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

First-and-Every-Print-Correct Metal Additive Manufacturing

Steven Y. Liang 한국생산제조학회 2022 한국생산제조시스템학회 학술발표대회 논문집 Vol.2022 No.5

Improving Daily Total Complete Shipment by Using System Dynamics Simulation in an Indonesian Car Spare Parts Manufacturer

Steven Liang,Aditya Tirta Pratama,Sumarsono Sudarto 대한산업공학회 2022 Industrial Engineeering & Management Systems Vol.21 No.1

PT. Nusahadi Citraharmonis is a leading manufacturing company in Indonesia that produces spare parts for various automotive industries. The company is an active supplier for several well-known car manufacturer in Indonesia, which are: NTC, HMMI, TMMIN, and ASKA. However, the company is currently having difficulty in fulfilling all customer demand on time. Thus, creating backlogged order in the system and decreasing the daily total complete shipment. The objective of this research is to redesign new inventory level to increase the daily total complete shipment. To solve the problem, Define-Measure-Analyze-Improve-Control (DMAIC) is used as the procedure, cause effect analysis is used as a tool to identify the key factors affecting daily total complete shipment, and system dynamics simulation is used as a tool to redesign inventory level. From the result of system dynamics simulation, the newly redesigned inventory level is able to decrease the backlogged order in the system and increase the daily total complete shipment. Besides the system dynamics simulation, standard operating procedure for operating the system is also made and given to the company for future use.

초소형 가공 기 구조물 사이즈 최적 설계에 관한 연구

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.

Emission Projections for Long-Haul Freight Trucks and Rail in the United States through 2050

Liu, Liang,Hwang, Taesung,Lee, Sungwon,Ouyang, Yanfeng,Lee, Bumsoo,Smith, Steven J.,Yan, Fang,Daenzer, Kathryn,Bond, Tami C. American Chemical Society 2015 Environmental science & technology Vol.49 No.19

<P>This work develops an integrated model approach for estimating emissions from long-haul freight truck and rail transport in the United States between 2010 and 2050. We connect models of macroeconomic activity, freight demand by commodity, transportation networks, and emission technology to represent different pathways of future freight emissions. Emissions of particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and total hydrocarbon (THC) decrease by 60%-70% from 2010 to 2030, as older vehicles built to less-stringent emission standards retire. Climate policy, in the form of carbon tax that increases apparent fuel prices, causes a shift from truck to rail, resulting in a 30% reduction in fuel consumption and a 10%-28% reduction in pollutant emissions by 2050, if rail capacity is sufficient. Eliminating high-emitting conditions in the truck fleet affects air pollutants by 20% to 65%; although these estimates are highly uncertain, they indicate the importance of durability in vehicle engines and emission control systems. Future infrastructure investment will be required both to meet transport demand and to enable actions that reduce emissions of air and climate pollutants. By driving the integrated model framework with two macroeconomic scenarios, we show that the effect of carbon tax on air pollution is robust regardless of growth levels.</P>

Intravenous lidocaine infusions for 48 hours in open colorectal surgery: a prospective, randomized, double-blinded, placebo-controlled trial

Matthew Liang Jinn Ho,Stephen John Kerr,Jennifer Stevens 대한마취통증의학회 2018 Korean Journal of Anesthesiology Vol.71 No.1

Background: Although intravenous (i.v.) lidocaine is used as a perioperative analgesic in abdominal surgery, evidence of efficacy is limited. The infusion dose and duration remain unclear. This study aimed to investigate the effect of a longer low-dose 48-hour infusion regimen on these outcomes. Methods: Fifty-eight adults undergoing elective open colorectal surgery were randomized into the lidocaine group (1.5 mg/kg bolus followed by 1 mg/kg/h infusion for 48 hours) and control group. After surgery, patients were given a fentanyl patient-controlled analgesia machine and time to first bowel movement (primary outcome) and flatus were recorded. Postoperative pain scores and fentanyl consumption were assessed for 72 hours. Results: There was no significant difference in time to first bowel movement (80.1 ± 42.2 vs. 82.5 ± 40.4 hours; P = 0.830), time to first flatus (64.7 ± 38.5 vs. 70.0 ± 31.2 hours; P = 0.568), length of hospital stay (9 [8–13] vs. 11 [9–14) days; P = 0.531], nor postoperative pain scores in the lidocaine vs. control groups. Cumulative opioid consumption was significantly lower in the lidocaine vs. the control group from 24 hours onwards. At 72 hours, cumulative opioid consumption (μg fentanyl) in the lidocaine group (1,570 [825–3,587]) was over 40% lower than in the placebo group (2,730 [1,778–5,327]; P = 0.039). Conclusions: A 48-hour low-dose i.v. lidocaine infusion does not significantly speed the return of bowel function in patients undergoing elective open colorectal surgery. It was associated with reduced postoperative opioid consumption, but not with earlier hospital discharge, or lower pain scores.

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.

Micro-grinding temperature prediction considering the effects of crystallographic orientation and the strain induced by phase transformation

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.

Analytical Modelling of Cutting Force in End-Milling with Minimum Quantity Lubrication

Linger Cai,Yixuan Feng,Steven Y. Liang 한국정밀공학회 2024 International Journal of Precision Engineering and Vol.25 No.5

Milling with minimum quantity lubrication (MQL) is a commonly used machining technique in the industry because of its advantage in lowering the cutting temperature and cutting force. Among its wide usage in machining, modeling for milling operations was particularly hard for its complexity. This paper proposed an analytical model for cutting force prediction in the end-milling process with MQL. The 3D milling operation was transferred into equivalent 2D orthogonal cutting at each rotational angle. Then the proposed model incorporated updated friction coefficients due to the MQL with boundary lubrication effect. Based on Oxley’s orthogonal cutting model, the cutting force was calculated with an updated friction coefficient. Two sets of validations were done with experimental measurements using different cutting materials. The proposed model delivered reasonable accuracy for the force prediction with MQL, providing an adequate method for the industry. Based on the model investigation, the friction coefficient in cutting was also significantly affected by the droplet’s layer thickness, which was presumably linearly correlated with the flow speed of the lubricant.