• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.39 no.1
• /
• pp.130-139
• /
• 2016

The purpose of this paper is to analyze the problems and the sources of defective products and draw improvement plans in a small plastic boat manufacturing process using TOC (Theory Of Constraints) and statistical analysis. TOC is a methodology to present a scheme for optimization of production process by finding the CCR (Capacity Constraints Resource) in the organization or the all production process through the concentration improvement activity. In this paper, we found and reformed constraints and bottlenecks in plastic boat manufacturing process in the target company for less defect ratio and production cost by applying DBR (Drum, Buffer, Rope) scheduling. And we set the threshold values for the critical process variables using statistical analysis. The result can be summarized as follows. First, CCRs in inventory control, material mix, and oven setting were found and solutions were suggested by applying DBR method. Second, the logical thinking process was utilized to find core conflict factors and draw solutions. Third, to specify the solution plan, experiment data were statistically analyzed. Data were collected from the daily journal addressing the details of 96 products such as temperature, humidity, duration and temperature of heating process, rotation speed, duration time of cooling, and the temperature of removal process. Basic statistics and logistic regression analysis were conducted with the defection as the dependent variable. Finally, critical values for major processes were proposed based on the analysis. This paper has a practical importance in contribution to the quality level of the target company through theoretical approach, TOC, and statistical analysis. However, limited number of data might depreciate the significance of the analysis and therefore it will be interesting further research direction to specify the significant manufacturing conditions across different products and processes.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.41 no.3
• /
• pp.165-172
• /
• 2017

In this study, centrifugal fan blades used to circulate cold air inside a household refrigerator were optimized to achieve high performance and low noise by using the response surface method, which is frequently employed as an optimization algorithm when multiple independent variables affect one dependent variable. The inlet and outlet blade angles, and the inner radius, were selected as the independent variables. First, the fan blades were optimized to achieve the maximum volume flow rate. Based on this result, a prototype fan blade was manufactured using a 3-D printer. The measured P-Q curves confirmed the increased volume flow rate of the proposed fan. Then, the rotation speed of the new fan was decreased to match the P-Q curve of the existing fan. It was found that a noise reduction of 1.7 dBA could be achieved using the new fan at the same volume flow rate.

• Korean Journal of Food Science and Technology
• /
• v.51 no.6
• /
• pp.531-536
• /
• 2019

In this study, the optimization of the manufacturing process for the oil-in-water emulsification of rice bran oil was performed by response surface methodology (RSM) using varying amounts of the emulsifier (0.05-0.25%), varying rotation speeds of the homomixer (4,000-8,000 rpm), and varying water to oil ratios (0.8-1.6%) as independent variables, and the emulsion stability index (ESI) as the dependent variable. The optimization conditions predicted by the RSM model were 0.2%g of the total amount of the rice bran oil emulsion, emulsified at the homomixer rotation speed of 6,700 rpm using a water to oil ratio of 1:3. The ESI of the rice bran oil emulsion prepared under the optimized conditions was 95.7%, which was similar to the predicted value of 94.4% obtained by the RSM model. The transmission stability and the backscattering values were found to agree with each other over time and the turbiscan stability index was less than 0.7, indicating that the aggregation and upper floatation were less while the dispersion stability was maintained.

• Journal of the Ergonomics Society of Korea
• /
• v.36 no.3
• /
• pp.183-196
• /
• 2017

Objective: The purpose of this research was to assess the agreement between job physical risk factor analysis by ergonomists using ergonomic methods and physical examinations made by occupational physicians on the presence of musculoskeletal disorders of the upper extremities. Background: Ergonomics is the systematic application of principles concerned with the design of devices and working conditions for enhancing human capabilities and optimizing working and living conditions. Proper ergonomic design is necessary to prevent injuries and physical and emotional stress. The major types of ergonomic injuries and incidents are cumulative trauma disorders (CTDs), acute strains, sprains, and system failures. Minimization of use of excessive force and awkward postures can help to prevent such injuries Method: Initial data were collected as part of a larger study by the University of Utah Ergonomics and Safety program field data collection teams and medical data collection teams from the Rocky Mountain Center for Occupational and Environmental Health (RMCOEH). Subjects included 173 male and female workers, 83 at Beehive Clothing (a clothing plant), 74 at Autoliv (a plant making air bags for vehicles), and 16 at Deseret Meat (a meat-processing plant). Posture and effort levels were analyzed using a software program developed at the University of Utah (Utah Ergonomic Analysis Tool). The Ergonomic Epicondylitis Model (EEM) was developed to assess the risk of epicondylitis from observable job physical factors. The model considers five job risk factors: (1) intensity of exertion, (2) forearm rotation, (3) wrist posture, (4) elbow compression, and (5) speed of work. Qualitative ratings of these physical factors were determined during video analysis. Personal variables were also investigated to study their relationship with epicondylitis. Logistic regression models were used to determine the association between risk factors and symptoms of epicondyle pain. Results: Results of this study indicate that gender, smoking status, and BMI do have an effect on the risk of epicondylitis but there is not a statistically significant relationship between EEM and epicondylitis. Conclusion: This research studied the relationship between an Ergonomic Epicondylitis Model (EEM) and the occurrence of epicondylitis. The model was not predictive for epicondylitis. However, it is clear that epicondylitis was associated with some individual risk factors such as smoking status, gender, and BMI. Based on the results, future research may discover risk factors that seem to increase the risk of epicondylitis. Application: Although this research used a combination of questionnaire, ergonomic job analysis, and medical job analysis to specifically verify risk factors related to epicondylitis, there are limitations. This research did not have a very large sample size because only 173 subjects were available for this study. Also, it was conducted in only 3 facilities, a plant making air bags for vehicles, a meat-processing plant, and a clothing plant in Utah. If working conditions in other kinds of facilities are considered, results may improve. Therefore, future research should perform analysis with additional subjects in different kinds of facilities. Repetition and duration of a task were not considered as risk factors in this research. These two factors could be associated with epicondylitis so it could be important to include these factors in future research. Psychosocial data and workplace conditions (e.g., low temperature) were also noted during data collection, and could be used to further study the prevalence of epicondylitis. Univariate analysis methods could be used for each variable of EEM. This research was performed using multivariate analysis. Therefore, it was difficult to recognize the different effect of each variable. Basically, the difference between univariate and multivariate analysis is that univariate analysis deals with one predictor variable at a time, whereas multivariate analysis deals with multiple predictor variables combined in a predetermined manner. The univariate analysis could show how each variable is associated with epicondyle pain. This may allow more appropriate weighting factors to be determined and therefore improve the performance of the EEM.

