http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Effects of Microstructure on Ultraprecision Machining and Grinding
Steven Y. Liang,Siva Venkatachalam,Hyung Wook Park 한국생산제조학회 2012 한국생산제조시스템학회 학술발표대회 논문집 Vol.2012 No.10
The roles of crystallography in the integrity of the parts produced in ultraprecision machining and grinding processes have been quantitatively studied. In ultraprecision machining and grinding the tool geometry features and the process parameters are on the same order of magnitude as the grain size; therefore, the material microstructural attributes in the context of grain size, grain boundaries, crystallographic orientation, etc. can be important. For machining, the grain size, having an effect on the material yield stress via the Hall-Petch model, is represented by a log-normal distribution while the effects of grain boundary and orientation have been modeled using the dislocation principles. For grinding, combined consideration of mechanical and thermal effects within a single grit interaction model of material removal in coupling with the topography and material crystallographic effects has been examined to predict the grinding forces. This study aims to provide a clear understanding of the crystallographic effects to allow for the ultimate goals of prediction, planning, optimization, and control of ultraprecision material removal and finishing processes.
박형욱,Steven Y. Liang,박종권 한국공작기계학회 2008 한국공작기계학회 춘계학술대회논문집 Vol.2008 No.-
In this study, a systematic design scheme was developed to reduce a subjective design of micro/mesoscale machine tools. Based on this developed method, the optimization of the size of micro/mesoscale machine tools was performed. These computations include individual mathematical modeling of key parameters such as volumetric error, machine working space, and static, thermal, and dynamic stiffness. In case of dynamic stiffness, it was supplemented with a hammer impact test to identify the dynamic characteristics of the joints.
Prediction model of the surface roughness of micro-milling single crystal copper
Xiaohong Lu,Liang Xue,Feixiang Ruan,Kun Yang,Steven Y. Liang 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.11
Presently, the demand for single crystal copper micro-components is increasing in various fields because single crystal copper has good electrical conductivity. Micro-milling technology is an effective processing technology for small single crystal copper parts. Surface roughness is a key performance indicator for micro-milling single crystal copper. Establishing a surface roughness prediction model with high precision is useful to guarantee the processing quality by selecting the cutting parameters for micro-milling. Few studies have currently focused on micro-milling single crystal copper. In this study, the orthogonal experiments of micro-milling single crystal copper were conducted, and the influences of the spindle and feed speeds and axial depth of cut on the surface roughness of micro-milled single crystal copper with different orientations were analyzed by range analyses. The spindle rotation speed was the major affecting factor. The surface roughness of single crystal copper in different crystal orientations was predicted by using the SVM method. Experimental results showed that the average relative error of the surface roughness of <100>, <110>, and <111> crystal orientation single crystal copper was 2.7 %, 3.3 %, and 2.2 %, respectively, and that the maximum relative errors were 7.0 %. 10.1 %, and 3.1 %, respectively. The uncertainty analysis was conducted by using the Monte Carlo method to verify the reliability of the built surface roughness model.
Man Zhao,Xia Jinan,Steven Y. Liang 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.11
This paper proposes a physical-based model to predict the temperature in the micro-grinding of maraging steel 3J33b with the consideration of material microstructure and process parameters. In micro-grinding, the effects of crystallography on the grinding machinability become significant, since the depth of cut is of the same order as the grain size. In this research, the Taylor factor model for multi-phase materials is proposed to quantify the crystallographic orientation (CO) with respect to the cutting direction by examining the number and type of activated slip systems. Then, the flow stress model is developed, in which both the athermal stress resulted from the COs and the strain induced by the phase transformation are taken into account. On the basis of the flow stress model, the grinding forces are predicted followed by the calculation of the grinding heat. In the investigation, the triangular heat flux distribution and the reported energy partition model are applied in the calculation of workpiece temperature. Furthermore, the temperature model is validated by conducting an orthogonal-designed experiment, with the predictions of the maximum temperature in good agreement with the experimental data. Moreover, the predictive data is compared with the predictions resulted from the two other previously reported models. The results indicate that the proposed temperature model with considering the effect of CO and the phase transformation improved the prediction accuracy of the micro-grinding temperature.
Tool Wear and Chatter Detection Based on Multiple Time Series Model Using Neural Network Analysis
Chung, Eui-Sik,Liang, Steven Y. 한밭대학교 산업과학기술연구소 1993 논문집 Vol.1 No.-
The progressive wear of cutting tools and the occurrence of chatter vibration often pose limiting factors on the achievable productivity in machining processes. An effective in-process monitoring system for tool wear and chatter therefore offers the unique advangtage of relaxing the process parameter constraints and optimizing the machining production rate. One challenging aspect involved in the monitoring and differentiation of these two phenomena is that the individual effect of tool wear and chatter vibration on the fundamental machining mechanics is usually rather subtle and sophisticated. This paper discusses the application of acoustic emission and tool acceleration measurements to the in-process assessment of machining quality in the contex of tool wear and chatter. This study utilizes the adaptive time series modeling of each measurement and the cutting condition classification based on artificial neural network analysis with multiple inputs of time series model parameters. The methodology and implementation principles are discussed along with the presentation of results from a series of turning experiments.
In-Situ Distortion Prediction in Metal Additive Manufacturing Considering Boundary Conditions
Wenjia Wang,Jinqiang Ning,Steven Y. Liang 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.22 No.5
Undesired distortion often occurs in metal additive manufacturing due to the high temperature gradient resulting from repeated thermal cycles. A good understanding and fast predictions of in-situ distortion are essential to achieve high dimensional accuracy and prevent delamination or failure of build parts. Experimental investigations and numerical methods have been employed to study the in-situ distortion. However, the complex measurement systems and high computational cost limit their applications. An analytical modeling method with closed-form solutions is proposed in this paper to predict the in-situ distortion of laser cladding process without using iteration-based numerical calculations. The effects of build edges and geometry are considered, which include thermal convection and radiation at boundaries. Heat input and heat sink solutions modified from the point moving heat source model are added together to predict the temperature profile of the build and substrate. The die-substrate assembly model is used to calculate the deflection during the manufacturing process. Alloy 625 is selected to test the predictive accuracy and computational efficiency of the presented analytical model. The predicted results are close to the experimental data of in-situ distortion in literature. The computational time is less than 30 s. The good predictive accuracy and low computational cost make the presented method a promising approach to study the full-field temperature and distortion of a geometrically complex part.