• Industrial Engineering and Management Systems
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• v.10 no.2
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• pp.128-133
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• 2011

This paper presents a study on product design optimization to reduce the environmental impact of machining. The objective is to analyze the effect of changing the product design parameters such as its dimensions, and basic features on the environmental impact of machining process in terms of its energy consumption, waste produced and the chemicals and other consumables used up during the process. To realize this objective, we used a CAD model of a product with different design scenarios, and analyze their energy consumption using an environmental impact calculator method developed. The waste produced, and the consumables used up, such as lubricants and coolants were analyzed using environmental emission factors. Optimization methods using Genetic Algorithm and Goal Programming are applied to the product design parameters in order to get the best possible product dimensions with the least environmental impact of the machining process.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.12-17
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• 2001

With the advance of the robotic welding process, procedure optimization that selects the welding procedure and predicts bead width that will be deposited is increased. A major concern involving procedure optimization should define a welding procedure that can be shown to be the best with respect to some standard and chosen combination of process parameters, which give an acceptable balance between production rate and the scope of defects for a given situation. This paper presents a new algorithm to establish a mathematical model f3r predicting bead width through a neural network and multiple regression methods, to understand relationships between process parameters and bead width, and to predict process parameters on bead width for GMA welding process. Using a series of robotic arc welding, additional multi-pass butt welds were carried out in order to verify the performance of the neural network estimator and multiple regression methods as well as to select the most suitable model. The results show that not only the proposed models can predict the bead width with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.11
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• pp.1765-1771
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• 2010

In the case of micro.electro-mechanical system (MEMS) devices, the quality of punched via hole is one of the most important factors governing the performance of the device. The common features that affect the laser micromachining of via holes drilled by using Nd:$YVO_4$ laser are described, and efficient optimization methods to measure them are presented. The analysis methods involving an orthogonal array, analysis of variance (ANOVA), and response surface optimization are employed to determine the main effects and to determine the optimal laser process parameters. The significant laser process parameters were identified and their effects on the quality of via holes were studied. Finally, an experiment in which the optimal levels of the laser process parameters were used was carried out to demonstrate the effectiveness of the optimization method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.1
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• pp.114-120
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• 2010

Optimization of the turning process has been concentrated on the selection of the optimal cutting parameters, such as cutting speed, feed rate and depth of cut. However, optimization of the cutting parameters does not necessarily guarantee the maximum profit. For the maximization of the profit, parameters other than cutting parameters have to be taken care of. In this study, 8 price-related parameters were considered to maximize the profit of the product. Regression equations obtained from RSM technique to relate the cutting parameters and maximum cutting volume with a given insert were used. The experiments with four combinations of cutting inserts and material were executed to compare the results that made the profit and cutting volume maximized. The results showed that the cutting parameters for volume and profit maximization were totally different. Contrary to our intuition, global optimization was achieved when the number of inserts change was larger than those for volume maximization. It is attributed to the faster cutting velocity, which decreases processing time and increasing the number of tool used and the total tool changing time.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.2
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• pp.8-15
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• 2008

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. Based on the independence axiom of axiomatic design theory that illustrates the relationship between desired specifications and design parameters, the designs can be classified into three types: uncoupled, decoupled and coupled. To best approach the target performance with the maximum robustness is one of the main functional requirements of a mechanical system. Most engineering designs are pertaining to either coupled or decoupled ones, but these designs cannot currently accomplish a real robustness thus a trade-off between performance and robustness has to be made. In this research, the game theory will be applied to optimize the trade-off.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1809-1814
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• 2003

Differential evolution (DE) algorithm is presented and applied to global optimization in this research. DE suggested initially fur the solution to Chebychev polynomial fitting problem is similar to genetic algorithm(GA) including crossover, mutation and selection process. However, differential evolution algorithm is simpler than GA because it uses a vector concept in populating process. And DE turns out to be converged faster than CA, since it employs the difference information as pseudo-sensitivity In this paper, a trial vector and its control parameters of DE are examined and unconstrained optimization problems of highly nonlinear multimodal functions are demonstrated. To illustrate the efficiency of DE, convergence rates and robustness of global optimization algorithms are compared with those of simple GA.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.123-130
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• 2006

In manufacturing, process automation and parameter optimization are required in order to improve productivity. Especially in welding process, productivity and weldablity should be considered to determine the process parameter. In this paper, optimization methodology was proposed to determine the welding conditions using the objective function in terms of productivity and weldablity. In order to conduct this, welding experiments were carried out. Tensile test was performed to evaluate the weldability. Neural network model to estimate tensile strength using the laser power, welding speed, and wire feed rate was developed. Objective function was defined using the normalized tensile strength which represented the weldablilty and welding speed and wire feed rate which represented the productivity. The optimal welding parameters which maximized the objective function were determined.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1338-1343
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• 2007

High-efficient machining, which means cutting a part in the least amount of time, is the most effective tool to improve productivity. In this study, a new feed optimization method based on the cutting power regulation was proposed to realize the high-efficient machining in turning process. The cutting area was evaluated by using the Boolean intersection operation between the cutting tool and workpiece. And the cutting force and power were predicted from the cutting parameters such as feed, depth of cut, spindle speed, specific cutting force, and so on. Especially, the reliability of the proposed optimization method was validated by comparing the predicted and measured cutting forces. The simulation results showed that the proposed optimization method could effectively enhance the productivity in turning process.

• Korean Chemical Engineering Research
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• v.46 no.5
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• pp.903-914
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• 2008

This study was undertaken for the production capacity expansion and energy saving through entire process simulation and optimization for the commercial process of manufacturing monoethylene glycol as a staple from ethylene oxide. Aspen $Plus^{TM}$(ver. 2006) was employed in the simulation and optimization work. The multicomponent vapor-liquid equilibria involved in the process were calculated using the NRTL-RK equation. As for the binary interaction parameters required for a total of 91 binary systems, those for 8 systems were self-supplied by the simulator, those for 28 systems were estimated through regression of the VLE data in the literature, and the remainder were estimated with the estimation system built in the simulator. Subsequent to ascertaining the accuracy of the generated parameters through comparison between actual and simulated process data, sensitive variables highly affecting the process were searched and selected using sensitivity analysis tool in the simulator. The optimum operating conditions minimizing the total heat duty of the process were investigated using the optimization tool based on the successive quadratic programming in the simulator.