Case Example 7: Monitoring and predicting surface roughness and bore tolerance in end-milling

1999 ◽  
pp. 379-406
Author(s):  
A. Chukwujekwu Okafor
Keyword(s):  
Surface Roughness ◽  
End Milling

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Parameter Optimization of Ball End Milling Process on Inconel 718 Using RSM and TLBO Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Nandkumar N. Bhopale ◽  
Nilesh Nikam ◽  
Raju S. Pawade
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Integrity ◽  
Inconel 718 ◽  
Tool Path ◽  
End Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Ball End Milling

AbstractThis paper presents the application of Response Surface Methodology (RSM) coupled with Teaching Learning Based Optimization Technique (TLBO) for optimizing surface integrity of thin cantilever type Inconel 718 workpiece in ball end milling. The machining and tool related parameters like spindle speed, milling feed, axial depth of cut and tool path orientation are optimized with considerations of multiple response like deflection, surface roughness, and micro hardness of plate. Mathematical relationship between process parameters and deflection, surface roughness and microhardness are found out by using response surface methodology. It is observed that after optimizing the process that at the spindle speed of 2,000 rpm, feed 0.05 mm/tooth/rev, plate thickness of 5.5 mm and 15° workpiece inclination with horizontal tool path gives favorable surface integrity.

A Comprehensive Study on Surface Roughness in Machining of AISI D2 Hardened Steel

2012 ◽  
Vol 576 ◽  
pp. 60-63 ◽  
Author(s):  
N.A.H. Jasni ◽  
Mohd Amri Lajis
Keyword(s):  
Surface Roughness ◽  
High Speed ◽  
End Milling ◽  
Cutting Speed ◽  
Hardened Steel ◽  
Depth Of Cut ◽  
Feed Speed ◽  
Radial Depth ◽  
Aisi D2 ◽  
Coated Carbide

Hard milling of hardened steel has wide application in mould and die industries. However, milling induced surface finish has received little attention. An experimental investigation is conducted to comprehensively characterize the surface roughness of AISI D2 hardened steel (58-62 HRC) in end milling operation using TiAlN/AlCrN multilayer coated carbide. Surface roughness (Ra) was examined at different cutting speed (v) and radial depth of cut (dr) while the measurement was taken in feed speed, Vf and cutting speed, Vc directions. The experimental results show that the milled surface is anisotropic in nature. Surface roughness values in feed speed direction do not appear to correspond to any definite pattern in relation to cutting speed, while it increases with radial depth-of-cut within the range 0.13-0.24 µm. In cutting speed direction, surface roughness value decreases in the high speed range, while it increases in the high radial depth of cut. Radial depth of cut is the most influencing parameter in surface roughness followed by cutting speed.

Surface Roughness Identification in End Milling Using Vibration Signals and Fuzzy Clustering

2016 ◽  
Author(s):  
Issam Abu-Mahfouz ◽  
Amit Banerjee ◽  
A. H. M. Esfakur Rahman
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Wavelet Transforms ◽  
Fourier Transforms ◽  
End Milling ◽  
Cutting Conditions ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Machined Surface ◽  
Vibration Signals

The study presented involves the identification of surface roughness in Aluminum work pieces in an end milling process using fuzzy clustering of vibration signals. Vibration signals are experimentally acquired using an accelerometer for varying cutting conditions such as spindle speed, feed rate and depth of cut. Features are then extracted by processing the acquired signals in both the time and frequency domain. Techniques based on statistical parameters, Fast Fourier Transforms (FFT) and the Continuous Wavelet Transforms (CWT) are utilized for feature extraction. The surface roughness of the machined surface is also measured. In this study, fuzzy clustering is used to partition the feature sets, followed by a correlation with the experimentally obtained surface roughness measurements. The fuzzifier and the number of clusters are varied and it is found that the partitions produced by fuzzy clustering in the vibration signal feature space are related to the partitions based on cutting conditions with surface roughness as the output parameter. The results based on limited simulations are encouraging and work is underway to develop a larger framework for online cutting condition monitoring system for end milling.

Prediction of Surface Roughness in Compressed Air Jet Assisted High Speed End Milling of Silicon Using Diamond Coated Tools

2012 ◽  
Vol 576 ◽  
pp. 41-45
Author(s):  
A.K.M. Nurul Amin ◽  
M.A. Mahmud ◽  
M.D. Arif
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Surface Finish ◽  
High Speed ◽  
End Milling ◽  
Silicon Wafers ◽  
Compressed Air ◽  
Machining Parameters ◽  
High Quality ◽  
Ductile Mode

The majority of semiconductor devices are made up of silicon wafers. Manufacturing of high-quality silicon wafers includes numerous machining processes, including end milling. In order to end mill silicon to a nano-meteric surface finish, it is crucial to determine the effect of machining parameters, which influence the machining transition from brittle to ductile mode. Thus, this paper presents a novel experimental technique to study the effects of machining parameters in high speed end milling of silicon. The application of compressed air, in order to blow away the chips formed, is also investigated. The machining parameters’ ranges which facilitate the transition from brittle to ductile mode cutting as well as enable the attainment of high quality surface finish and integrity are identified. Mathematical model of the response parameter, the average surface roughness (Ra) is subsequently developed using RSM in terms of the machining parameters. The model was determined, by Analysis of Variance (ANOVA), to have a confidence level of 95%. The experimental results show that the developed mathematical model can effectively describe the performance indicators within the controlled limits of the factors that are being considered.

