• International Journal of Precision Engineering and Manufacturing
• /
• 제9권2호
• /
• pp.54-62
• /
• 2008

This investigation deals with machining AISI 52100 steel using a carbide-coated tool. The machining cutting force and tool tip temperature are measured online while turning using different cutting parameters. The surface roughness is also measured, but off-line after each cut. The obtained data are analyzed and the influence of the cutting parameters on the machining variables is determined in the form of plots. Regression models obtained from the results are tested using additional experimental data.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2000년도 추계학술대회 논문집
• /
• pp.918-921
• /
• 2000

Micro-machining, one of the non-traditional machining techniques, can achieve a wanted shape of the surface using metal dissolution with electrochemical reaction and can be applied to the metal such as high tension, heat resistance and hardened steel. The workpiece dissolves when it is positioned close to the tool electrode in electrolyte and the current is applied. Traditional machining has been used in the industries such as cutting, deburring, drilling and shaping. The aim of this work is to develop Micro-machining techniques for micro shape by establishing appropriate machining parameters of micro-machining

• 한국정밀공학회지
• /
• 제20권12호
• /
• pp.34-41
• /
• 2003

This paper suggests a methodology fur improving the machining accuracy by compensating for the machining errors based on on-machine measurement process. Probing errors and machine tool errors included in the measurement data were calibrated or compensated to obtain the actual machining errors. Machine tool errors were modeled in forward and backward directions according to the axis movement direction to consider the effects of backlash errors on the measurement data, and model parameters were determined by measuring a cube array artifact. A rectangular workpiece was machined and then measured with a touch probe as a verification experiment. Machining experiments showed that the machining errors were reduced to within the designated tolerance after compensating for the actual machining errors by modifying the original footpath for the next-step machining.

• 한국CDE학회논문집
• /
• 제8권4호
• /
• pp.254-261
• /
• 2003

In mold and die shops, NC machining process mainly affects the quality of the machined surface and the manufacturing time of molds and dies. The estimation of NC machining time is a prerequisite to measure the machining productivity and to generate a process schedule, which generally includes the process sequence and the completion time of each process. It is required to take into account dynamic characteristics in the estimation, such as the ac/deceleration of NC machine controllers. Intensive observations at start and end points of NC blocks show that a minimum feedrate, a key variable in a machining time model, has a close relation to a block distance, an angle between blocks, and a command feedrate. Thus, this study addresses regression models for the minimum feedrate estimation on short and long NC blocks considering these parameters. Furthermore, machining time estimation models by the four types of feedrate behaviors are suggested based on the estimated minimum feedrate. To show the validity of the proposed machining time model, the study compares the estimated with the actual machining time in the sculptured surface machining of several mold dies.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1997년도 추계학술대회 논문집
• /
• pp.145-149
• /
• 1997

STEP AP224 includes the information of machining feature and tolerances. Machining features are machined from raw material. Tolerance constrain feasible methods of manufacture, strongly influence the cost of manufacture. And tolerances influence the machining process. We need to decide the precedence between features .tool radius and tool direction for minimum tool changes. This paper deals with the method of decision of precedence between features and process parameters using feature information and tolerances in STEP AP224.

• 대한기계학회논문집
• /
• 제16권7호
• /
• pp.1223-1233
• /
• 1992

본 연구에서는 일반화된 델타규칙을 이용한 신경회로망 기법을 사용하여 절삭 계수의 수학적인 모형화를 수행하였고, 탄소강 공작물 및 고속도강 공구에 대하여 절 삭속도의 추정을 통하여 절삭성을 예측할 수 있는 방법론을 산삭작업에서 개발하였다. 그결과 방대한 양의 절삭계수를 저장할 필요가 없을 뿐만 아니라, 작업자의 경험에 따 른 절삭계수의 선정으로 인하여 발생할 수 있는 단점을 극복하고 유연한 절삭계수의 선정을 할 수 있게 하므로써 적응제어 기능을 갖는 수치제어장치의 개발에 응용할 수 있음을 밝혔다.

• 한국안전학회지
• /
• 제24권1호
• /
• pp.1-6
• /
• 2009

In machining of ceramic materials, they are very difficult-to cut materials because of there high strength and hardness. Machining of ceramics are characterized by cracking and brittle fracture. Generally, ceramics are machined using conventional method such as grinding and polishing. However these processes are generally costly and have low MRR(material removal rate). This paper focuses on machinability evaluation of machinable ceramics for products with CNC machining center. Thus, in this paper, experiment applying cutting parameters is performed based on experimental design method. A design and analysis of experiments is conducted to study the effects of these parameters on the surface roughness by using the S/N ratio, analysis of ANOVA, and F-test. And multiple linear regression analysis is applied to compare experimental with predicted data in consideration of surface roughness. Cutting parameters, namely, feed, cutting speed and depth of cut are used to accomplish purpose of this paper. Required experiments are performed, and the results are investigated.

• 소성∙가공
• /
• 제29권3호
• /
• pp.144-150
• /
• 2020

In this paper, we propose the generation of a double enveloping worm thread profile with a non-developable ruled surface. Thread surface machining cuts all the way from the tip to the tooth root at one time, like full-face contact machining, rather than cutting several times like point machining. This cutting can reduce the cutting duration and achieve the smooth surface that does not require a grinding process for the threaded surface. The mathematical model of the cutting process was developed from theoretical equations, and the tooth surface was generated using two parameters and modeled in the CATIA using the generated Excel data. Additionally, the machining process of the worm was simulated in a numerical control simulation system. To verify the validity of the proposed method, the deviation between the modeling and the workpiece was measured using a 3D measuring machine.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2001년도 춘계학술대회 논문집(한국공작기계학회)
• /
• pp.376-381
• /
• 2001

This paper presents a methodology of machining error compensation by using Artificial Neural Network(ANN) model based on the inspection database of On-Machine-Measurement(OMM) system. First, the geometric errors of the machining center and the probing errors are significantly reduced through compensation processes. Then, we acquire machining error distributions from a specimen workpiece. In order to efficiently analyze the machining errors, we define two characteristic machining error parameters. These can be modeled by using an ANN model, which allows us to determine the machining errors in the domain of considered cutting conditions. Based on this ANN model, we try to correct the tool path in order to effectively reduce the errors by using an iterative algorithm. The iterative algorithm allows us to integrate changes of the cutting conditions according to the corrected tool path. Experimentation is carried out in order to validate the approaches proposed in this paper.

• 한국공작기계학회논문집
• /
• 제15권4호
• /
• pp.92-97
• /
• 2006

This paper presents an integrated machining error compensation method based on PNN(Polynomial Neural Network) approach and inspection database of OMM(On-Machine-Measurement) system. To efficiently analyze the machining errors, two machining error parameters are defined and modeled using the PNN approach, which is used to determine machining errors for the considered cutting conditions. Experiments are carried out to validate the approaches proposed in this paper. In result, the proposed methods can be effectively implemented in a real machining situation, producing much fewer errors.