• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.1
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• pp.1-6
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• 2012

The polishing of silicon wafers has an important role in semiconductor manufacturing. Generally, getting a flat surface such as a mirror is the purpose of the process. The wafer surface roughness is affected by many variables such as the characteristics of the carrier head unit, operation, speed, the pad and slurry temperature. Optimum process conditions for experimental temperature, pH value, down-force, slurry ratio are investigated, time is used as a fixed factor. This study carried out a series of experiments at varying platen, chuck rpm and oscillation cpm taking particular note of the difference between the rpm and the affect it has on the surface roughness. In this experiment determine the optimum conditions for polishing silicone wafers.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.4
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• pp.13-26
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• 1999

When we try to design a production planning system for a manufacturing company, it is a time consuming task to analyze various planning activities and identify inter-relationship among a lot of decisions made for the production planning. Most of the research efforts have been concentrated to well-organized independent decision-making problems that may usually be identified only after analyzing the characteristics of the decision-making process as a whole. In this paper, a methodology is suggested to characterize the whole process of the production planning for a manufacturing company and reduce the complexity of decision-making problems. The methodology is based on an experience of developing a production planning software for an automobile component manufacturer in korea. First, it is explained how to identify and represent the dependency among various decision-making variables. And a methodology is proposed to analyze the identified dependency among decision variables and identify decision-making process. Lastly, a practical example is provided to illustrate the analysis procedure in this paper.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.65-73
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• 2015

This paper presents a method of predicting the machining parameters on the turning process of low carbon steel using a neural network with back propagation (BP) and particle swarm optimization (PSO). Cutting speed, feed rate, and depth of cut are used as input variables, while surface roughness and electric current consumption are used as output variables. The data from experiments are used to train the neural network that uses BP and PSO to update the weights in the neural network. After training, the neural network model is run using test data, and the results using BP and PSO are compared with each other.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.1
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• pp.48-56
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• 2009

This paper presents the results of miniaturized micro milling machine tool development for micro precision machining process. Finite element analysis has been performed to know the relationship between design dimensional variables and structural stiffness in terms of static, dynamic, thermal aspects. Design optimization has been performed to optimize the design variables of micro machine tool to minimize the volume, weight and deformation of machine tool structure and to maximize the stiffness in terms of static, dynamic, and thermal characteristics. This study presents the assessment of the technology incentive for the minimization of machine tool in the quantitative context of static, dynamic stiffness, thermal resistance and thus the accuracy implications. This study can also be provided a basic knowledge for further research of micro factory development.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.9
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• pp.53-58
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• 2020

This study analyzed the surface roughness of the fine particle spraying process in the plane and the surface roughness by the factors in the fine particle spraying process on the helical surface is analyzed. Finally, the surface fine particle spraying process and the helical curved surface fine particle Analyze the difference in injection conditions of the injection process. Key process variables are particle type, nozzle diameter, and pressure. The remaining conditions are fixed values of. A total of 32 experiments were conducted, each with different process variables. Rectangular and cylindrical specimens were fabricated and a corresponding jig was prepared for use in the experiment. Analyses conducted by using ANOVA enabled comparisons of the effects of each process variable on the experiment.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.36-41
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• 1997

With the trend towards welding automation and robozation, mathematical models for studying the influence of various parameters on the weld bead geometry in Gas Metal Arc(GMA) welding process are required. The results of bead on plate welds deposited using the GMA welding process has enabled mathematical relationships to be developed that model the weld bead geometry. Experimental results were compared to outputs obtained using existing formulae that correlate process input variables to output parameters and subsequent modelling was performed in order to better predict the output of the GMA welding process. The aim of this work was to explain the relationships between GMA welding variables and weld bead geometry and thus, be able to predict input weld bead size. The relationships can be usefully employed for open loop process control and also for adaptive control provided that dynamic sensing of process output is performed.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.4
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• pp.334-344
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• 2003

A mixture experiment is a special type of response surface experiment in which factors are the ingredients or components of a mixture, and the response is a function of the proportions of each ingredient. Evolutionary operation is useful to improve on-line full-scale manufacturing process by systematically changing the levels of the process variables without jeopardizing the product. This paper presents an evolutionary operation procedure with mixture variables for large-scale mixture production process which can be beneficial to practitioners who should improve on-line mixture quality while maintaining the production amount of the mixture product.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.2
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• pp.55-63
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• 2014

Fractures and wrinkles are two major defects frequently found in the sheet metal forming process. The process has several noise factors that cannot be ignored when determining the optimal process conditions. Therefore, without any countermeasures against noise, attempts to reduce defects through optimal design methods have often led to failure. In this study, a new and robust design methodology that can reduce the possibility of formation of fractures and wrinkles is presented using decision-making theory. A two-attribute value function is presented to form the design metric for the sheet metal forming process. A modified complex method is adopted to isolate the optimal robust design variables. One of the major limitations of the traditional robust design methodology, which is based on an orthogonal array experiment, is that the values of the optimal design variables have to coincide with one of the experimental levels. As this restriction is eliminated in the complex method, a better solution can be expected. The procedure of the proposed method is illustrated through a robust design of the sheet metal forming process of a side member of an automobile body.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.8
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• pp.99-109
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• 2022

In this study, an electric resistance-heated surface friction spot-welding process was proposed and tested for the spot-welding ability of copper and aluminum dissimilar metal sheets using electric resistance heating and surface friction heating. This process has welding variables, such as the current value, energizing cycles, rotational speed, and friction time. The current value and energizing cycle can affect the resistance heat, and the rotational speed of the rotating pin and friction time influence frictional heat generation. Resistance heating before friction heating has a preheating effect on the Cu-Al contact interface and a positive effect on preventing friction heat loss during the friction stage. However, because resistance preheating can soften the copper sheet and affect the contact stress and friction coefficient, it has difficulties that may adversely affect frictional heat generation. Therefore, the optimal combination of welding variables should be determined through simulations and experiments of the spot-welding process to determine the effects of electric resistance preheating on the suggested process. Through this procedure, it is known that the proposed spot-welding process can improve the welding quality during the spot welding of Cu-Al sheets.