Surface Roughness Analysis for Radius End Mill in Cross Feed Direction Both Contour and Scanning Line Machinings

2020 ◽  
Author(s):  
Hirohisa Narita
Keyword(s):  
Surface Roughness ◽  
Inclination Angle ◽  
End Milling ◽  
Contour Line ◽  
End Mill ◽  
Good Surface ◽  
Corner Radius ◽  
Feed Direction ◽  
Scanning Line ◽  
Influence Degree

Abstract An optimum experimental condition, which realize good surface roughness in cross direction both contour and scanning lines, for radius end mill against some inclined surfaces is obtained and some features is these cutting processes is discussed in this paper. The optimum experimental condition, which consists of cutting type (or feed direction), spindle speed, feed rate, depth of immersion, inclination angle, corner radius of end mill and cross feed, is obtained and the influence degree of these parameters is calculated by using Taguchi method. The experiment is carried out based on L18 orthogonal array. Based on the influence degree and geometric contact status due to unique shape of radius end mill, some feature of radius end milling is introduced. As a result of the contour line machining, a scallop height is very influenced by the inclination angle and the corner radius, and surface machined by bottom edge must not be remained. Regarding the scanning line machining, “go-up” is good for the feed direction. Big corner radius is also suitable because side edge does not contact to workpiece. In other words, the cutting force in radial direction becomes small. Furthermore, the surface roughness of the scanning line machining is smaller than the one of the contour line machining.

Development of the cBN Electroplated End-Mill for High Precision Machining of CFRP

2020 ◽  
Author(s):  
Shinnosuke Yamashita ◽  
Tatsuya Furuki ◽  
Hiroyuki Kousaka ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Surface Roughness ◽  
End Milling ◽  
Drop Out ◽  
End Mill ◽  
Machined Surface ◽  
Side Milling ◽  
Reinforced Plastics ◽  
Milled Surface ◽  
Grinding Conditions ◽  
Grinding Tool

Abstract Recently, the demand of carbon fiber reinforced plastics (CFRP) has been rapidly increased in various fields. In most cases, CFRP products requires a finish machining like cutting or grinding. In the case of an end-milling, burrs and uncut fibers are easy to occur. On the other hand, a precise machined surface and edge will be able to obtain by using the grinding tool. Therefore, this research has been developed a novel the cBN electroplated end-mill that combined end-mill and grinding tool. In this report, the effectiveness of developed tool was investigated. First, the developed tool cut the CFRP with side milling. As the result, the cBN abrasives that were fixed on the outer surface of developed tool did not drop out. Next, the end-milled surface of CFRP was ground with the developed tool under several grinding conditions based on the Design of Experiment. Consequently, the optimum grinding condition that can obtain the sharp edge which does not have burrs and uncut fibers was found. However, surface roughness was not good enough. Thus, an oscillating grinding was applied. In addition, the theoretical surface roughness formula in case using the developed tool was formularized. As the result, the required surface roughness in the airplane field was obtained.

scholarly journals Optimization of the Surface Roughness Equation obtained by Al7136 End-Milling

2017 ◽  
Vol 137 ◽  
pp. 03011 ◽  
Author(s):  
Alina Bianca Pop ◽  
Ţîţu Mihail Aurel
Keyword(s):  
Surface Roughness ◽  
End Milling

Investigation of Surface Roughness and Material Removal Rate for High Speed Micro End Milling on PMMA

2015 ◽  
Vol 1115 ◽  
pp. 12-15
Author(s):  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
Abdul Rahman Mohamed ◽  
Md. Sazzad Hossein Chowdhury
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
High Speed ◽  
End Milling ◽  
Removal Rate ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Micro End Milling

Micro end milling is one of the most important micromachining process and widely used for producing miniaturized components with high accuracy and surface finish. This paper present the influence of three micro end milling process parameters; spindle speed, feed rate, and depth of cut on surface roughness (Ra) and material removal rate (MRR). The machining was performed using multi-process micro machine tools (DT-110 Mikrotools Inc., Singapore) with poly methyl methacrylate (PMMA) as the workpiece and tungsten carbide as its tool. To develop the mathematical model for the responses in high speed micro end milling machining, Taguchi design has been used to design the experiment by using the orthogonal array of three levels L18 (21×37). The developed models were used for multiple response optimizations by desirability function approach to obtain minimum Ra and maximum MRR. The optimized values of Ra and MRR were 128.24 nm, and 0.0463 mg/min, respectively obtained at spindle speed of 30000 rpm, feed rate of 2.65 mm/min, and depth of cut of 40 μm. The analysis of variance revealed that spindle speeds are the most influential parameters on Ra. The optimization of MRR is mostly influence by feed rate. Keywords:Micromilling,surfaceroughness,MRR,PMMA

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