ROUGHNESS OF A MACHINED SURFACE IN MILLING OPERATION FOR FERROUS AND NON FERROUS A FUZZY LOGIC BASED MODEL TO PREDICT SURFACE MATERIALS USING HSS END MILL CUTTING TOOL

Nowadays every manufacturing and industrial industry has to focus on the manufacturing of quality products. Manufacturing of these kinds product with higher accuracy, better surface finish, lower maintenance and lower process planning and manufacturing cost are very important factor that can achieved by using non-conventional optimization techniques instead of conventional techniques. Many of non-conventional optimization techniques like Fuzzy Logic approach based technique, Genetic algorithms, Artificial Neural Network, Particle Swarm optimization, Ant colony optimization, Scatter search technique and simulated Annealing etc. are used to optimization of surface roughness. Milling is a machining operation in which workpiece is fed below the cylindrical rotating multi point cutting tool, multi point cutting tool having multiple cutting edges. On the basis of literature review, many machining parameters such as cutting speed, feed rate, depth of cut, cutting fluid pressure etc. and performance parameters as surface roughness, material removal rate, tool wear ratio, tool vibration etc., were observed for CNC milling operation. The correct selection of machining parameters is very important factor to achieve best performance measure. In this research work, spindle speed (SS), feed rate (FR) and depth of cut (DOC) are selected as machining parameters while surface roughness is considered as performance parameters to perform end milling operation on the workpiece materials of 6101 Aluminum alloy, Copper of electrolytic grade and Mild Steel 2062 by using High Speed Steel (HSS) end mill cutter of 12 mm diameter. Minimum experiment trials are designed by Taguchi based L9 (3^3) orthogonal array with the help of Minitab 17.0 software and a fuzzy logic approach based model is taken as to predict the value of surface roughness of a machined surface in 6101 aluminum alloy, Copper of Electrolytic grade and Mild Steel 2062 milling operation using HSS end mill cutter of 12 mmdiameter. Three membership functions are allocated to be connected with each input of the model. The predicted results achieved via fuzzy logic model are compared to the experimental result. The result demonstrated settlement between the fuzzy model and experimental results with the 95.618% model accuracy for 6101 aluminum alloy material, 83.849% for copper (Electrolytic grade) and 98.334% Mild Steel 2062

Optimization of 5-axis milling processes using process models

Productivity and part quality are extremely important for all machining operations, but particularly for 5-axis milling where the machine tool cost is relatively higher, and most parts have complex geometries and high quality requirements with tight tolerances. 5- axis milling, presents additional challenges in modeling due to more complex tool and workpiece interface geometry, and process mechanics. In this paper, modeling and optimization of 5-axis processes with cutting strategy selection are presented. The developed process models are used for cutting force predictions using a part-tool interface identification method which is also presented. Based on the model predictions and simulations, best cutting conditions are identified. Also, for finish process of a complex surface, machining time is estimated using three machining strategy alternatives. Results are demonstrated by example applications, and verified by experiments

Analytical models for high performance milling. Part I: cutting forces, structural deformations and tolerance integrity

Milling is one of the most common manufacturing processes in industry. Despite recent advances in machining technology, productivity in milling is usually reduced due to the process limitations such as high cutting forces and stability. If milling conditions are not selected properly, the process may result in violations of machine limitations and part quality, or reduced productivity. The usual practice in machining operations is to use experience-based selection of cutting parameters which may not yield optimum conditions. In this two-part paper, milling force, part and tool deection, form error and stability models are presented. These methods can be used to check the process constraints as well as optimal selection of the cutting conditions for high performance milling. The use of the models in optimizing the process variables such as feed, depth of cut and spindle speed are demonstrated by simulations and experiments

Comparative study of Sustainability Metrics for Face Milling AISI 1045 in different Machining Centers

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)The objective of this study is to compare a set of sustainability metrics between different manufacturing resources applied to high performances machining centers. The research compares distributed scenarios in order to find the optimal conditions that allow the minimum consumed power and the minimum roughness when performing face milling operations of AISI 1045 steel. The set of experiments for the surface machining was carried out considering different path strategies in three main directions for two dimensional movements of the tool. The selected experiments considered the main axis movement, the perpendicular axis movement and a 45 degrees movement. Besides, it was considered the feed rate speed and the cutting depth. The design of experiments was developed with the Taguchi method considering an orthogonal matrix of L27 design type, and three levels of experimental design, and the analysis of variance and noise signal were performed. The methodology to determine the lowest power consumed and the best surface quality allowed to establish the working condition in the most sustainable machining. The results show how the cutting parameters influence in each manufacturing resource

Mono and Multi-Objective Optimization and Modeling of Machining Performance in Face Milling of Ti6Al4V Alloy

