• 제어로봇시스템학회논문지
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• 제15권7호
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• pp.728-733
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• 2009

This paper presents the compensation of mechanical deflection error in SCARA robot. End of robot gripper is deflected by weight of arm and pay-load. If end of robot gripper is deflected constantly regardless of robot configuration, it is not necessary to consider above mechanical deflection error. However, deflection in end of gripper varies because that moment of each axis varies when robot moves, it affects the relative accuracy. I propose the compensation method of deflection error using neural network. FEM analysis to obtain the deflection of gripper end was carried out on various joint angle, the results is used in neural network teaming. The result by simulation showed that maximum relative accuracy reduced maximum 9.48% on a given working area.

• 한국기계가공학회지
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• 제3권1호
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• pp.7-14
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• 2004

Machining error is defined the normal distance between designed surface and actual tool path with tool deflection. This is inevitably caused by the tool deflection, tool wear, thermal effect and machine tool errors and so on. Among these factors, tool deflection is usually known as the most significant factor of machining error. Tool deflection problem is analyzed using Instantaneous horizontal cutting forces. The high quality and precision of machining products are required in finishing. In order to achieve these purposes, it is necessary work that decrease the machining error. This paper presents a study on the machining error caused by the tool deflection in ball end milling of 2 dimensional surface. Tool deflection model and simple machining error prediction model are described. This model is checked the validity with machining experiments of 2 dimensional surface. These results may be used to decrease machining error and tool path decision.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.1003-1008
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• 1997

In this paper, the prediction of cutting force and tool deflection in the ball-end milling process are studied. Identifying various cutting region using Z-map, cutting force in the ball-end milling process can be predicted. Cutting force deflects the tool and the tool deflection changes the cutting force. Tool deflection is included in the cutting force prediction. Tool deflecition also causes machining error of the machined surface. A series of experiments were performed to verify the simulated cutting force and machining error.

• 한국생산제조학회지
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• 제19권4호
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• pp.498-503
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• 2010

The main purpose of this study strongly concerned micro machining error estimation by using FEM analysis of tool deflection shapes in micro flat end-milling process. For the precision micro flat end-milling process, analysis of micro cutting errors is mandatory. In general, tool deflection is a major factor which causes cutting error and limits realization of the high-precision cutting process. Especially, in micro end-milling process, micro tool deflection generates very serious problems in contrast to macro tool deflection. Methods which deal with compensation of cutting error by tool deflection in macro end-milling process have been studied plentifully but, few researches transact with micro scaled cutting tool deflection in micro cutting process. Therefore, the trend of micro tool deflection was estimated by using FEM analysis in this paper. Cutting forces were acquired by micro dynamometer and these were utilized in FEM analysis. In order to verify FEM analysis results, micro machining processes were carried out and real machined profiles were compared with FEM results. Finally through the proposed approach well suited FEM results were obtained.

• 한국산업융합학회 논문집
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• 제5권4호
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• pp.373-378
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• 2002

This paper presents a method to predict tool deflection induced by the thermal distribution and the cutting force using FEM in milling operation. The thermal distribution of cutting tool was predicted using FEM after measuring the temperature of the end of tool and of the tool holder. The thermal deflection of cutting tool was predicted using FEM as well. The tool deflection induced by the cutting force was analyzed with the solid model of cutting tool. An end mill tool caused most of tool deflection comparing to tool holder. Most of thermal deflection came from Z-direction and most of tool deflection induced by the cutting force came from X and Y direction. Precision cutting will be accomplished when tool locations are generated considering the thermal deflection of cutting tool and the tool deflection induced by the cutting force in CAD/CAM.

• 한국정밀공학회지
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• 제21권6호
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• pp.43-51
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• 2004

A method for form error prediction in side wall machining with a flat end mill is suggested. Form error is predicted directly from the tool deflection without surface generation by cutting edge locus with time simulation. Developed model can predict the surface form error about three hundred times faster than the previous method. Cutting forces and tool deflection are calculated considering tool geometry, tool setting error and machine tool stiffness. The characteristics and the difference of generated surface shape in up milling and down milling are discussed. The usefulness of the presented method is verified from a set of experiments under various cutting conditions generally used in die and mold manufacturing. This study contributes to real time surface shape estimation and cutting process planning for the improvement of form accuracy.

