Prediction of Forces, Torque and Power in Face Milling Operations

1996 ◽  
pp. 97-105 ◽  
Author(s):  
E. J. A. Armarego ◽  
J. Wang
Keyword(s):  
Face Milling ◽  
Milling Operations

scholarly journals Optimum Selection of Variable Pitch for Chatter Suppression in Face Milling Operations

Materials ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 112 ◽  
Author(s):  
Alex Iglesias ◽  
Zoltan Dombovari ◽  
German Gonzalez ◽  
Jokin Munoa ◽  
Gabor Stepan
Keyword(s):  
Removal Rate ◽  
Face Milling ◽  
Heavy Duty ◽  
Variable Pitch ◽  
Thin Walls ◽  
Chatter Suppression ◽  
Milling Operations ◽  
Optimum Selection ◽  
Cutting Capacity ◽  
Selection Of

Cutting capacity can be seriously limited in heavy duty face milling processes due to self-excited structural vibrations. Special geometry tools and, specifically, variable pitch milling tools have been extensively used in aeronautic applications with the purpose of removing these detrimental chatter vibrations, where high frequency chatter related to slender tools or thin walls limits productivity. However, the application of this technique in heavy duty face milling operations has not been thoroughly explored. In this paper, a method for the definition of the optimum angles between inserts is presented, based on the optimum pitch angle and the stabilizability diagrams. These diagrams are obtained through the brute force (BF) iterative method, which basically consists of an iterative maximization of the stability by using the semidiscretization method. From the observed results, hints for the selection of the optimum pitch pattern and the optimum values of the angles between inserts are presented. A practical application is implemented and the cutting performance when using an optimized variable pitch tool is assessed. It is concluded that with an optimum selection of the pitch, the material removal rate can be improved up to three times. Finally, the existence of two more different stability lobe families related to the saddle-node and flip type stability losses is demonstrated.

The creation of new research system for the investigation of surface quality by stones

2013 ◽  
Vol 4 (1) ◽  
pp. 43-48
Author(s):  
I. G. Gyurika ◽  
M. Gálos
Keyword(s):  
Research Team ◽  
Face Milling ◽  
Average Surface Roughness ◽  
Research System ◽  
Machining Processes ◽  
Average Surface ◽  
Milling Operations ◽  
University Of Technology ◽  
New Research ◽  
Cutting Speeds

Abstract The research on automated stone machining processes was very significant in the last two decades. Sawing, cutting and grinding of different stones like granite, marble, limestone became cheaper and more productive because of the results of researches. When searching through international specialised literature in the topic of stone machining with machine centres, theoretical summaries or researches can hardly be found. The aim of the researchers writing this article is — as a pioneer in Hungary, but also among the first internationally — to examine the optimization and technological problems in the area of stone milling processes. The researchers have developed a complex research system with the collaboration of two departments of University of Technology and Economics and an industrial stone machining firm, Woldem Ltd. to solve the problems. This paper summarizes the parts of this system. General steps and results of research processes are demonstrated by reference experiments. Face milling operations were made on a granite block with five different cutting speeds and then the researchers measured slip safety and average surface roughness values in case of different samples. Finally, upcoming tasks of the research team are summarized.

scholarly journals Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel

2019 ◽  
Vol 105 (5-6) ◽  
pp. 2151-2165 ◽  
Author(s):  
Adel Taha Abbas ◽  
Danil Yurievich Pimenov ◽  
Ivan Nikolaevich Erdakov ◽  
Tadeusz Mikolajczyk ◽  
Mahmoud Sayed Soliman ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Unit Volume ◽  
High Strength ◽  
Face Milling ◽  
Production Costs ◽  
The Other ◽  
Cutting Conditions ◽  
Milling Operations ◽  
Artificial Neural ◽  
Pareto Method

Abstract Computer Numerical Control (CNC) face milling is commonly used to manufacture products from high-strength grade-H steel in both the automotive and the construction industry. The various milling operations for these components have key performance indicators: accuracy, surface roughness (Ra), and machining time for removal of a unit volume min/cm3 (Tm). The specified surface roughness values for machining each component is achieved based on the prototype specifications. However, poor adherence to specifications can result in the rejection of the machined parts, implying extra production costs and raw material wastage. An algorithm using an artificial neural network (ANN) with the Edgeworth-Pareto method is presented in this paper to optimize the cutting parameter in CNC face-milling operations. The set of parameters are adjusted to improve surface roughness and minimal unit-volume material removal rates, thereby reducing production costs and improving accuracy. An ANN algorithm is designed in Matlab, based on a 3–10-1 Multi-Layer Perceptron (MLP), which predicts the Ra of the workpiece surface to an accuracy of ± 5.78% within the range of the experimental angular spindle speed, feed rate, and cutting depth. An unprecedented Pareto frontier for Ra and Tm was obtained for the finished grade-H steel workpiece using an ANN algorithm that was then used to determine optimized cutting conditions. Depending on the production objective, one or the other of two sets of optimum machining conditions can be used: the first one sets a minimum cutting power, while the other sets a maximum Tm with a slight increase (under 5%) in milling costs.

