• International Journal of Korean Welding Society
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• 제1권1호
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• pp.44-50
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• 2001

A genetic algorithm was applied to the arc welding process as to determine the near-optimal settings of welding process parameters that produce the good weld quality. This method searches for optimal settings of welding parameters through the systematic experiments without the need for a model between the input and output variables. It has an advantage of being capable to find the optimal conditions with a fewer number of experiments rather than conventional full factorial designs. A genetic algorithm was applied to the optimization of the weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed. The output variables were the bead height bead width, and penetration. The number of levels for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions,2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions in less than 40 experiments.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2005년도 춘계학술대회 논문집
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• pp.266-269
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• 2005

The effect of exit temperature on the thickness of recrystallization layer during Al extrusion process was investigated. The recrystallization layer of an extruded Al alloy is an important feature of the product in a wide range of applications, particularly those within the automotive industry. The thicker recrystallized layer in the Al alloys can give rise to a number of problems including reduced fatigue resistance and orange peel during cold forming. But the interaction of extrusion process variables with the thickness of recrystallization layer is poorly understood, and there is limited information available regarding the role of the main hot extrusion variables. Using the 3650 US ton extrusion press, this paper describes the effect of the main process variables such as billet temperature, ram speed, and exit temperature on the thickness of recrystallization layer for the A6XXX Al alloy.

• 통합자연과학논문집
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• 제9권3호
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• pp.215-222
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• 2016

When the linear correlation of the quality variables are considerably high, multivariate control charts may be a more effective way than univariate charts which operate quality variables and process parameters individually. Performances and efficiencies of the multivariate control charts under multivariate normal process has been considered. Some numerical results are presented under small scale of the shifts in the process to see the improvement of the efficiency of EWMA chart and CUSUM chart, which use past quality information, comparing to Shewart chart, which do not use quality information. We can know that the decision of the optimum value of the smoothing constant in EWMA structure or the reference value in CUSUM structure are very important whether we adopt combine-accumulate technique or accumulate-combine technique under the given condition of process.

• 한국분말야금학회:학술대회논문집
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• 한국분말야금학회 2006년도 Extended Abstracts of 2006 POWDER METALLURGY World Congress Part2
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• pp.826-827
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• 2006

Copper infiltration is demonstrated as a viable solution to achieve higher mechanical properties by filling the interconnected porosities of a ferrous structure with copper infiltrant. This paper will present the results of a design of experiments study based on the selected processing variables in the copper infiltration process. The variables are the following: Infiltrating temperatures, infiltrating time at pre-heat zone and hot zone, the green density of iron part, the migration of copper into the iron part at different processing conditions. The results show the flexibility of the infiltration process to attain certain mechanical properties by changing the processing conditions.

• 한국분말재료학회지
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• 제6권2호
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• pp.152-162
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• 1999

The present study is concerned with the effect of PM process variables on the microstructure by using atomized superalloy powders. It is suggested that the inhomogeneity of composition is strongly dependent on the process variables. The contents of segregation elements of plasma rotating electrode process (PREP) powders are larger than those of Ar atomization (AA) powders. As HIP treatment temperature in-increases, the secondary phases on the prior particle boundaries (PPB) have continuous,uniform distribution and high density, but the amount of PPB decreases suddenly at 1150$^{\circ}$C. Segregated phases on the PPB are identified to be MC type carbide. Brittle MC type carbides on the PPB provide fracture initiation sites and preferred fracture path, thereby leading to intergranular type brittle fracture.

• Communications for Statistical Applications and Methods
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• 제27권1호
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• pp.15-35
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• 2020

In research on behavioral studies, significant attention has been paid to the stage-sequential process for multiple latent class variables. We now explore the stage-sequential process of multiple latent class variables using the multivariate latent class profile analysis (MLCPA). A latent profile variable, representing the stage-sequential process in MLCPA, is formed by a set of repeatedly measured categorical response variables. This paper proposes the extended MLCPA in order to explain an association between the latent profile variable and the latent group variable as a form of a two-dimensional contingency table. We applied the extended MLCPA to the National Longitudinal Survey on Youth 1997 (NLSY97) data to investigate the association between of developmental progression of depression and substance use behaviors among adolescents who experienced Authoritarian parental styles in their youth.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.243-260
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• 2008

To prevent low yields in the semiconductor industry is crucial to the success of that industry. However, to prevent low yields is difficult because of too many factors to affect yield variation and their complex relation in the semiconductor manufacturing process. This study presents a new efficient detection methodology for detecting abnormal yields including high and low yields, which can forecast the yield level of a production unit (namely a lot) based on yield-related feature variables' behaviors. In the methodology, we use C5.0 to identify the yield-related feature variables that are the combination of correlated process variables associated with yield, use SOM (Self-Organizing Map) neural networks to extract and classify significant patterns of past abnormal yield lots and finally use C5.0 to generate classification rules for detecting abnormal yield lot. We illustrate the effectiveness of our methodology using a semiconductor manufacturing company's field data.

• International Journal of Korean Welding Society
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• 제3권2호
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• pp.15-23
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed, root opening and the output variables were bead height, bead width, penetration and back bead width. The number of level for each input variable is 8, 16, 8 and 3, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 3,072 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 48 experiments.

• Journal of Welding and Joining
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• 제18권5호
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• pp.63-69
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• 2000

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables was wire feed rate, welding voltage, and welding speed and the output variables were bead height, bead width, and penetration. The number of level for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 40 experiments.

• 산업공학
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• 제12권1호
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• pp.26-31
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• 1999

Evolutionary Operation(EVOP) is a method for continuously monitoring and improving a full-scale process to get an optimal operating condition while production is under way. To avoid appreciable changes in the product quality characteristics only small changes are made in the levels of the process variables. One of the reasons why EVOP is not so popular is that people in charge of the EVOP is blamed when the EVOP does not produce good results. We present an EVOP procedure when prior information of the relationship between defect types and process variables is known. The procedure is illustrated with an injection molding case study.