RBF network for predicting surface roughness and tool life in ceramic tool turning of gray cast iron. An RBF network was used to predict the cutting force in gray cast iron turning with a ceramic tool.
Turning Processes
Modeling of turning process is over a century old with the pioneering work of Taylor [1907]. Among the other important parameters are tool geometry, tool material, work material, work dimensions, cutting fluid, machine tool stability, motor power, temperature at the cutting zone etc. which have a direct influence on the turning process.
Environmental Aspects inTurning Processes
In the turning operation, the most important process parameters are speed, feed and depth of cut, the optimal value of which depends on the workpiece and the tool material. The cutting force and vibration have a direct influence on the surface finish and dimensional deviation of the turned job.
Soft Computing Techniques and Optimization Methods
Optimization of turning process parameter is a widely used research topic for most of the researchers. However, since recent years, a trend to use non-traditional techniques has become popular in the optimization process.
Objectives and Organization of the Thesis
In the modeling of the turning processes, faster estimations of parameters are important for online control and optimization. A strategy has been developed to optimize the finishing turning process without prior knowledge of the tool life.
Introduction
In this chapter, a review of available literature in the area of turning process and environmentally friendly processing is presented in different sections. Application of soft computing techniques to various modeling techniques in turning is discussed in Section 2.4.
Experimental Studies on Machining Performance
Surface Roughness and Dimensional Deviation
Albrecht [1956] investigated the effect of speed, feed, depth of cut and nose radius on the surface finish of a steel workpiece. Ansell and Taylor [1962] studied the effect of tool material on the surface finish of a cast iron workpiece.
Tool Wear and Tool Life
The end of tool life for tools with high CBN content was mainly due to flank and crater wear. This indicates that the tool life is improved due to the coating over the tool.
Cutting Force
However, instead of raw data from cutting force components, the processed data is more efficient. The relative magnitude of cutting force components depends on the tool-work combination and the condition of the tool.
Mathematical Models
2002] developed a model for predicting surface roughness in turning using response surface methodology to produce the factorial effects of individual process parameters. In addition to simulating the machining process, the finite element model has been used to predict tool wear and cutting tool fracture [Ahmad et al., 1989;.
Application of Soft Computing Technique
Surface Finish and Dimensional Deviation
Chen and Savage [2001] used a fuzzy net-based model to predict surface roughness with different tool and task combinations in end milling. Abburi and Dixit [2006] developed a knowledge base system using the NN model and fuzzy set theory for the prediction of surface roughness in turning processes.
Tool Wear and Tool Life
A number of other soft computing techniques have been used to estimate tool wear in machining. 2000] proposed a controlled fuzzy backpropagation neural network model for tool wear prediction in face grinding operation.
Cutting Force
2006] predicted shear forces on self-propelled rotary vehicle using a multi-layer perceptron neural network. 2002] also compared a multi-layer perceptron neural network with a radial basis function neural network for predicting machining forces in milling.
Green Manufacturing and its Relvance to Turning Process
However, the use of cutting fluid and disposal is a major concern for the environmental issue. Environmental considerations require reduction of cutting fluid either by using minimal cutting fluid or dry machining.
Optimization Techniques
He considered the following restrictions:. a) maximum available cutting power, (b) required surface roughness, (c) maximum cutting force permitted by the machine tool, (d) maximum feed rate and rotational speed available to the machine tool. Optimum cutting parameters were achieved by minimizing the unit production cost while taking into account various practical constraints.
Challenging Issues
Scope and Objectives of the Present Work
Neural network is one of the most popular soft computing techniques that has different architectures. On the other hand, MLP neural network requires less training data compared to RBF neural network, but.
Summary
Many of the researchers have used soft computing techniques for predicting surface finish, dimensional deviation of tool wear, tool life, cutting forces etc. Some of the important soft computing techniques used by researchers are neural networks (NN), fuzzy systems (FS ), evolutionary computation (EC), swarm intelligence (SI), genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA) etc.
Introduction
White oxide ceramics are used for roughing gray cast iron, nodular cast iron and chilled cast iron. It is generally used for machining hard materials and finishing cast iron.
Experimental Procedure
Experimental Set-up
The effect of forced air cooling on surface roughness of the work, tool wear and cutting forces was investigated. These accelerometers are connected to a dynamic signal analyzer (Model: Enkel 3560D, Make: Bruel & Kjaer), which in turn is connected to the computer to take the data using B&K software, Pulse.
Workpiece Materials
The hardness of the workpiece was 130 BHN, yield strength 290 MPa and ultimate tensile strength 477 MPa. The workpieces are heat treated at approximately 1040°C followed by air quenching to increase the hardness of the material to 46 HRC.
Cutting Tools
Design of Experiments and Comparative Study
Study of Cutting Performance of HSS Tool and Non-coated Brazed
Next, the experiments were conducted to study the surface finish in turning cast iron with non-coated carbide tool. Consequently, more experiments were conducted with uncoated carbide tools considering different cutting combinations as shown in Table 3.5.
