• 품질경영학회지
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• 제46권1호
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

• Journal of Microbiology and Biotechnology
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• 제21권11호
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• pp.1199-1202
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• 2011

Infection of surgical wounds is a severe problem. Conventional tissue reattachment methods have limits of incomplete sealing and high susceptibility to infection. Medical adhesives have several advantages over traditional tissue reattachment techniques, but still have drawbacks, such as the probability of infection, low adhesive strength, and high cytotoxicity. Recently, a new medical adhesive (new-adhesive) with high adhesive strength and low cytotoxicity, composed of aldehyded dextran and ${\varepsilon}$-poly(L-lysine), was developed. The antimicrobial activity of the new-adhesive was assayed using agar media and porcine skin. In the agar diffusion method, inoculated microorganisms that contacted the new-adhesive were inactivated, but this was not dependent on the amount of new-adhesive. Similar to the agar media results, the topical antimicrobial effect of new-adhesive was confirmed using a porcine skin antimicrobial assay, and the effect was not due to physical blocking based on comparison with the group whose wounds were wrapped.

• 한국정밀공학회지
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• 제33권12호
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• pp.993-998
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• 2016

In today's manufacturing industries, the demand for light non-ferrous materials is considerable due to the need to improve productivity and manufacturability. Since the surface roughness of a material is important for improving the functionality of machined parts, various techniques for surface treatments have been developed to obtain non-ferrous materials with low roughness. A superfinishing method utilizing polishing films is generally applied to the anodized surface of Al7075 in order to improve its roughness. The objective of this research is to determine through experiment the parameters that facilitate the shortest processing time, using a superfinishing method, for reaching a roughness of Ra $0.2{\mu}m$. This objective is met by applying the Taguchi method in the experiments. Through the experiments of superfinishing, the effectiveness of the parameters adopted for the surface treatment is demonstrated.

• Design & Manufacturing
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• 제18권2호
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• 산업경영시스템학회지
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• 제39권1호
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• pp.123-129
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• 2016

In the manufacturing industry fields, thousands of quality characteristics are measured in a day because the systems of process have been automated through the development of computer and improvement of techniques. Also, the process has been monitored in database in real time. Particularly, the data in the design step of the process have contributed to the product that customers have required through getting useful information from the data and reflecting them to the design of product. In this study, first, characteristics and variables affecting to them in the data of the design step of the process were analyzed by decision tree to find out the relation between explanatory and target variables. Second, the tolerance of continuous variables influencing on the target variable primarily was shown by the application of algorithm of decision tree, C4.5. Finally, the target variable, loss, was calculated by a loss function of Taguchi and analyzed. In this paper, the general method that the value of continuous explanatory variables has been used intactly not to be transformed to the discrete value and new method that the value of continuous explanatory variables was divided into 3 categories were compared. As a result, first, the tolerance obtained from the new method was more effective in decreasing the target variable, loss, than general method. In addition, the tolerance levels for the continuous explanatory variables to be chosen of the major variables were calculated. In further research, a systematic method using decision tree of data mining needs to be developed in order to categorize continuous variables under various scenarios of loss function.

• Journal of Information Display
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• 제8권2호
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• pp.10-14
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• 2007

A blazed $GxL^{TM}$ device is described as having high optical efficiency (> 70% for RGB lasers), and high contrast ratio (> 10,000:1), and that is highly reliable when used in a large-area laser projection system. It has a robust design and precise stress control technology to maintain a uniform shape (bow and tilt) of more than 6,000 ribbons, a $0.25-{\mu}m$ CMOS compatible fabrication processing and planarization techniques to reduce fluctuation of the ribbons, and a reliable Al-Cu reflective film that provided protection against a high-power laser. No degradation in characteristics of the GxL device is observed after operating a 5,000- lumen projector for 2,000 hours and conducting 2,000 temperature cycling tests at $-20^{\circ}C$ and $+80^{\circ}C$. At the 2005 World Exposition in Aichi, Japan the world's largest laser projection screen with a size of 2005 inches (10 m ${\times}$ 50 m) and 6 million pixels (1,080 ${\times}$ 5,760) was demonstrated.

• 드라이브 ㆍ 컨트롤
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• 제13권2호
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• pp.34-41
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• 2016

Unlike the commonly used On/Off solenoid, constant attraction force which is independent of plunger displacement is a considerably important characteristic to proportional solenoid of the EPPR Valve. Attraction force uniformity is mainly affected by the internal shape design parameters. Due to a number of shape design parameters, the optimal parameter values are very complex and time consuming to find by trial and error method. Much research has been conducted or are still in progress to find the optimal parameter values by applying various optimization techniques like Genetic Algorithm, Evolution Strategy, Simulated Annealing, or the Taguchi method. In this paper, the design parameters which have primary effects on the attraction force uniformity and the average attraction force are decided by main effects analysis of Design of Experiments. Optimal parameter values are derived using finite-element analysis and a neural network model.

• Steel and Composite Structures
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• 제34권5호
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• pp.681-698
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• 2020

The paper addresses contribution to the modeling and optimization of major machinability parameters (cutting force, surface roughness, and tool wear) in finish dry hard turning (FDHT) for machinability evaluation of hardened AISI grade die steel D₃ with PVD-TiN coated (Al₂O₃-TiCN) mixed ceramic tool insert. The turning trials are performed based on Taguchi's L₁₈ orthogonal array design of experiments for the development of regression model as well as adequate model prediction by considering tool approach angle, nose radius, cutting speed, feed rate, and depth of cut as major machining parameters. The models or correlations are developed by employing multiple regression analysis (MRA). In addition, statistical technique (response surface methodology) followed by computational approaches (genetic algorithm and particle swarm optimization) have been employed for multiple response optimization. Thereafter, the effectiveness of proposed three (RSM, GA, PSO) optimization techniques are evaluated by confirmation test and subsequently the best optimization results have been used for estimation of energy consumption which includes savings of carbon footprint towards green machining and for tool life estimation followed by cost analysis to justify the economic feasibility of PVD-TiN coated Al₂O₃+TiCN mixed ceramic tool in FDHT operation. Finally, estimation of energy savings, economic analysis, and sustainability assessment are performed by employing carbon footprint analysis, Gilbert approach, and Pugh matrix, respectively. Novelty aspects, the present work: (i) contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45-60 HRC, (ii) demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing ceramic tool in hard turning processes considering technological, economical and ecological aspects, which are helpful and efficient from industrial point of view, (iii) provides environment friendliness, cleaner production for machining of hardened steels, (iv) helps to improve the desirable machinability characteristics, and (v) serves as a knowledge for the development of a common language for sustainable manufacturing in both research field and industrial practice.