• Title/Summary/Keyword: Rotational Machine

검색결과 237건 처리시간 0.027초

현장상황을 고려한 회전현상 부품의 공정계획 시스템 구축과 운영에 관한 연구 (A Study on Computer Aided Process Planning System for Rotational Parts Considering Shop Floor Status)

  • 전성범;박남규;신기태;김기동;박진우
    • 산업공학
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    • 제7권3호
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    • pp.125-135
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    • 1994
  • This thesis reports the development of a Computer-Aided Process Planning system for rotational parts. The developed system ultimately generates process plans for rotational parts through a knowledge-base. The knowledge-base and decision-making algorithms are represented by Pascal computer programming language. We have developed a process planning system which adjusts the sequence of processes by itself to ensure the quality of the parts. This system generates more detailed job sequence and descriptions than other well-known process planning systems. We present realistic and efficient process plans through the integration of process planning and scheduling. This system optimizes flow time of parts by decreasing the number of machine set-ups.

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만드렐이 없는 CNC Spinning 기술개발에 관한 연구 (A study on the development of CNC spinning technology without mandrel)

  • 이춘만;허태목
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 2002년도 춘계학술대회 논문집
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    • pp.620-623
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    • 2002
  • Spinning has been used widely for the manufacture of hollow parts with rotational symmetry. With developing CNC machine, CNC machine center can be applied to the spinning processes. In this paper, a study on the development of CNC spinning technology without mandrel is carried out. The deforming process of the spinning process was simulated by DEFORM 3D to give basic design data.

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Influence of the Welding Speeds and Changing the Tool Pin Profiles on the Friction Stir Welded AA5083-O Joints

  • El-Sayed, M.M.;Shash, A.Y.;Abd Rabou, M.
    • Journal of Welding and Joining
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    • 제35권3호
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    • pp.44-51
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    • 2017
  • In the present study, AA 5083-O plates are joined by friction stir welding technique. A universal milling machine was used to perform the welding process of the work-pieces which were fixed on the proper position by a vice. The joints were friction stir welded by two tools with different pin profiles; cylindrical threaded pin and tapered smooth one at different rotational speed values; 400 rpm and 630 rpm, and different welding speed values; 100 mm/min and 160 mm/min. During FSW of each joint, the temperature was measured by infra-red thermal image camera. The welded joints were inspected by visually as well as by the macro- and microstructure evolutions. Furthermore, the joints were tested for measuring the hardness and the tensile strength to study the effect of changing the FSW parameters on the mechanical properties. The results show that increasing the rotational speed results in increasing the peak temperature, while increasing the welding speed results in decreasing the peak temperature for the same tool pin profile. Defect free welds were obtained at lower rotational speed by the threaded tool profile. Moreover, the threaded tool pin profile gives superior mechanical properties at lower rotational speed.

Shield TBM disc cutter replacement and wear rate prediction using machine learning techniques

  • Kim, Yunhee;Hong, Jiyeon;Shin, Jaewoo;Kim, Bumjoo
    • Geomechanics and Engineering
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    • 제29권3호
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    • pp.249-258
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    • 2022
  • A disc cutter is an excavation tool on a tunnel boring machine (TBM) cutterhead; it crushes and cuts rock mass while the machine excavates using the cutterhead's rotational movement. Disc cutter wear occurs naturally. Thus, along with the management of downtime and excavation efficiency, abrasioned disc cutters need to be replaced at the proper time; otherwise, the construction period could be delayed and the cost could increase. The most common prediction models for TBM performance and for the disc cutter lifetime have been proposed by the Colorado School of Mines and Norwegian University of Science and Technology. However, design parameters of existing models do not well correspond to the field values when a TBM encounters complex and difficult ground conditions in the field. Thus, this study proposes a series of machine learning models to predict the disc cutter lifetime of a shield TBM using the excavation (machine) data during operation which is response to the rock mass. This study utilizes five different machine learning techniques: four types of classification models (i.e., K-Nearest Neighbors (KNN), Support Vector Machine, Decision Tree, and Staking Ensemble Model) and one artificial neural network (ANN) model. The KNN model was found to be the best model among the four classification models, affording the highest recall of 81%. The ANN model also predicted the wear rate of disc cutters reasonably well.

