• 한국생산제조학회지
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• 제22권3호
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• pp.349-355
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• 2013

Recently, there is an increasing need to produce more precise products with small deviations from defined target values. Machine capability is the ability of a machine tool to produce parts within a tolerance interval. Capability indices are a statistical way of describing how well a product is machined compared to defined target values and tolerances. Today, there is no standardized way to acquire a machine capability value. This paper describes a method for evaluating machine capability indices in machining centers. After the machining of specimens, the straightness, roundness, and positioning accuracy were measured by using CMM (coordinate measuring machine). These measured values and defined tolerances were used to evaluate the machine capability indices. It will be useful for the industry to have standardized ways to choose and calculate machine capability indices.

• Soft Computing and Machine Intelligence
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• 제1권1호
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• pp.11-23
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• 2021

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

• 펄프종이기술
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• 제45권3호
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• pp.1-8
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• 2013

Hanji has been used mainly for preservation paper because of superior mechanical properties. However, it was not used in printing for inkjet and laser printer-printed letters. In this study, machine-made Hanji was prepared with five different contents of paper mulberry 20, 40, 60, 80 and 100% and managed by same pressure calendering. By increasing of paper mulberry contents, tearing index and folding endurance of machine-made Hanji increased because of increased fiber-to-fiber bonding. Printability property of machine-made Hanji improved by decreasing of paper mulberry contents. After 20 hours accelerated aging, the initial folding endurance of machine-made Hanji was reduced by approximately one-fourth degree. Between 40 and 100% contents of paper mulberry was showed similar levels about preservation property. The machine-made Hanji of paper mulberry 60% content was suitable for permanence and printability properties using preservation paper and printer-printed letters.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 춘계학술대회 논문집
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• pp.435-436
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• 2012

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.185-189
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• 1999

There has been a considerable attention in Invar alloys because of its low thermal expansion property. A low thermal expansion property of Invar alloys, lower than 10-6 near the room temperature, is attractive for precision machine tools. However, the expansion property of Invar alloys is limited below about 520。K, and mechanical properties are relatively low to apply to machine tools. In order to improve mechanical properties in this alloy, Ti alloy element was added to an invar alloy. Microstructure changes and optimum heat-treatment conditions according to Ti addition were discussed in the Ni38-Mo2-Crl-Fe Invar alloy.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.653-658
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• 2000

For testing machine tools, we used test the work by measure of 3-D object that it was made by the machine tool. By reason of these, we have spent to the cost of measure and have delayed supply to user. In this study, we made a macro-program and a application program in order to test automatic the machine tool. So, if you constitute a ring core and a touch prove in machine tool, you should test the property of machine tool.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.120.5-120
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• 2002

1. Introduction 2. Property of FPGA 3. Evolvable Hardware (EHW) 4. Genetic Algorithm Processor (GAP) 5. State Machine 6. Conclusion

• 제어로봇시스템학회논문지
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• 제3권3호
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• pp.305-310
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• 1997

In this paper, a fuzzy set based machine-cell formation algorithm for cellular manufacturing is presented. The fuzzy logic is emoloyed to express the degree of appropriateness when alternative machines are specified to process a part shape. For machine grouping, the similarity coefficient based approach is used. The algorithm produces efficient machine cells and part families which maximize the similarity values.

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권8호
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• pp.886-901
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• 2000

네트웍을 이용한 분산 컴퓨팅 환경이나 소프트웨어의 안전성이 중요한 시스템 등에서, 외부로 부터 전달되는 코드의 안전성을 검증하는 일이 점점 더 중요한 문제가 되고 있다. 전자메일에 첨부되어 온 코드나 웹브라우저를 통해 외부로부터 전달된 코드를 수행하는 일이 일상생활에서 자주 발생하고 있다. 본 논문에서는 기계어 코드의 성질을 검증하는 방법을 제안한다. 코드제공자가 기계어를 만듦과 동시에 그 기계어 코드의 성질을 만들어내어 전달하면, 코드사용자는 전달받은 코드와 그 성질이 부합하는지를 검사하는 것이다. 소스언어의 안전성은 잘 정의된 컴파일러 시스템이 검증해 주지만, 중간언어나 기계어 코드의 성질을 검증하는 잘 정의된 방법은 아직 개발되어있지 않다. 중간언어로 etySECK을 설계하고 이 언어로 작성된 프로그램의 성질을 검증하는 방법을 제안한다. 그리고 타입과 효과분석방식을 사용하여 설계된 시스템의 안전성을 증명한다.

• Geomechanics and Engineering
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• 제37권3호
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.