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김진영,조형석,Kim, Jin-Young,Cho, Hyung-Suck 한국광학회 2002 한국광학회지 Vol.13 No.4
유연부품은 조립 시에 변형이 발생하기 때문에 로봇을 이용한 자동조립에서 이의 성공적인 조립을 위해서는 부품변형 및 상대오차를 효과적으로 측정할 수 있는 방법이 필요하다. 이에 본 논문에서는 시각센서를 이용하여 상대오차 및 부품변형을 측정할 수 있는 3차원 측정시스템의 설계방법을 제안한다. 시스템의 광로해석을 통해 실제 작업공간과 카메라 영상면 사이의 사상관계를 해석하고, 이를 토대로 시스템의 설계방법을 제안한다. 또한 구현된 실제 시스템을 이용한 조립실험을 통해 제안된 시스템의 유효성을 검증한다. Unlike rigid parts, flexible parts can be deformed by contact force during assembly. In robotic assembly, information about their deformation as well as possible misalignment between the holes and their respective mating parts is essential for successful assembly. This paper presents a method to design a visual sensing system for measuring parts deformation and misalignment in flexible parts assembly. This paper performs ray-trace analysis of the system. A series of experiments for flexible parts assembly by using the implemented system are performed.
자동조립에서 시뮬레이트 어닐링을 이용한 조립순서 최적화
홍대선,조형석,Hong, Dae-Sun,Cho, Hyung-Suck 대한기계학회 1996 大韓機械學會論文集A Vol.20 No.1
An assembly sequence is considered to be optimal when is minimizes assembly cost while satisfying assembly constraints. To derive such an optimal sequence for robotic assembly, this paper proposes a method using a simulated annealing algorithm. In this method, an energy funciton is derived inconsideration of both the assembly constraints and the assembly cost. The energy function thus derived is iteratively minimized until no further change in energy occurs. During the minimization, the energy is occationally perturbed probabilistically in order to escape from local minima. The minimized energy yields an optimal assembly sequence. To show the effectiveness of the proposed method, case studies are presented for industrial products such as an electrical relay and an automobil alternator. The performance is analyzed by comparing the results with those of a neural network-based method, based upon the optimal solutions of an expert system.
레이져 표면 경화 공정에서 신경회로망을 이용한 경화층 깊이 예측
우현구,조형석,한유희,Woo, Hyun Gu,Cho, Hyung Suck,Han, You Hie 한국정밀공학회 1995 한국정밀공학회지 Vol.12 No.11
In the laser surface hardening process the geometrical parameters, especially the depth, of the hardened layer are utilized to assess the integrity of the hardening layer quality. Monitoring of this geometrical parameter ofr on-line process control as well as for on-line quality evaluation, however, is an extremely difficult problem because the hardening layer is formed beneath a material surface. Moreover, the uncertainties in monitoring the depth can be raised by the inevitable use of a surface coating to enhance the processing efficiency and the insufficient knowledge on the effects of coating materials and its thicknesses. The paper describes the extimation results using neural network to estimate the hardening layer depth from measured surface temperanture and process variables (laser beam power and feeding velocity) under various situations. To evaluate the effec- tiveness of the measured temperature in estimating the harding layer depth, estimation was performed with or without temperature informations. Also to investigate the effects of coating thickness variations in the real industry situations, in which the coating thickness cannot be controlled uniform with good precision, estimation was done over only uniformly coated specimen or various thickness-coated specimens. A series of hardening experiments were performed to find the relationships between the hardening layer depth, temperature and process variables. The estimation results show the temperature informations greatly improve the estimation accuracy over various thickness-coated specimens.