퍼지 적용 PID제어를 이용한 오일쿨러 시스템의 온도제어 (Temperature Control for an Oil Cooler System Using PID Control with Fuzzy Logic)

  • 김순철;홍대선;정원지
    • 한국공작기계학회논문집
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    • 제13권4호
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    • pp.87-94
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    • 2004
  • Recently, technical trend in machine tools is focused on enhancing of speed, accuracy and reliability. The high speed usually results in thermal displacement and structural deformation. To minimize the thermal effect, precision machine tools adopt a high precision cooling system. This study proposes a temperature control for an oil cooler system using Pill control with fuzzy logic. In the cooler system, refrigerant flow rate is controlled by rotational speed of a compressor, and outlet oil temperature is selected as the control variable. The fuzzy control rules iteratively correct PID parameters to minimize the error and difference between the outlet temperature and the reference temperature. Here, ambient temperature is used as the reference one. To show the effectiveness of the proposed method, a series of experiments are conducted for an oil cooler system of machine tools, and the results are compared with the ones of a conventional Pill control. The experimental results show that the proposed method has advantages of faster response and smaller overshoot.

물류 회전설비 고장예지 시스템 (A Fault Prognostic System for the Logistics Rotational Equipment)

  • 김수형;볘르드바에브 예르갈리;조형기;김규익;김진석
    • 산업경영시스템학회지
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    • 제46권2호
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    • pp.168-175
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    • 2023
  • In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

회전수가 변하는 기기의 고장진단에 있어서 특성 기반 분류와 합성곱 기반 알고리즘의 예측 정확도 비교 (Comparison of Prediction Accuracy Between Classification and Convolution Algorithm in Fault Diagnosis of Rotatory Machines at Varying Speed)

  • 문기영;김형진;황세윤;이장현
    • 한국항해항만학회지
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    • 제46권3호
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    • pp.280-288
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    • 2022
  • 본 연구는 정상 가동 중에도 회전수가 변하는 기기의 이상 및 고장 진단 방안을 다루고 있다. 회전수가 변함에 따라 비정상적 시계열 특성을 내포한 센서 데이터에 기계학습을 적용할 수 있는 절차를 제시하고자 하였다. 기계학습으로는 k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Random Forest을 사용하여 이상 및 고장 진단을 수행하였다. 또한 진단 정확성을 비교할 목적으로 이상 감지에 오토인코더, 고장진단에는 합성곱 기반의 Conv1D도 추가로 이용하였다. 비정상적 시계열로부터 통계 및 주파수 속성으로 구성된 시계열 특징 벡터를 추출하고, 추출된 특징 벡터에 정규화 및 차원 축소 기법을 적용하였다. 특징 벡터의 선택과 정규화, 차원 축소 여부에 따라 달라지는 기계학습의 진단 정확도를 비교하였다. 또한, 적용된 학습 알고리즘 별로 초매개변수 최적화 과정과 적층 구조를 설명하였다. 최종적으로 기존의 심층학습과 비교하여, 기계학습도 가변 회전기기의 고장을 정확하게 진단할 수 있는 절차를 제시하였다.

공작기계 볼트결합부의 전산모델링 (Computational Modeling of Bolt Joint for Machine Tools)

  • 이재학;하태호;이찬홍
    • 한국정밀공학회지
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    • 제29권10호
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    • pp.1070-1077
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    • 2012
  • Virtual machine tools have been magnified recently as manufacturers could estimate performances of machine tools before design and manufacturing of them. However, it requires much time and efforts to make FEM models and predict precision of machine tools well because machine tools are composed of many joints such as bolt joints, LM joints, rotational bearing joints and mounts. Especially, we have studied computational modeling methods of bolt joints to predict precision of machine tools well in this paper. Stiffness and damping coefficients of bolt joints are investigated and generalized with respect to fasten forces through experiments and FEM analysis. Matrix 27 element of ANSYS is used and bolt joints are simplified as square areas with 8 nodes to apply stiffness and damping simultaneously. Additionally, coordinate transformation of matrix 27 for bolt joints is induced to apply to skewed bolt joints of machine tools and evaluate it using FEM analysis.