process has relatively high production cost and low speed of manufacturing. In this research, a new process method is introduced in order to reduce material cost and to improve manufacturing speed dramatically. Plate forging process means basically that thickening of local wall area thickness after deform exterior shape by deep drawing and bending process. Therefore, it is possible to minimize the waste of material and the manufacturing time. In this study the process of plate forging is designed using finite element program AFDEX-2D and the thickness and the width of initial deformed blank. And it is verified as a sample which is a part of laptop developed through the proposed plate forging method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.890-895
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• 2021

Recently, the "Act of Serious Disasters, etc" was enacted and institutional and social interest in safety accidents is increasing. In this paper, we analyze statistical data published by government agency on safety accidents that occur in manufacturing sites, and compare various object detection models based on deep learning to build a model to determine dangerous situations to reduce the occurrence of safety accidents. The data-set was directly constructed by collecting images from CCTVs at the manufacturing site, and the YOLO-v4, SSD, CenterNet models were used as training data and evaluation data for learning. As a result, the YOLO-v4 model obtained a value of 81% of mAP. It is meaningful to select a class in an industrial field and directly build a dataset to learn a model, and it is thought that it can be used as an initial research data for a system that determines a risk situation and infers it.

• The Journal of Bigdata
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• v.8 no.1
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• pp.49-59
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• 2023

There are various items in the safety and health standards of the manufacturing industry, but they can be divided into work-related diseases and musculoskeletal diseases according to the standards for sickness and accident victims. Musculoskeletal diseases occur frequently in manufacturing and can lead to a decrease in labor productivity and a weakening of competitiveness in manufacturing. In this paper, to detect the musculoskeletal harmful factors of manufacturing workers, we defined the musculoskeletal load work factor analysis, harmful load working postures, and key points matching, and constructed data for Artificial Intelligence(AI) learning. To check the effectiveness of the suggested dataset, AI algorithms such as YOLO, Lite-HRNet, and EfficientNet were used to train and verify. Our experimental results the human detection accuracy is 99%, the key points matching accuracy of the detected person is @AP0.5 88%, and the accuracy of working postures evaluation by integrating the inferred matching positions is LEGS 72.2%, NECT 85.7%, TRUNK 81.9%, UPPERARM 79.8%, and LOWERARM 92.7%, and considered the necessity for research that can prevent deep learning-based musculoskeletal diseases.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.7
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• pp.591-596
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• 2014

Augmented reality is a novel human-machine interaction that overlays virtual computer-generated information on a real world environment. It has found good potential applications in many fields, such as training, surgery, entertainment, maintenance, assembly, product design and other manufacturing operations. In this study, a smartphone-based augmented reality system was developed for the purpose of monitoring and managing injection molding production lines. Required management items were drawn from a management content analysis, and then the items were divided into two broad management categories: line management and equipment management. Effective work management was enabled by providing those working on the shop floor with management content information combined with the actual images of an injection molding production line through augmented reality.

• Steel and Composite Structures
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• v.29 no.3
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• pp.349-362
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• 2018

Thermal buckling behavior of porous functionally graded nanobeam integrated with piezoelectric sensor and actuator based on the nonlocal higher-order shear deformation beam theory is investigated for the first time. Its material properties are assumed to be temperature-dependent and varying along the thickness direction according to the modified power-law rule. Note that the porosity with even type is considered herein. The equations of motion are obtained through Hamilton's principle. The influences of several parameters (such as type of temperature distribution, external electric voltage, material composition, porosity, small-scale effect, Ker foundation parameters, and beam thickness) on the thermal buckling of FG nanobeam are investigated in detail.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.154-167
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• 2004

The TOC/DBR and Six Sigma are the most attention-getting concepts for managing manufacturing companies in Korea. Using the ideas and methods of the TOC/DBR, companies can achieve a large reduction of work-in-process and finished-good inventories, significant improvement in scheduling performance, and substantial earnings increase. The Six Sigma approach derives the overall process of selecting the right projects based on their potential to improve performance metrics and selecting/training the right people to get the business results. These two concepts have different backgrounds and different viewpoints for production systems. So, if the two concepts collaborate each other, the synergy effects to innovate production systems can be expected. This paper proposes a new approach to implement the TOC/DBR concepts in production systems. This approach uses some concepts of Six Sigma which stresses educations, project approaches (step by step procedure using a Roadmap), and improvement philosophy.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.393-398
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• 2002

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. forthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.