Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests

Accuracy of numerical models based in finite elements (FE), extensively used for simulation of cutting processes, depends strongly on the identification of proper material parameters. Experimental identification of the constitutive law parameters for simulation of cutting processes involves unsolved problems such as the complex testing techniques or the difficulty to reproduce the stress triaxiality state during cutting. This work proposes a methodology for the inverse identification of the material parameters from cutting test. Two hybrid approaches are compared. One of them based on FE and artificial neural networks (ANN). The other one based on FE and local polynomial regression (LPR). Firstly, a FE model is validated with experimental data. Then, ANN and LPR are trained with FE simulations. Finally, the estimated ANN and LPR models are used for the inverse identification of material parameters. This identification is solved as an optimization problem. The FE/LPR approach shows good performance, outperforming the FE/ANN approach.The authors acknowledge the financial support of this work to the Ministry of Science and Education of Spain (under project DPI2008-06746).Publicad

Modelado de las fuerzas de corte en el torneado de alta velocidad utilizando redes neuronales artificiales

Cutting forces are very important variables in machining performance because they affect surface roughness, cutting tool life, and energy consumption. Reducing electrical energy consumption in manufacturing processes not only provides economic benefits to manufacturers but also improves their environmental performance. Many factors, such as cutting tool material, cutting speed, and machining time, have an impact on cutting forces and energy consumption. Recently, many studies have investigated the energy consumption of machine tools; however, only a few have examined high-speed turning of plain carbon steel. This paper seeks to analyze the effects of cutting tool materials and cutting speed on cutting forces and Specific Energy Consumption (SEC) during dry high-speed turning of AISI 1045 steel. For this purpose, cutting forces were experimentally measured and compared with estimates of predictive models developed using polynomial regression and artificial neural networks. The resulting models were evaluated based on two performance metrics: coefficient of determination and root mean square error. According to the results, the polynomial models did not reach 70 % in the representation of the variability of the data. The cutting speed and machining time associated with the highest and lowest SEC of CT5015-P10 and GC4225-P25 inserts were calculated. The lowest SEC values of these cutting tools were obtained at a medium cutting speed. Also, the SEC of the GC4225 insert was found to be higher than that of the CT5015 tool.Las fuerzas de corte son variables muy importantes para el rendimiento del mecanizado, ya que afectan la rugosidad de la superficie, la vida útil de la herramienta de corte y el consumo de energía. La reducción del consumo de energía eléctrica de los procesos de fabricación no solo beneficia económicamente a los fabricantes, sino que también mejora su comportamiento medioambiental. Muchos factores, como el material de la herramienta de corte, la velocidad de corte y el tiempo de mecanizado, afectan la fuerza de corte y el consumo de energía de la máquina. En la actualidad, muchas investigaciones se han realizado sobre el consumo energético de las máquinas herramienta. Sin embargo, la investigación sobre torneado de acero al carbono a alta velocidad es escasa. En este trabajo se estudiaron los efectos de los materiales de las herramientas de corte y su velocidad sobre las fuerzas de corte y el consumo específico de energía en el torneado en seco de alta velocidad de acero AISI 1045. Las fuerzas de corte se determinaron experimentalmente y se compararon con las estimaciones de los modelos predictivos desarrollados mediante regresión polinomial y redes neuronales artificiales. Los modelos obtenidos fueron evaluados según métricas de desempeño como el coeficiente de determinación y la raíz del error cuadrático medio, donde los modelos polinomiales no superaron el 70% en la representación de la variabilidad de los datos. Se determinó la velocidad de corte y el tiempo de mecanizado relacionados con el mayor y menor consumo de energía de las plaquitas CT5015-P10 y GC4225-P25. Los valores más bajos de consumo de energía de estas herramientas se alcanzaron para la velocidad de corte intermedia. Además, la plaquita GC4225 presentó un mayor consumo que la herramienta CT5015

Machinability assessment when turning AISI 316L austenitic stainless steel using uncoated and coated carbide inserts