Improvement of surface roughness models for face milling operations through dimensionality reduction

2012 ◽  
Vol 19 (2) ◽  
pp. 179-197 ◽  
Author(s):  
Maciej Grzenda ◽  
Andres Bustillo ◽  
Guillem Quintana ◽  
Joaquim Ciurana
Keyword(s):  
Surface Roughness ◽  
Dimensionality Reduction ◽  
Face Milling ◽  
Milling Operations

scholarly journals Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Gustavo M. Minquiz ◽  
Vicente Borja ◽  
Marcelo López-Parra ◽  
Alejandro C. Ramírez-Reivich ◽  
Leopoldo Ruiz-Huerta ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Linear Equations ◽  
Cutting Parameters ◽  
Face Milling ◽  
Machining Parameters ◽  
Significance Level ◽  
Manufacturing Operations ◽  
Milling Operations ◽  
Energy Consumption Optimization ◽  
Common Activity

Very commonly, a mechanical workpiece manufactured industrially includes more than one machining operation. Even more, it is a common activity of programmers, who make a decision in this regard every time a milling and drilling operation is performed. This research is focused on better understanding the power behavior for face milling and drilling manufacturing operations, and the methodology followed was the design of experiments (DOEs) with the cutting parameters set in combination with toolpath evaluation available in commercial software, having as main goal to get a predictive power equation validated in two ways, linear or nonlinear, and understanding the energy consumption and the quality surface in face milling and final diameter in drilling. The results show that it is possible to find difference in a power demand of 1.52 kW to 3.9 kW in the same workpiece, depending on the operations (face milling or drilling), cutting parameters, and toolpath chosen. Additionally, the equations modelled showed acceptable values to predict the power, with p values higher than 0.05 which is the significance level for the nonlinear and linear equations with an R square predictive of 98.36. Some conclusions established that optimization of the cutting parameters combined with toolpath strategies can represent an energy consumption optimization higher than 0.21% and the importance to try to find an energy consumption balance when a workpiece has different milling operations.

On the Efficiency of NC Tool Path Planning for Face Milling Operations

1987 ◽  
Vol 109 (4) ◽  
pp. 370-376 ◽  
Author(s):  
H. Wang ◽  
H. Chang ◽  
R. A. Wysk ◽  
A. Chandawarkar
Keyword(s):  
Tool Path ◽  
Face Milling ◽  
Path Selection ◽  
Manufacturing Industries ◽  
Control Parameters ◽  
Sweep Angle ◽  
Window Frame ◽  
Milling Operations ◽  
Starting Point ◽  
The Impact

Face milling operations are important for producing flat machined surfaces. Existing methodologies to generate NC tool path for face milling are employed everyday by manufacturing industries without considering the impact of tool path selection. A systematic study has been conducted in order to identify those critical control parameters affecting length of cut in face milling operations. Basic polygons, from triangles to heptagons, were planned using both window frame and stair case milling procedures. For each polygon, each vertex was introduced as a possible starting point for a single run in the window frame milling. The cutting orientation was further examined for stair case milling by enumerating the tool sweep angle from 0 to 180 (and 180 to 0 as well) degrees from each vertex. The results are analyzed and important conclusions are drawn noting the efficiency of the cutting process.

Prediction of Surface Roughness Profiles for Milling Process with Fractal Parameters Based on LS-SVM

2010 ◽  
Vol 97-101 ◽  
pp. 1186-1193 ◽  
Author(s):  
Ben Gan ◽  
Yi Jian Huang ◽  
Gui Xia Zheng
Keyword(s):  
Surface Roughness ◽  
Cutting Parameters ◽  
Milling Process ◽  
Face Milling ◽  
Cutting Conditions ◽  
Vertical Scaling ◽  
Support Vector ◽  
Fractal Function ◽  
Milling Operations ◽  
Fractal Parameters

Least squares support vector machines (LS-SVM) were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the LS-SVM are the cutting parameters: rotational speed, feed, depth of milling. The output parameters of the LS-SVM are the corresponding calculated fractal parameters: fractal dimension D and vertical scaling parameter G. The LS-SVM were utilized successfully for training and predicting the fractal parameters D and G in face milling operations. Moreover, Weierstrass-Mandelbrot（W–M ）fractal function was integrated with the LS-SVM in order to generate an artificially fractal predicted profiles at different milling conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens and there is a relationship between the scale-independent fractal coefficients(D and G).

scholarly journals A Soft Computing System to Perform Face Milling Operations

2009 ◽  
pp. 1282-1291 ◽  
Author(s):  
Raquel Redondo ◽  
Pedro Santos ◽  
Andres Bustillo ◽  
Javier Sedano ◽  
José Ramón Villar ◽  
...  
Keyword(s):  
Soft Computing ◽  
Computing System ◽  
Face Milling ◽  
Milling Operations
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