Erry Yulian Triblas Adesta

Flank Wear Modeling in High Speed Hard Turning by using artificial Neural Network and Regression Analysis

Predicting and modeling flank wear length in high speed hard turning by using ceramic cutting tools with negative rake angle was conducted using two different techniques. Regression model is developed by using design of expert 7.1.6 and neural network technique model was built by using matlab2009b. A set of experimental data for high speed hard turning of hardened AISI 4340 steel was obtained with different cutting speeds, feed rate and negative rake angle. Flank wear length was measured to train the neural network models and to develop mathematical model by using regression analysis. Predictive neural network models are found to be capable of better predictions tool flank wear within the range that they had been trained.

Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

Investigation of the effect of cutting speed on the Surface Roughness parameters in CNC End Milling using Artificial Neural Network

This research presents the effect of high cutting speed on the surface roughness in the end milling process by using the Artificial Neural Network (ANN). An experimental investigation was conducted to measure the surface roughness for end milling. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted. The artificial neural network (ANN) was applied to simulate and study the effect of high cutting speed on the surface roughness.

Cutting Temperature and Surface Roughness Optimization in CNC End Milling Using Multi Objective Genetic Algorithm

Machining of hard materials at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality. Thus, developing a model for estimating the cutting parameters and optimizing this model to minimize the cutting temperatures and surface roughness becomes utmost important to avoid any damage to the quality surface. This paper presents the development of new models and optimizing these models of machining parameters to minimize the cutting temperature in end milling process by integrating the genetic algorithm (GA) with the statistical approach. The mathematical models for the cutting temperature and surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Two objectives have been considered, minimum cutting temperature and minimum arithmetic mean roughness (Ra). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.

Predicting Surface Roughness with Respect to Process Parameters Using Regression Analysis Models in End Milling

Surface roughness affects the functional attributes of finished parts. Therefore, predicting the finish surface is important to select the cutting levels in order to reach the required quality. In this research an experimental investigation was conducted to predict the surface roughness in the finish end milling process with higher cutting speed. Twenty sets of data for finish end milling on AISI H13 at hardness of 48 HRC have been collected based on five-level of Central Composite Design (CCD). All the experiments done by using indexable tool holder Sandvick Coromill R490 and the insert was PVD coated TiAlN carbide. The experimental work performed to predict four different roughness parameters; arithmetic mean roughness (Ra), total roughness (Rt), mean depth of roughness (Rz) and the root mean square (Rq).

Surface Roughness Optimization in End Milling Using the Multi Objective Genetic Algorithm Approach

In finishing end milling, not only good accuracy but also good roughness levels must be achieved. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness is important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). The mathematical models for the surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). Due to complexity of this machining optimization problem, a multi objective genetic algorithm (MOGA) has been applied to resolve the problem, and the results have been analyzed.

An Investigation on Low-Temperature Thermochemical Treatments of Austenitic Stainless Steel in Fluidized Bed Furnace

In this study, the feasibility of using an industrial fluidized bed furnace to perform low-temperature thermochemical treatments of austenitic stainless steels has been studied, with the aim to produce expanded austenite layers with combined wear and corrosion resistance, similar to those achievable by plasma and gaseous processes. Several low-temperature thermochemical treatments were studied, including nitriding, carburizing, combined nitriding-carburizing (hybrid treatment), and sequential carburizing and nitriding. The results demonstrate that it is feasible to produce expanded austenite layers on the investigated austenitic stainless steel by the fluidized bed heat treatment technique, thus widening the application window for the novel low-temperature processes. The results also demonstrate that the fluidized bed furnace is the most effective for performing the hybrid treatment, which involves the simultaneous incorporation of nitrogen and carbon together into the surface region of the component in nitrogen- and carbon-containing atmospheres. Such hybrid treatment produces a thicker and harder layer than the other three processes investigated.

Powder mixed micro electro discharge milling of titanium alloy: analysis of surface roughness

This paper presents effects of silicon carbide SiC powder concentration on micro EDM parameters on average surface roughness (Ra). The aim is to achieve minimum Ra value on titanium alloy (Ti-6Al-4V) machined with tungsten electrode for various level of concentration of SiC powder and discharge energy (E). By using two-parameter and four-level factorial design of experiment sixteen experiments were conducted. The measured surface roughness values were analyzed with input parameters using by Design Expert software. The minimum Ra value obtained was 0.75 µm for 16.8 g/L SiC powder concentration and 57.8 µJ energy discharge. The analysis of variance revealed that powder concentration is the most influential parameter.

Tool Life in High Speed Turning with Negative Rake Angle

The present work studies some aspects of turning process applied on mild steel using cermets tools at high speed cutting (1000mm/min) by using negative rake angle (0 to-12). The influence of increasing the cutting speed and negative rake angle on flank tool wear, cutting forces, feeding forces and tool temperature were analyzed. The research studies and concentrates on the tool life estimation and the effect of the negative rake angle and higher cutting speed on tool life. It was found that the maximum tool life is obtained in (-6) rake angle for the cutting parameters.

Machining Time Simulation in High Speed Hard Turning

High speed hard turning is an advanced manufacturing technology that reduces the machining time because of two reasons; reducing the manufacturing steps and increasing the cutting speed. This new approach needs an economical justification; one of the main economical factors is the machining time. The machining time was breaking down into three main parts; productive time, non productive time, and preparation time. By using matlab Simulink, a new program was developed for machining time allowing the manufacturer to find rapidly the values of cutting time parameters and gives the management the opportunity to modify the processing parameters to achieve the optimum time by using the optimum cutting parameters. Table 1: Nomenclature d Depth of cut M T total machining time pmv t Total movement time D Work piece diameter h t handling time pch t Total Tool changing time f Feed rate tc t tool changing time pre t Total preparing time z e Engagement distance on Z-axis ch t Tool changing time per piece, prg t Programming time x e Degagement distance on X-axis am t Machine allowance time su t Set up time k Number of passes ao t Operator allowance time sum t Machine set up L Tool life a t Allowance time sut t Tool set up l Work piece length o t Tool movement at the rapid speed suw t Work piece set up N Spindle speed oA t From zero point to cutting point TH Tool hardness tool n No. of tool posts in the turret p t Total productive time o X tidy of the O t point o1 p Initial position of the turret. o Z = abciss of the O t point w Work piece weigh o2 p Position of the used tool c V Cutting speed c w Width of cutting speed r Rotation speed of the turret f V Feeding speed tool n no. of tool in the turret c t Cutting time o V Rapid speed speed r : Turret rotation speed