• Korean Journal of Applied Biomechanics
• /
• v.12 no.2
• /
• pp.381-392
• /
• 2002

This study attempted to indentify changeability of the factorial structure of kinematic analysis in bowling. Subjects of group composed of three groups : Higher bowers who are national representative bowers with 200 average point and one pro-bowler. Middle bowlers who are three common persons with 170 average points. Lower bowler who are three common persons with 150 average points. Motion analysis on throw motion in three groups respectively has been made through three-dimension cinematography using DLT method. Two high-speed video camera at operating 180 and 60 frame per secondary. T-test factorial structure analysis has been used to define variable relations. It was concluded that : 1. The difference of x1, x2, x4, x8, x9, x11, x12, x13 where significant between two group. 2. The difference of number of spin and angle of the back-hand where statistically significant between two group(p<.001, p<.05) 3. The correlation over r=.5 between the kinematic data x1, x2, x3, x9, x10, x11. In the rotation loading matrix Factor 1 was x1, x2, x9, x10 and Factor 2 relates to x3, x11. 4. In order to obtain the factor score as follow as ; Factor 1 = (0.248)X1 + (0.265)X2 + (-0.074)X3 + (0.259)X9 + (0.259)X10 + (-0.025)X11 Factor 2=(-0.016)X1 + (-0.055)X2 + (0.84)X3 + (-0.013)X9 + (-0.007)X10 + (0.553)X11.

• Journal of Astronomy and Space Sciences
• /
• v.21 no.2
• /
• pp.141-152
• /
• 2004

Optics fabrication process for precision space optical parts includes bound abrasive grinding, loose abrasive lapping and polishing. The traditional bound abrasive grinding with bronze bond cupped diamond wheel leaves the machine marks of about $20{mu}m$ rms in height and the subsurface damage of about 1 ${mu}m$ rms in height to be removed by subsequent loose abrasive lapping. We explored an efficient quantitative control of precision CNC grinding. The machining parameters such as grain size, work-piece rotation speed and feed rate were altered while grinding the work-piece surfaces of 20-100 mm in diameter. The input grinding variables and the resulting surface quality data were used to build grinding prediction models using empirical and multi-variable regression analysis. The effectiveness of such grinding prediction models was then examined by running a series of precision CNC grinding operation with a set of controlled input variables and predicted output surface quality indicators. The experiment achieved the predictability down to ${pm}20$ nm in height and the surface roughness down to 36 nm in height. This study contributed to improvement of the process efficiency reaching directly the polishing and figuring process without the need for the loose abrasive lapping stage.

• Journal of Internet of Things and Convergence
• /
• v.10 no.1
• /
• pp.1-6
• /
• 2024

In order to increase the use of electric vehicles (EVs) and minimize grid strain, microgrid using renewable energy must take an important role. Microgrid may use fossil fuels such as small diesel power, but in many cases, they can be supplied with energy from renewable energy, which is an eco-friendly energy source. However, renewable energy such as solar and wind power have variable output characteristics. Therefore, in order to meet the charging and discharging energy demands of electric vehicles and at the same time supply load power stably, it is necessary to review the configuration of electric vehicle charging infrastructure that utilizes diesel power or electric vehicle-to-grid (V2G) as a parallel energy source in the microgrid. Against this background, this study modelized a microgrid that can stably supply power to loads using solar power, wind power, diesel power, and V2G. The proposed microgrid uses solar power and wind power generation as the primary supply energy source to respond to power demand, and determines the operation type of the load's electric vehicles and the rotation speed of the load synchronous machine to provide stable power from diesel power for insufficient generations. In order to verify the system performance of the proposed model, we studied the stable operation plan of the microgrid by simulating it with MATLAB /Simulink.