• 소성∙가공
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• 제16권1호
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• pp.15-19
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• 2007

This paper presents an integrated machining error compensation method based on captured images of tool deflection shapes in flat end-milling processes. This approach allows us to avoid modeling machining characteristics (cutting forces, tool deflections and machining errors etc.) and accumulating calculation errors induced by several simulations. For this, a high-speed camera captured images of real deformed tool shapes which were cutting under given machining conditions. Using image processes and a machining error model, it is possible to estimate tool deflection in cutting conditions modeled and to compensate for machining errors using an iterative algorithm correcting tool paths. This corrected tool path can effectively reduce machining errors in the flat end-milling process. Experiments are carried out to validate the approaches proposed in this paper. The proposed error compensation method can be effectively implemented in a real machining situation, producing much smaller errors.

• 대한기계학회논문집A
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• 제37권7호
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• pp.833-839
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• 2013

본 논문에서는 광섬유 브래그 격자 센서를 이용한 외팔보 형태의 복합재 구조물 2차원 형상 모니터링 연구를 수행하였다. 복합재 구조물의 형상을 모니터링하기 위해 NASA에서 개발한 처짐 방정식과 FBG 센서가 부착된 복합재 보를 이용하였으며, 처짐에 따른 복합재 보의 형상을 추정하고 동시에 실제 처짐과 측정된 처짐 크기를 비교하였다. 실험 결과 보의 형상을 성공적으로 추정할 수 있었으나 실제 처짐과 측정된 처짐이 오차가 발생하였다. 하지만 이러한 오차가 선형 관계를 갖고 있어 이를 정량화하여 보정한 후 재 실험한 결과 실제 처짐과 동일한 처짐 크기를 구할 수 있었다. 결과적으로 오차 보상을 잘 활용한다면 외팔보 구조물의 형상 모니터링을 위해 FBG 센서와 처침 방정식이 매우 유용하다는 점을 확인할 수 있었다.

• 한국기계기술학회지
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• 제13권3호
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• pp.7-16
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• 2011

This study proposes the compensation method for the mechanical deflection error of a SCARA robot. While most studies on the related subject have dealt with the development of a control algorithm for improvement of robot accuracy, this study presents the control method reflecting the mechanical deflection error which is predicted in advance. The deflection at the end of the gripper of SCARA robot is caused by the self-weights and payloads of Arm 1, Arm 2 and quill. If the deflection is constant even though robot's posture and payload vary, there may not be a big problem on robot accuracy because repetitive accuracy, that is relative accuracy, is more important than absolute accuracy in robot. The deflection in the end of the gripper varies as robot's posture and payload change. That's why the moments $M_x$, $M_y$ and $M_z$ working on every joint of a robot vary with robot's posture and payload size. This study suggests the compensation method which predicts the deflection in advance with the variations in robot's posture and payload using neural network. To do this, I chose the posture of robot and the payloads at random, found the deflections by the FEM analysis, and then on the basis of this data, made compensation possible by predicting deflections in advance successively with the variations in robot's posture and payload through neural network learning.

• 대한기계학회논문집A
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• 제21권2호
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• pp.189-198
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• 1997

This paper presents development of a practical tool deflection compensation system in order to reduce the machining error from the tool deflection compensation system in order to reduce the machining error from the tool deflection in the end-milling process. The devised system is a tool adapter which includes 1-axes force sensor for detecting tool deflection and 2-axes tool tilting device for adjusting tool position through computer interface on line process. Experimental in investigations for typical shaped workpieces representing various end milling situations are performed to verify the ability of the system to suppress the surface errors due to tool deflections. With the system, it is possible to get precise machining surface without any excessive machining error due to increased cutting force in more productive machining conditions.