Study of Cutting Performance of Coated Carbide Tool
In most cases, surface finish is almost the same for both dry and air-cooled turning under all cutting conditions. It is noted that the value of cutting force or feed force in most cases is almost the same for both air cooled and dry turning.
Study of Cutting Performance of Ceramic Tool
Progression of surface roughness with cutting time: (a) dry turning (b) air-cooled turning of gray cast iron with ceramic tool at high cutting speed. Progression of tool flank wear during turning: (a) dry turning and (b) air-cooled turning of gray cast iron with ceramic tool.
Study of Cutting Performance of CBN Tool
In general, the surface finish with air-cooled turning is considered slightly better than with dry turning. Course of tool wear during (a) dry turning and (b) air-cooled turning of hardened steel with CBN tools.
Summary
It was observed that air cooling does not significantly affect the surface roughness of the machined surface. It was also observed that air cooling does not help in reducing cutting and feed forces.
Introduction
Next, a neural network model was developed to predict surface roughness and tool life when turning gray cast iron with mixed oxide ceramic tools in dry and air-cooled conditions. Neural network modeling was performed to predict cutting and feed forces when turning gray cast iron with a mixed oxide ceramic tool.
Background on Neural Networks
The output of a layer reaches as a weighted input in the neurons of the next layer. This propagated error is used to adjust the weights connecting these neurons to the neurons of the previous layer.
Comparative Study of Cutting Performance of HSS, Carbide and Ceramic
In this work, to fine-tune the value of dispersion parameter, the value calculated by Equation 4.7 is multiplied by a factor β. It also identifies the zones where detailed investigation should be carried out to study the performance parameter of carbide and ceramic tools.
Modeling of Cutting Performance of Coated Carbide Tool in Dry and
Design of Experiments and Prediction of Surface Roughness by
Factor with the least influence is assigned level 2. The levels of other factors are taken in proportion to their effect, and additional experiments are performed to represent these levels in the data set. Test data set for predicting surface roughness when turning mild steel with coated carbide tools during air-cooled turning.
Estimation of Tool Life using Neural Network Model
The tool life values are then fed into the neural network model to develop a model for predicting tool life in the machining of coated carbide tool steel. In certain cases, tool life is increased by more than a factor of 2 during air-cooled turning.
Modelling of Cutting Performance of Ceramic Tool in Dry and Air-cooled
Neural Network Modelling for Prediction of Surface Roughness and
The following are the results obtained using this methodology for surface roughness and tool life in air cooled and dry turning. The effect of depth of cut on tool life is not uniform especially at low speeds.
Neural Network Modelling of Cutting Forces
Predicted versus actual cutting force in turning of gray cast iron with ceramic tools: (a) dry turning and (b) air-cooled turning. Predicted versus actual feed force in turning of gray cast iron with ceramic tools: (a) dry turning and (b) air-cooled turning.
Summary
Another important work was the modeling of the cutting force and feed force using the RBF mesh. One of the major challenges in this work was a model for indirectly predicting tool flank wear based on cutting and feed forces.
Introduction
Tool Life in Finish Turning
Introduction to Weibull Distribution
Weibull distribution parameters were estimated by the probability plot graphical method combined with the principle of least squares [Murthy et al., 2004]. Estimates of the parameters of the Weibull distribution can be found with probability graph paper or analytically, or using least squares analysis (regression analysis) or the method of maximum likelihood.
Application of Weibull Distribution for Calculation of Tool Life
Probability distributions of tool life under different cutting conditions: (a) dry turning at a cutting speed of 300 m/min (b) air-cooled turning at a cutting speed of 300 m/min (c) dry turning at a cutting speed of 270 m/min min (d) air-cooled turning when cutting. It is also noted that the maximum wear of the tool flank at the end of the tool life depends on the cutting conditions.
Enhancing the Tool Life with Air-cooling
Let the tool edge cost be Ct and the total tool life in dry turning be denoted as T1. Then the cost of tool edge per unit time in dry turning is equal to Ct/T1.
Enhancing the Utilization of Cutting Tool by Changing Cutting Conditions
If the tool change time is a very small fraction of the time to tool failure, then a simplified relation is For experimental verification of this strategy, after the tool failed at a feed of 0.1 mm/rev in dry turning, it was operated at a feed of 0.07 mm/rev.
A Heuristic Method for Optimizing the Finish Turning Process
However, if increasing fv decreases fvT, the objective function may increase or decrease according to the value of t*c. When the optimum value of c is reached, machining is performed at a cutting speed that is (1-c) times the maximum speed.
Summary
Conclusions
The experiment has shown that HSS tools are not suitable for finishing (corresponding to N7 surface roughness) of gray cast iron. It has been observed that the neural network modeling helps in predicting the surface finish, tool life and cutting forces during turning.
Scope for Future Work
Fang, X.D., (1995), Ekspertsystem-understøttet fuzzy diagnosis of finish-turning process states, International Journal of Machine tools and Manufacture, 35, s. Kilic, S.E., (1987), Use of one-dimensional search method for the optimering af drejeoperationer, Modeling and Simulation Control B, 14, s.39–63.