Austenitic stainless steel AISI 316L is mostly used as an implant material and is customarily applied as impermanent devices in orthopedic surgery because of its low cost, adequate mechanical properties, and acceptable biocompatibility. AISI 316L is an extra-low carbon type 316 (austenitic chromium nickel stainless steel containing molybdenum) that minimizes harmful carbide precipitation at elevated temperature. Machining is part and parcel during the fabrication of implants and medical devices made from stainless steels and thus it is of interest to evaluate the machinability of AISI 316L. In this study, austenitic stainless steel AISI 316L was turned using two commercially available cutting tool inserts at various cutting speeds (90, 150, and 210 m/min) and feeds (0.10, 0.16, and 0.22 mm/rev) and at a constant depth of cut of 0.4 mm. The turning of AISI 316L was implemented in dry cutting. The cutting tools used were an uncoated tungsten carbide-cobalt insert (WC-Co) and a multi coated nano-textured TiCN, nano-textured Al2O3 thin layer, and a TiN outer layer insert. The cutting forces, total power consumption, surface roughness, and tool life were measured/obtained and analyzed. The total power consumption of the turning process was obtained from direct measurements as well as using a combination of theoretical formulas and experimental cutting force data. The machining experiments and their responses were designed and evaluated using the three-level full factorial design and the analysis of variance (ANOVA). It was found that the cutting speed and feed significantly affect the various machining responses observed. The cutting force and total power consumption increased with increasing cutting speed, but the surface roughness and tool life decreased. With increasing feed, surface roughness and tool life decreased but the cutting force and total power consumption increased. The empirical mathematical models of the machining responses as functions of cutting speed and feed developed were statistically valid. Confirmation runs helped to prove the validity of the models within the limits of the factors investigated

Residual Stress Prediction in Turbine Blade Machining Operations Using a Virtual Machining System

Part manufacturing process using machining operations is along with residual stress due to friction, chip formation and generated heat in cutting zone. The performance of produced parts in working conditions such as fatigue life, corrosion resistance and part distortion is under the influence of residual stress which should be analyzed and minimized. To produce compressor section blades of gas turbines, machining operations can be used. The process is always with complexities and challenges. However it can be analyzed and modified in virtual environments. Residual stress due to machining operations of gas turbine blades can also be analyzed in virtual environments in order to be minimized. In the present research work, application of a virtual machining system to predict residual stress in milling operations of turbine blades is presented. Finite element analysis is implemented in order to calculate residual stress as well as strain of blades in machining operations. In order to validate the research work, experimental results are compared with the finite element results obtained from the virtual machining system. The present research work can replace the costly experimental tests by predicting the residual stress in a virtual machining environment

Modelling and Prediction of Effect of Machining Parameters on Surface Roughness in Turning Operations

In this study, effects of different machining parameters on surface roughness in turning of St-37 material are presented. The machining experiments were carried out on the CNC lathe. In order to minimize the number of experiments, the experimental design was set up using Taguchi\u27s L27 orthogonal array. Cutting speed (150 m/min, 200 m/min, and 250 m/min), feed rate (0,1 mm /rev, 0,2 mm/rev, and 0,3 mm/rev), depth of cut (0,5 mm, 1 mm, and 1,5 mm), and tool nose radius (0,4 mm, 0,8 mm and 1,2 mm) were used as control factors. The analysis of variance (ANOVA) was performed in order to determine the impact of the control factors on surface roughness. Signal/noise (S/N) ratios were determined in the Taguchi design. The results of the regression models and Taguchi Analysis revealed that the most effective parameters on surface roughness (Ra and Rz) were the feed rate (f) and tool nose radius (R)

Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

Recent advances in modelling and simulation of surface integrity in machining - A review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted

Prediction of Hardness and Residual Stress in Orthogonal Cutting of Inconel 718

Due to its high strength in high temperatures, Inconel 718 is widely used in the aerospace industry. However, Inconel 718 is a difficult-to-cut alloy with poor machinability. For instance, the cutting force is high in cutting Inconel 718, resulting in work-hardening of the machined surface and high residual stress in the machined surface. When residual stress releases, the part deforms and scrapes with error beyond tolerance. Therefore, it is necessary to predict the residual stress in the machined surface under a set of machining conditions. By modifying the machining conditions, the residual stress in the machined surface is under control, and the part deformation is limited. In this research, an analytical approach to the hardness and the residual stress in the machined surface in orthogonal cutting is proposed. This research has advantages over the experiment, the conventional approach and the FEA methods. With this approach, the cutting parameters can be optimized to minimize the residual stress in the machined surface and improve the surface integrity

Multi revolution finite element model to predict machining induced residual stresses in Inconel 718

Inconel 718 is commonly used in structural critical components of aircraft engines due to its mechanical thermal properties at high temperatures, which makes it to be considered as a difficult to machine material. In these critical parts, such as disk turbines, surface integrity should be assured in order to ensure the expected fatigue life. In order to determine the influence of feed and depth of cut in residual stresses a finite element facing model has been developed. This model takes into account the complex thermo mechanical phenomena that take place during chip formation process as well as the effect of cyclic loading phenomena due to the successive revolutions. Firstly, full stress, strain and temperature fields are obtained with a Deform 3D v10.2 nose turning model. Those fields are introduced in a multi revolution Abaqus/Standard v6.12 machining model. Finally the residual stresses of the model are extracted as an approach of Hole Drilling measurement technique. The results are in good agreement with empirical measurements

DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al4V)

Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters