Papers by Keyword: Milling Operation

Abstract: This study investigates the careful investigation of cutting parameters to improve machining effectiveness and increase tool life while milling Nickel-Titanium Shape Memory alloy (NiTiNOL). The NiTiNOL material is employed to manufacture components such as, dental braces, seismic dampers and medical implants. Using Finite Element (FE) simulation, the research closely examines the intricate interactions among parameters, such as feed rate (f_r), depth of cut (D), and cutting speed (V_c). The use of Response Surface Methodology (RSM) and Taguchi has been used to determine the most optimal settings for tool longevity and machining efficiency. The FE simulation model provides a strong framework to investigate how cutting parameters affect necessary reactions. The present study examines interactions among parameters like cutting speed, depth of cut, and feed rate. Moderate cutting speed, lower depth of cut, and the highest feed rate has induced lower stress in the workpiece. This study adds to understanding NiTiNOL alloy machining fundamentals and offers useful information for industry applications. To attain better machining results while milling NiTiNOL alloy, the results are intended to guide an optimization technique

Abstract: Milling is a widely used machining process for creating intricate parts with desired dimensions and surface quality. In this study, we investigate the effects of process parameters namely spindle speed, feed rate and depth of cut on the vibration behavior of a milling machine tool. The analysis begins by selecting appropriate cutting conditions for the milling operation. Various combinations of process parameters are considered to observe their influence on vibration of the tool. A series of experiments are conducted, with each experiment using a specific set of process parameters. The experimental trials were designed according to the factorial design. Accelerometer is employed to capture the dynamic behavior of the tool and quantify the amplitude and frequency of vibrations. The results can be utilized to optimize the machining parameters for enhanced surface quality in milling operations, leading to improved productivity, reduced tool wear and increased overall process efficiency.

Abstract: End milling is a key machining operation in industrial world, particularly in manufacturing of dies and similar products. Although, such products require high degree of surface roughness, milling operation is taken to be the enough for the cost wise if considering further finishing operations. Thus optimizing the cutting conditions to achieve the optimal surface roughness is becoming a vital issue. Several authors tackled this problem. In this paper the same case is investigated but with an advanced algorithm using regression and genetic methodology. The results obtained which ended by deducing a general equation combining the effect of various parameters on surface roughness highlighted the factors involved in achieving the surface roughness and proved to be good tool to predict the optimal cutting conditions.

Abstract: The paper reports the research on the improvement of tool wear resistant of Titanium Carbide (TiC) cutting tool after microwave post sintering treatment. Titanium Carbide square milling insert was microwave sintered at 600°C with 15 minutes of holding time. The face milling operations were conducted to Carbon Steel S45C block (130 mm x 95 mm x 40 mm) by using both of original and microwave sintered insert at 5 different cutting speed (60, 90 , 120 , 150 and 180 m/min), constant feed rate (0.2 mm/tooth) and constant depth of cut (0.2 mm/tooth). The flank wear of the insert was measured every nearest 10^th minute of complete cutting passes. The results of the experiment show that microwave post sintering treatment improves the tool resistant of the TiC insert. The flank wear of the sintered insert is lower at any machining time and all cutting speed. The research also found that the percentage of the improvement is lower at higher cutting speed compare to lower cutting speed.

Abstract: Milling is a common machining process with high cutting speed and material removal rate. High cutting speed tends to generate heat at the interface between tool and workpiece. This may reduce the surface quality of the workpiece and reduce the tool life. The application of conventional cutting fluid to reduce friction and heat between tool and workpiece may produce numerous environmental problems. The vegetable-based lubricant as an alternative for measuring the effect on surface quality during milling operation is studied. The relation between machining parameters such as spindle speed, feed rate, depth of cut and lubricants is analyzed by using Analysis of Variance (ANOVA) and Response Surface Methodology (RSM). The optimization of surface quality is analyzed by using Box-Behnken Design of RSM. The research focused on using sunflower oil as lubricant during machining process using mild steel solid block with TiCN coated HSS tools and using synthetic oil as comparison. Surface roughness for using sunflower oil as lubricant is 0.457 μm which lower compared to synthetic oil with 0.679 μm. Feed rate and spindle speed give the most significant effect to the surface roughness during milling operation. The application of vegetable-based oil as lubricant gives better surface quality, prevent tool wear and offer environmental advantages.

Abstract: The main purpose of the paper is to develop a neural network application that could predict the tool-workpiece vibration. Increase efficiency by decreasing vibrations has been imposed by the cutting progresses theory and the fields related directly to the cutting process. Thus, this procedure aims to an increasing efficiency, lowering costs and execution time and also improving the quality of parts.

Abstract: Parameter optimization in multi-pass cutting operations involves optimal selection of cutting speed, feed rate, depth of cut, and the number of passes, duo to significant influence of these parameters on the quality of machined parts and machining economics. In this paper, a non-linear mathematical model based on minimum production cost for multi-pass milling operations is presented. The unwanted material is removed by one finishing pass and one or multiple roughing passes depending on the total depth of cut. Various realistic constraints are considered when developing the model. Optimal values of machining parameters are found by Genetic Algorithms. An example is presented to illustrate the optimization model and solution approach. The method yields lower unit production costs compared with the results from the literature and machining data handbook.

Abstract: The use of CNC machines is one of the successful factors in the computer integrated manufacturing (CIM). Even though the CNC machine can automatically perform the machining processes, some of the situations that may significantly influence the quality of product such as a cutting tool breakage. Therefore, to prevent the machine from damaging and ensure the quality of product, it is important to develop a system that can monitor the tool conditions. The purpose of this study is to develop a Taguchi-neural-based in-process tool breakage monitoring system in end milling operations that can monitor the tool conditions and immediately response a proper action. For an in-process tool breakage monitoring system, a neural network was applied to making decisions of monitoring. One of the disadvantages of neural network is the training processes. It is difficult to determine an optimal combination of training parameters of neural networks. Traditionally, the try-and-error method is time-consuming and without systematic base. Therefore, the optimization of training parameters for neural networks using Taguchi design was applied to training the neural network model and to enhance the accuracy of the tool breakage monitoring system.

Abstract: Many research show that in metal cutting process wear rate is dependent on the cutting process variables such as temperature at tool face, contact pressure (normal stress) and relative sliding velocity at tool/chip and tool/work interface; their relationship is described by “differential” wear rate model. Based on this “differential” wear rate model, a method to estimate tool wear in milling operation is proposed and the cutting process variables are predicted by performing chip formation analysis with FEM code ABAQUS/Explicit and heat transfer analysis with ABAQUS/Standard. The implementation is exemplified by estimating tool wear of carbide tools in milling of Ck45 work. Both progressive flank wear and crater wear profile are estimated.