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.

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).

A Comparative Analysis Between High Speed Dynamic and Traditional Pocketing Toolpaths in Precision Milling Machines

2016 ◽  
Author(s):  
Gustavo M. Minquiz ◽  
Vicente Borja ◽  
Marcelo López-Parra ◽  
David Dornfeld ◽  
Pablo Flores
Keyword(s):  
Energy Consumption ◽  
Comparative Analysis ◽  
Tool Wear ◽  
Carbon Emissions ◽  
High Speed ◽  
Face Milling ◽  
High Speeds ◽  
Milling Operations ◽  
Different Types ◽  
Milling Machines

Different types of toolpaths have been extensively studied with regards to different factors such as energy consumption and tool wear. However, toolpaths have been introduced recently, where high speeds and dynamic movements are combined to provide higher performance. The aim of this paper is to compare a spiral toolpath strategy, which has been studied previously with good results in energy consumption, with a high speed dynamic toolpath strategy, which combines helical and dynamic movements, with regards to energy consumption, tool wear and carbon emissions. Several advantages are identified with a high speed dynamic toolpath strategy over the typical spiral toolpath strategy in terms of tool wear, energy consumption and carbon emissions. The results show that the high speed dynamic toolpath is a better alternative for different milling operations such as slotting, pocketing, and face milling.

scholarly journals PENENTUAN KECEPATAN PEMOTONGAN EFEKTIF BERDASARKAN NILAI KEKASARAN PERMUKAAN MATERIAL AA-7075 PADA PROSES FACE MILLING

POROS ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 51
Author(s):  
Sobron Y. Lubis ◽  
Rosehan Rosehan ◽  
Musa Law
Keyword(s):  
Surface Roughness ◽  
Surface Quality ◽  
Feed Rate ◽  
Tooth Development ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Face Milling ◽  
Machining Parameters ◽  
Aa 7075 ◽  
Aluminum Alloy 7075

During face milling machining, several machining parameters such as feed rate and cuttingspeed determine the surface quality of the workpiece produced by the process. The selection of the rightparameters will lead to the surface quality as planned. Therefore, to improve machining effectiveness, amethod is needed to determine the appropriate machining parameters to produce the desired surfacequality. This research was conducted using a milling machine, five variations of cutting speed and fivevariations of feed rate were used to cut the workpiece aluminum alloy 7075. After machining, the surfaceroughness was measured using a surface test. The surface roughness value is then substituted into thefeed rate equation and effective cutting speed. By finding effective cutting parameters, the machiningprocess will be more efficient and effective without using unnecessary resources. From the results of thestudy note that the development equation to determine the feed rate based on the value of surfaceroughness is ???? = 0,6????√???? ????????0.443mm/tooth. Development equation to determine the effective cutting speedbased on Surface roughness value is ???????? = 3.0686????????0.124 mm/min

Evaluation of Face Milling Operation Parameters on Surface Roughness of Crankcase Housing by Two Level Factorial Design with Center Points

2018 ◽  
Vol 780 ◽  
pp. 105-110
Author(s):  
Ukrit Thanasuptawee ◽  
Chamrat Thakhamwang ◽  
Somsak Siwadamrongpong
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Milling Process ◽  
Face Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Significance Level ◽  
Machining Center ◽  
Semi Solid

In this study, there are three machining parameters consist of spindle speed, feed rate and depth of cut which were conducted through full factorial with four center points to determine the effect of machining parameters on the surface roughness and verify whether there is curvature in the model for CNC face milling process in an automotive components manufacturer in Thailand. The workpieces used semi-solid die casted ADC12 aluminum alloy crankcase housing which they were performed by the ARES SEIKI model R5630 3-axis CNC vertical machining center and face milling cutter with diameter of 63 millimeters. The surface roughness of face-milled was measured by the surface roughness tester. It was found that the greatest main effect influence to surface roughness was spindle speed, followed by feed rate and depth of cut at significance level of 0.05.

Optimization of machining economics and energy consumption in face milling operations

2018 ◽  
Vol 99 (9-12) ◽  
pp. 2093-2100 ◽  
Author(s):  
Yi-Chi Wang ◽  
Dong-Won Kim ◽  
Hiroshi Katayama ◽  
Wen-Chin Hsueh
Keyword(s):  
Energy Consumption ◽  
Face Milling ◽  
Milling Operations ◽  
Machining Economics

Optimal Selection of Machining Parameters by Genetic Algorithms for Face-Milling Operations Based on Minimum Unit Machining Time

2012 ◽  
Vol 602-604 ◽  
pp. 1989-1992 ◽  
Author(s):  
Pei Qing Yang ◽  
Li Bao An
Keyword(s):  
Face Milling ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Machining Time ◽  
Milling Operations ◽  
Tool Replacement ◽  
Optimal Values ◽  
Cutting Speeds ◽  
Selection Of

In this paper, the parameter optimization problem for face-milling operations is studied. A mathematical model is developed in order to minimize unit machining time. The machining process needs one finishing pass and at least one roughing pass depending on the total depth of cut. Maximum and minimum allowable cutting speeds, feed rates and depths of cut, as well as tool life, surface roughness, cutting force and cutting power consumption are constraints of the model. Optimal values of machining parameters are found by a genetic algorithm (GA). The influence of tool replacement time and GA operators is evaluated.

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