scholarly journals Influence of Cutting Parameters and Tool Path Strategies on Surface Roughness in Pocket Milling of UNS A96082 Alloy

2019 ◽  
Vol 9 (1) ◽  
pp. 6241-6245
Keyword(s):  
Surface Roughness ◽  
Tool Path ◽  
Design Method ◽  
Cutting Parameters ◽  
Quality Parameters ◽  
Machining Process ◽  
Pocket Milling ◽  
Measuring Surface ◽  
Experimental Design Method ◽  
Step Over

CNC Pocket Milling is the commonly employed machining process in molding, aircraft building, and shipbuilding industries. Surface integrity is one of the main quality parameters considered to accept the component. In the present work, an attempt is made to optimize the cutting factors in pocket milling of UNS A96082 using two different tool path strategies to get a better surface finish. Speed, Feed and Step over are considered as influencing parameters in the present study for measuring surface roughness. Taguchi experimental design method is employed to conduct experiments and to optimize the cutting parameters. It is observed that the results obtained from confirmation experiments also show nearer value for predicted surface roughness

scholarly journals The Effect of the Feed Direction on the Micro- and Macro Accuracy of 3D Ball-end Milling of Chromium-Molybdenum Alloy Steel

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4038
Author(s):  
Balázs Mikó ◽  
Bálint Varga ◽  
Wojciech Zębala
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
Tool Path ◽  
End Milling ◽  
Cutting Parameters ◽  
Machining Process ◽  
Free Form ◽  
Dimensional Error ◽  
Ball End Milling ◽  
Free Form Surfaces

The machining of free form surfaces is one of the most challenging problems in the field of metal cutting technology. The produced part and machining process should satisfy the working, accuracy, and financial requirements. The accuracy can describe dimensional, geometrical, and surface roughness parameters. In the current article, three of them are investigated in the case of the ball-end milling of a convex and concave cylindrical surface form 42CrMo4 steel alloy. The effect of the tool path direction is investigated and the other cutting parameters are constant. The surface roughness and the geometric error are measured by contact methods. Based on the results, the surface roughness, dimensional error, and the geometrical error mean different aspects of the accuracy, but they are not independent from each other. The investigated input parameters have a similar effect on them. The regression analyses result a very good liner regression for geometric errors and shows the importance of surface roughness.

scholarly journals Influence of Tool Path Strategies and Pocket Geometry on Surface Roughness in Pocket Milling

2020 ◽  
Vol 9 (2) ◽  
pp. 884-888
Keyword(s):  
Surface Roughness ◽  
Tool Path ◽  
Cutting Tool ◽  
Cutting Parameters ◽  
Milling Process ◽  
End Mill ◽  
Roughness Measurement ◽  
Machining Processes ◽  
Pocket Milling ◽  
Carbide Insert

This paper discusses the effect of tool path strategies and pocket geometry to surface roughness due to pocket milling process. The machining processes have been performed on mould steel DF2 using carbide insert end mill as the cutting tool. The cutting parameters for this experiment were kept constant while the variables were cutting tool, path strategies and pocket geometries at three levels each. The effectiveness of different tool path strategy and different pocket geometry is evaluated in terms of measured surface roughness (Ra) of the workpiece. The grade of a pocket is directly proportional with its surface roughness. The lowest surface roughness measurement was produced by pocket geometry B with parallel spiral cutting tool path strategy.

Studying The Effect of a New Mixed Cutting Fluid on Surface Roughness

2020 ◽  
Vol 38 (11A) ◽  
pp. 1593-1601
Author(s):  
Mohammed H. Shaker ◽  
Salah K. Jawad ◽  
Maan A. Tawfiq
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Cutting Parameters ◽  
Machining Process ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Machining Processes ◽  
Dry Cutting ◽  
Cutting Speeds

This research studied the influence of cutting fluids and cutting parameters on the surface roughness for stainless steel worked by turning machine in dry and wet cutting cases. The work was done with different cutting speeds, and feed rates with a fixed depth of cutting. During the machining process, heat was generated and effects of higher surface roughness of work material. In this study, the effects of some cutting fluids, and dry cutting on surface roughness have been examined in turning of AISI316 stainless steel material. Sodium Lauryl Ether Sulfate (SLES) instead of other soluble oils has been used and compared to dry machining processes. Experiments have been performed at four cutting speeds (60, 95, 155, 240) m/min, feed rates (0.065, 0.08, 0.096, 0.114) mm/rev. and constant depth of cut (0.5) mm. The amount of decrease in Ra after the used suggested mixture arrived at (0.21µm), while Ra exceeded (1µm) in case of soluble oils This means the suggested mixture gave the best results of lubricating properties than other cases.

Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations

2011 ◽  
Vol 383-390 ◽  
pp. 1062-1070
Author(s):  
Adeel H. Suhail ◽  
N. Ismail ◽  
S.V. Wong ◽  
N.A. Abdul Jalil
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Machining Process ◽  
Data Selection ◽  
Adaptive Network ◽  
Inference System ◽  
Single Input ◽  
Machinability Data

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

Effect of Cutting Parameters on Surface Roughness in Turning of Bone

2013 ◽  
Vol 845 ◽  
pp. 708-712 ◽  
Author(s):  
P.Y.M. Wibowo Ndaruhadi ◽  
S. Sharif ◽  
M.Y. Noordin ◽  
Denni Kurniawan
Keyword(s):  
Surface Roughness ◽  
Bone Tissue ◽  
Influencing Factor ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Machining Process ◽  
Turning Operation ◽  
Cutting Direction

Surface roughness indicates the damage of the bone tissue due to bone machining process. Aiming at inducing the least damage, this study evaluates the effect of some cutting conditions to the surface roughness of machined bone. In the turning operation performed, the variables are cutting speed (26 and 45 m/min), feed (0.05 and 0.09 mm/rev), tool type (coated and uncoated), and cutting direction (longitudinal and transversal). It was found that feed did not significantly influence surface roughness. Among the influencing factor, the rank is tool type, cutting speed, and cutting direction.

