scholarly journals Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
End Milling ◽  
Cutting Tools ◽  
Computing Methods ◽  
Milling Process ◽  
Dry Cutting ◽  
Inference System ◽  
Cutting Processes ◽  
Adaptive Neurofuzzy Inference System

Surface roughness is considered as the quality index of the machine parts. Many diverse techniques have been applied in modelling metal cutting processes. Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end millingTi6Al4Valloy with coated (PVD) and uncoated cutting tools under dry cutting conditions. Real experimental results have been used for training and testing ofANFISmodels, and the best model was selected based on minimum root mean square error. A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5. The findings provided evidence of the capability ofANFISin modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results.

Investigating the Effect of Cutting Speed Variation on Surface Roughness of 7136 Aluminum Alloy in End Milling

2015 ◽  
Vol 809-810 ◽  
pp. 129-134 ◽  
Author(s):  
Alina Bianca Bonţiu Pop ◽  
Mircea Lobonţiu
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Metal Cutting ◽  
End Milling ◽  
Cutting Speed ◽  
Processing Parameters ◽  
Milling Process ◽  
Future Research ◽  
Cutting Depth ◽  
Speed Variation

Surface quality is affected by various processing parameters and inherent uncertainties of the metal cutting process. Therefore, the surface roughness anticipation becomes a real challenge for engineers and researchers. In previous researches [1] I have investigated the feed rate influence on surface roughness and manufacturing time reduction. The 7136 aluminum alloy was machined by end milling operation using standard tools for aluminum machining. The purpose of this paper is to identify by experiments the influence of cutting speed variation on surface roughness. The scientific contribution brought by this research is the improvement of the end milling process of 7136 aluminum alloy. This material is an aluminum alloy developed by Universal Alloy Corporation and is used in the aircraft industry to manufacture parts from extruded profiles. The research method used to solve the problem is experiment. A range of cutting speeds was used while the cutting depth and the feed per tooth were constrained per minimum and maximum requirements defined for the given cutting tool. The experiment was performed by using a 16 mm End milling cutter, holding two indexable cutting inserts. The machine used for the milling tests was a HAAS VF2 CNC. The surface roughness (response) was measured by using a portable surface roughness tester (TESA RUGOSURF 20 Portable Surface Finish Instrument). Following the experimental research, results were obtained which highlight the cutting speed influence on surface roughness. Based on these results we created roughness variation diagrams according to the cutting speed for each value of feed per tooth and cutting depth. The final results will be used as data for future research.

scholarly journals A Cyber-Physical System for Surface Roughness Monitoring in End-Milling

2019 ◽  
Author(s):  
Uroš Župerl ◽  
Franci Čuš
Keyword(s):  
Surface Roughness ◽  
Process Control ◽  
Neural Control ◽  
End Milling ◽  
Milling Process ◽  
Machining Parameters ◽  
Inference System ◽  
Machining System ◽  
Chip Size ◽  
Roughness Control

A cyber-psychical machining system (CPMS) is developed to realize smart end-milling process monitoring. The CPMS provides a novel way for controlling the cutting chip size and monitoring the surface roughness in milling processes through Internet of Things (IoT) applications. The two level CPMS is realized by linking the IoT machining platform for process control to the machine tool with integrated visual system (VS). The VS is employed to acquire the signals of the cutting chip size during the machining of difficult to cut materials. The machining platform performs instant chip size and surface roughness control based on advanced signal processing, edge computing, modeling and cognitive corrective process control acting. A cognitive neural control system (CNCS) is employed to control the chip size by modifying the machining parameters and consequently maintaining surface roughness constant. An adaptive neural inference system (ANFIS) is applied to precisely model and in-process predict the surface roughness. Machining tests conducted using the proposed CPMS indicate that the cutting chip size and consequently the produced surface roughness are well maintained when the cutting-depth profile of a workpiece is varying step-wise or continuously.

Investigation of Dry Cutting and Applications of Cutting Fluid in Ball-End Milling Process

2011 ◽  
Vol 291-294 ◽  
pp. 3013-3023 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Channarong Rungruang
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
End Milling ◽  
Cutting Parameters ◽  
Milling Process ◽  
Cutting Fluid ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Ball End Milling ◽  
End Milling Process

In order to realize the environmental hazard, this paper presents the investigation of the machinability of ball-end milling process with the dry cutting, the wet cutting, and the mist cutting for aluminum. The relations of the surface roughness, the cutting force, and the cutting parameters are examined based on the experimental results by using the Response Surface Analysis with the Box-Behnken design. The in-process cutting force is monitored to analyze the relations of the surface roughness and the cutting parameters. The proper cutting condition can be determined easily referring to the minimum use of cutting fluid, and the minimum surface roughness and cutting force of the surface plot. The effectiveness of the obtained surface roughness and cutting force models have been proved by utilizing the analysis of variance at 95% confident level.

Hard Nano-Crystalline Coatings for Cutting Tools

2007 ◽  
Vol 567-568 ◽  
pp. 185-188 ◽  
Author(s):  
Miroslav Piska
Keyword(s):  
High Speed ◽  
Metal Cutting ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Dry Cutting ◽  
Nano Crystalline ◽  
The Right ◽  
Machining Productivity ◽  
Hard Cutting ◽  
Cutting Tool Materials

Modern trends in metal cutting, high speed/feed machining, dry cutting and hard cutting set more demanding characteristics for cutting tool materials. The exposed parts of the cutting edges must be protected against the severe loading conditions and wear. The most significant coatings methods for cutting tools are PVD and CVD/MTCVD today. The choice of the right substrate or the right protective coating in the specific machining operation can have serious impact on machining productivity and economy. In many cases the deposition of the cutting tool with a hard coating increases considerably its cutting performance and tool life. The coating protects the tool against abrasion, adhesion, diffusion, formation of comb cracks and other wear phenomena.

scholarly journals Influence of Chatter of VMC Arising During End Milling Operation and Cutting Conditions on Quality of Machined Surface

1970 ◽  
Vol 2 (1) ◽  
Author(s):  
A.K.M.N. Amin, M.A. Rizal, and M. Razman
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
Metal Removal ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Conditions ◽  
End Mill ◽  
Machined Surface ◽  
Operating Cycle ◽  
Wide Range

Machine tool chatter is a dynamic instability of the cutting process. Chatter results in poor part surface finish, damaged cutting tool, and an irritating and unacceptable noise. Exten¬sive research has been undertaken to study the mechanisms of chatter formation. Efforts have been also made to prevent the occurrence of chatter vibration. Even though some progress have been made, fundamental studies on the mechanics of metal cutting are necessary to achieve chatter free operation of CNC machine tools to maintain their smooth operating cycle. The same is also true for Vertical Machining Centres (VMC), which operate at high cutting speeds and are capable of offering high metal removal rates. The present work deals with the effect of work materials, cutting conditions and diameter of end mill cutters on the frequency-amplitude characteristics of chatter and on machined surface roughness. Vibration data were recorded using an experimental rig consisting of KISTLER 3-component dynamometer model 9257B, amplifier, scope meters and a PC.  Three different types of vibrations were observed. The first type was a low frequency vibration, associated with the interrupted nature of end mill operation. The second type of vibration was associated with the instability of the chip formation process and the third type was due to chatter. The frequency of the last type remained practically unchanged over a wide range of cutting speed.  It was further observed that chip-tool contact processes had considerable effect on the roughness of the machined surface.Key Words: Chatter, Cutting Conditions, Stable Cutting, Surface Roughness.

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Ball End Milling ◽  
Roughness Model ◽  
Force Ratio ◽  
Tool Diameter ◽  
End Milling Process

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

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