Study on the Cutting Prediction of Supercritical Material
نویسندگان
چکیده
The technology of the artificial neural network (ANN) was applied in the research of supercritical material cutting. Two-dimensional Gaussian surfaces of the three cutting elements and workpiece surface hardness had been established fitting through JMP software. Base on the orthogonal milling experiments, the rules of cutting forces variation were forecasted, as well as the effect to the hardness on workpiece surface. The cutting parameters selected according to the process were built, providing an important basis for the optimization of machining conditions. The prediction results were in good agreement with the experimental results.
منابع مشابه
Prediction Model for CNC Turning on AISI316 with Single and Multilayered Cutting tool Using Box Behnken Design (RESEARCH NOTE)
Austenitic stainless steels (AISI316) are used for many commercial and industrial applications for their excellent corrosive resistance. AISI316 is generally difficult to machine material due to their high strength and high work hardening tendency. Tool wear (TW) and surface roughness (SR) are broadly considered the most challenging phases causing poor quality in machining. Optimization of cutt...
متن کاملCutting Force Prediction in End Milling Process of AISI 304 Steel Using Solid Carbide Tools
In the present study, an attempt has been made to experimentally investigate the effects of cutting parameters on cutting force in end milling of AISI 304 steel with solid carbide tools. Experiments were conducted based on four factors and five level central composite rotatable design. Mathematical model has been developed to predict the cutting forces in terms of cutting parameters such as he...
متن کاملSolubility Prediction of Drugs in Supercritical Carbon Dioxide Using Artificial Neural Network
The descriptors computed by HyperChem® software were employed to represent the solubility of 40 drug molecules in supercritical carbon dioxide using an artificial neural network with the architecture of 15-4-1. The accuracy of the proposed method was evaluated by computing average of absolute error (AE) of calculated and experimental logarithm of solubilities. The AE (±SD) of data sets was 0.4 ...
متن کاملThe Effect of Power and Maximum Cutting Speed on the Material Removal Rate and Cutting Volume Efficiency in CO2 Laser Cutting of Polycarbonate Sheets
In the laser cutting process some well-known parameters, e.g. laser power and cutting speed, play major roles in the performance of the process. Each parameter or a combination of parameters can affect the material removal volume and cutting volume efficiency. The purpose of this research is to study the effect of power and maximum cutting speed on the material removal rate (MRR) and cutting vo...
متن کاملSolubility Prediction of High Molecular Weight n-Paraffins in Supercritical Carbon Dioxide
Solubility of high molecular weight n-paraffins in supercritical carbon dioxide has been a matter of interest to many researchers. However, not sufficient solubility experimental data are available although the methods by which the experimental data are obtained have many varieties. Utilizing cubic equations of state is an effective method for solubility prediction of n-paraffins in supercr...
متن کامل