OPTIMISING CUTTING PARAMETERS FOR HOT TURNING ON EN31 MATERIAL

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Prof. Hemant k. Baitule ◽  
Satish Rahangdale ◽  
Vaibhav Kamane ◽  
Saurabh Yende
Keyword(s):  
Surface Roughness ◽  
Surface Finish ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Machining Process ◽  
Depth Of Cut ◽  
Multiple Time ◽  
Turning Process ◽  
Workpiece Material ◽  
Hot Turning

In any type of machining process the surface roughness plays an important role. In these the product is judge on the basis of their (surface roughness) surface finish. In machining process there are four main cutting parameter i.e. cutting speed, feed rate, depth of cut, spindle speed. For obtaining good surface finish, we can use the hot turning process. In hot turning process we heat the workpiece material and perform turning process multiple time and obtain the reading. The taguchi method is design to perform an experiment and L18 experiment were performed. The result is analyzed by using the analysis of variance (ANOVA) method. The result Obtain by this method may be useful for many other researchers.

Chatter Prediction Based on NC Physical Simulation in Machining Ti6Al4V Thin-Walled Components

2013 ◽  
Vol 395-396 ◽  
pp. 1008-1014
Author(s):  
Yu Li ◽  
Chao Sun
Keyword(s):  
Tool Path ◽  
Physical Simulation ◽  
Prediction Method ◽  
Cutting Parameters ◽  
Simulation Software ◽  
Cnc Machining ◽  
Machining Process ◽  
Chatter Stability ◽  
Chatter Prediction ◽  
Thin Walled

Chatter has been a problem in CNC machining process especially during machining thin-walled components with low stiffness. For accurately predicting chatter stability in machining Ti6Al4V thin-walled components, this paper establishes a chatter prediction method considering of cutting parameters and tool path. The fast chatter prediction method for thin-walled components is based on physical simulation software. Cutting parameters and tool path is achieved through the chatter stability lobes test and finite element simulation. Machining process is simulated by the physical simulation software using generated NC code. This proposed method transforms the NC physical simulation toward the practical methodology for the stability prediction over the multi-pocket structure milling.

A novel approach to machining process fault detection using unsupervised learning

2020 ◽  
pp. 095440542093755
Author(s):  
Thomas McLeay ◽  
Michael S Turner ◽  
Keith Worden
Keyword(s):  
Fault Detection ◽  
Unsupervised Learning ◽  
Tool Path ◽  
Cutting Tool ◽  
Novelty Detection ◽  
Cutting Parameters ◽  
Machining Process ◽  
Machining Processes ◽  
Workpiece Material ◽  
Data Set

The most common machining processes of turning, drilling, milling and grinding concern the removal of material from a workpiece using a cutting tool. The performance of machining processes depends on a number of key method parameters, including cutting tool, workpiece material, machine configuration, fixturing, cutting parameters and tool path trajectory. The large number of possible configurations can make it difficult to implement fault detection systems without having to train the system to a particular method or fault type. The research of this article applies a novel method to detect the changing state of a process over time in order to detect faulty machining conditions such as worn tools and cutting depth changes. Unlike studies in the previous literature in this domain, an unsupervised learning method is used, so that the method can be applied in production to unfamiliar processes or fault conditions. In the case presented, novelty detection is applied to a multivariate sensor feature data set obtained from a milling process. Sensor modalities include acoustic emission, vibration and spindle power and time and frequency domain features are employed. The Mahalanobis squared-distance is used to measure discordancy of each new data point, and values that exceed a principled novelty threshold are categorised as fault conditions.

Optimisation of the Bauer Equation Using the Least Squares Method for Thermoplastics Turning

2018 ◽  
Vol 8 (1) ◽  
pp. 21-36
Author(s):  
János Farkas ◽  
Etele Csanády ◽  
Levente Csóka
Keyword(s):  
Surface Roughness ◽  
Least Squares Method ◽  
Composite Ceramic ◽  
Cutting Parameters ◽  
Machining Process ◽  
Tool Geometry ◽  
Machining Processes ◽  
Simple Formulation ◽  
Plastic Composite ◽  
Cutting Speeds

A number of equations are available for predicting the output of machining processes. These equations are most commonly used for the prediction of surface roughness after tooling. Surface roughness can be influenced by many factors, including cutting parameters, tool geometry and environmental factors such as the coolant used. It is difficult to create a universally applicable equation for all machining because of the variations in different materials' behaviours (e.g. metal, wood, plastic, composite, ceramic). There are also many differences between the various types of machining process such as the machining tools, rotational or translational movements, cutting speeds, cutting methods, etc. The large number of parameters required would make such an equation unusable, and difficult to apply quickly. The goal is thus to create a simple formulation with three or four inputs to predict the final surface roughness of the machined part within adequate tolerances. The two main equations used for this purpose are the Bauer and Brammertz formulas, both of which need to be optimised for a given material. In this paper, the turning of thermoplastics was investigated, with the aim of tuning the Bauer formula for use with thermoplastics. Eleven different plastics were used to develop a material-dependent surface roughness equation. Only new tooling inserts were used to eliminate the effects of tool wear.

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