Titanium alloys are extensively used in numerous industries like aerospace, automotive, military, etc., due to their exclusive characteristics. But machining these alloys has always been challenging for manufacturers. This research investigates the effect of radial depth of cut on cutting forces, tool life, surface roughness (Ra), and material removal rate (MRR) during face milling of Ti6Al4V alloy. It also aims to perform mono and multi-objective optimization of response characteristics to determine the optimal input parameters, namely cutting speed, feed rate, and radial depth of cut. Taguchi method and analysis of variance (ANOVA) have been used for mono-objective optimization, whereas Taguchi-based Grey relational analysis (GRA) and Genetic algorithm (GA) have been used for multi-objective optimization. Regression analysis has been performed for developing mathematical models to predict Ra, tool life, average cutting forces, and MRR. According to ANOVA analysis, the most significant parameter for tool life is cutting speed. For MRR and average cutting force (Avg. FY), the most influential parameter is the radial depth of cut. On the other hand, feed rate is the most significant parameter for Ra and average feed force (Avg. FX). The optimal combination of input parameters for tool life and Avg. FY is 50 m/min cutting speed, 0.2 mm/rev feed rate, and 7.5 mm radial depth of cut. However, the optimal parameters for Ra are 65 m/min cutting speed, 0.2 mm/rev feed rate, and 7.5 mm radial depth of cut. For Avg. FX, the optimal conditions are 57.5 m/min cutting speed, 0.2 mm/rev feed rate, and 7.5 mm radial depth of cut. Similarly, for MRR, the optimal parameters are 65 m/min cutting speed, 0.3 mm/rev feed rate, and 12.5 mm radial depth of cut. A validation experiment has been conducted at the optimal Ra parameters, which shows an improvement of 31.29% compared to the Ra measured at the initial condition. A minor error has been found while comparing the experimental data with the predicted values calculated from the mathematical models. GRA for multi-objective (3 objectives: tool life, Ra, and Avg. FY) optimization has improved 55.81% tool life, 6.12% Ra, and 23.98% Avg. FY. ANOVA analysis based on grey relational grade has demonstrated that radial depth of cut is the most significant parameter for multi-objective (three objectives) optimization during the face milling of Ti6Al4V. The results obtained from the GRA considering four output characteristics (tool life, Ra, Avg. FY, and MRR) are compared with GA optimization results for both roughing and finishing, and a negligible deviation has been observed

A multi-sensor based online tool condition monitoring system for milling process

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications

Optimization of Machining Parameters Using the Taguchi and ANOVA Analysis in the Face Milling of Aluminum Alloys AL7075

This paper examines the influence of the cutting parameters on the cutting forces and the surface roughness at the face milling process when machining aluminum alloy 7075 is obtained by the new SSM casting process. The parameters of the milling process are the cutting speed, the feed per tooth and the depth of cut. The experiments were performed according to the Taguchi method according to the L9 plan and the factors varied at three levels. For analysis of the effects of these parameters S/N ratio is used. In addition, ANOVA analysis was performed, i.e. analysis of the variance of the selected parameters. The analysis of the results shows that the optimal combination for the cutting force is the choice of a minimum level for all tested parameters. In contrast, for average arithmetic roughness, the optimal processing regime is achieved with minimum values for cutting speed and feed per tooth, whilst it is preferable to choose the cutting depth at the median level for the observed range. In addition, the study shows that the Taguchi method is suitable for solving the problem, where the research was carried out with a minimum number of tests compared to a full factorial experimental plan

Optimization of surface roughness of A1-7075 T6 in CNC end milling

Submitted in partial fulfilment for the requirement of the degree of Bachelor of Engineering in MechanicalThe demand for high strength and low weight material in aerospace industries is found to be\ud increasing in fabrication of structures and equipment’s of aircraft and space satellites.\ud Aluminium alloys possesses the characteristics of lightweight and high strength. The\ud identification of the optimum values of input parameters to achieve better surface finish as\ud response parameters is the prime objective of the project. The Aluminium 7075 T651 is worked\ud on the End milling process on the HAAS CNC machine. Different parameters of Cutting Speed,\ud Feed, and Depth of Cut in the Orthogonal Array method using Taguchi method. The results are\ud to be worked on the DOE method on software MINITAB.\ud The material can further be worked upon different input parameters on various machines and for\ud different response parameters. For carrying out the above-mentioned process various papers were\ud referred for guidance and knowledge in the same field of previously carried experiments and\ud results. Various technical concepts and tools like The Taguchi, Orthogonal Array, DOE,\ud ANNOVA, Fuzzy Logic, and Response Parameters etc. were learned from the Published papers\ud and it helped extensively in gaining knowledge required for the experiments to be performed on\ud our project