Monitoring tool wear and surface roughness in the face milling process of high-strength compacted graphite cast irons

2021 ◽  
Vol 43 (4) ◽  
Author(s):  
L. R. R. da Silva ◽  
P. H. P. França ◽  
C. L. F. Andrade ◽  
R. B. Da Silva ◽  
W. L. Guesser ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
High Strength ◽  
Milling Process ◽  
Face Milling ◽  
Monitoring Tool ◽  
Cast Irons ◽  
Compacted Graphite ◽  
The Face

scholarly journals Research and Optimization of Surface Roughness in Milling of SLM Semi-Finished Parts Manufactured by Using the Different Laser Scanning Speed

Materials ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 9 ◽  
Author(s):  
Andrzej Matras
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Laser Scanning ◽  
Cutting Tool ◽  
Scanning Speed ◽  
Milling Process ◽  
Face Milling ◽  
Machining Parameters ◽  
The Face ◽  
Laser Scanning Speed

The paper studies the potential to improve the surface roughness in parts manufactured in the Selective Laser Melting (SLM) process by using additional milling. The studied process was machining of samples made of the AlSi10Mg alloy powder. The simultaneous impacts of the laser scanning speed of the SLM process and the machining parameters of the milling process (such as the feed rate and milling width) on the surface roughness were analyzed. A mathematical model was created as a basis for optimizing the parameters of the studied processes and for selecting the sets of optimum solutions. As a result of the research, surface with low roughness (Ra = 0.14 μm, Rz = 1.1 μm) was obtained after the face milling. The performed milling allowed to reduce more than 20-fold the roughness of the SLM sample surfaces. The feed rate and the cutting width increase resulted in the surface roughness deterioration. Some milled surfaces were damaged by the chip adjoining to the rake face of the cutting tool back tooth.

scholarly journals Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

2021 ◽  
pp. 71-84
Author(s):  
Nhu-Tung Nguyen ◽  
Dung Hoang Tien ◽  
Nguyen Tien Tung ◽  
Nguyen Duc Luan
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Tool Wear ◽  
Feed Rate ◽  
High Speed ◽  
Cutting Speed ◽  
Milling Process ◽  
Face Milling ◽  
Depth Of Cut ◽  
High Speed Milling

In this study, the influence of cutting parameters and machining time on the tool wear and surface roughness was investigated in high-speed milling process of Al6061 using face carbide inserts. Taguchi experimental matrix (L9) was chosen to design and conduct the experimental research with three input parameters (feed rate, cutting speed, and axial depth of cut). Tool wear (VB) and surface roughness (Ra) after different machining strokes (after 10, 30, and 50 machining strokes) were selected as the output parameters. In almost cases of high-speed face milling process, the most significant factor that influenced on the tool wear was cutting speed (84.94 % after 10 machining strokes, 52.13 % after 30 machining strokes, and 68.58 % after 50 machining strokes), and the most significant factors that influenced on the surface roughness were depth of cut and feed rate (70.54 % after 10 machining strokes, 43.28 % after 30 machining strokes, and 30.97 % after 50 machining strokes for depth of cut. And 22.01 % after 10 machining strokes, 44.39 % after 30 machining strokes, and 66.58 % after 50 machining strokes for feed rate). Linear regression was the most suitable regression of VB and Ra with the determination coefficients (R2) from 88.00 % to 91.99 % for VB, and from 90.24 % to 96.84 % for Ra. These regression models were successfully verified by comparison between predicted and measured results of VB and Ra. Besides, the relationship of VB, Ra, and different machining strokes was also investigated and evaluated. Tool wear, surface roughness models, and their relationship that were found in this study can be used to improve the surface quality and reduce the tool wear in the high-speed face milling of aluminum alloy Al6061

scholarly journals Surface Roughness Evaluation in Thin EN AW-6086-T6 Alloy Plates after Face Milling Process with Different Strategies

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3036
Author(s):  
Daniel Chuchala ◽  
Michal Dobrzynski ◽  
Danil Yurievich Pimenov ◽  
Kazimierz A. Orlowski ◽  
Grzegorz Krolczyk ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Cold Rolling ◽  
Aluminium Alloy ◽  
Manufacturing Process ◽  
Thick Plate ◽  
Rolling Direction ◽  
Milling Process ◽  
Face Milling ◽  
Machining Process ◽  
The Face

Lightweight alloys made from aluminium are used to manufacture cars, trains and planes. The main parts most often manufactured from thin sheets requiring the use of milling in the manufacturing process are front panels for control systems, housing parts for electrical and electronic components. As a result of the final phase of the manufacturing process, cold rolling, residual stresses remain in the surface layers, which can influence the cutting processes carried out on these materials. The main aim of this study was to verify whether the strategy of removing the outer material layers of aluminium alloy sheets affects the surface roughness after the face milling process. EN AW-6082-T6 aluminium alloy thin plates with three different thicknesses and with two directions relative to the cold rolling process direction (longitudinal and transverse) were analysed. Three different strategies for removing the outer layers of the material by face milling were considered. Noticeable differences in surface roughness 2D and 3D parameters were found among all machining strategies and for both rolling directions, but these differences were not statistically significant. The lowest values of Ra = 0.34 µm were measured for the S#3 strategy, which asymmetrically removed material from both sides of the plate (main and back), for an 8-mm-thick plate in the transverse rolling direction. The highest values of Ra = 0.48 µm were measured for a 6-mm-thick plate milled with the S#2 strategy, which symmetrically removed material from both sides of the plate, in the longitudinal rolling direction. However, the position of the face cutter axis during the machining process was observed to have a significant effect on the surface roughness. A higher surface roughness was measured in the areas of the tool point transition from the up-milling direction to the down-milling direction (tool axis path) for all analysed strategies (Ra = 0.63–0.68 µm). The best values were obtained for the up-milling direction, but in the area of the smooth execution of the process (Ra = 0.26–0.29 µm), not in the area of the blade entry into the material. A similar relationship was obtained for analysed medians of the arithmetic mean height (Sa) and the root-mean-square height (Sq). However, in the case of the S#3 strategy, the spreads of results were the lowest.

scholarly journals Durability identification of individual inserts used in the face milling cutter

Mechanik ◽  
2019 ◽  
Vol 92 (2) ◽  
pp. 96-98
Author(s):  
Łukasz Żurawski ◽  
Borys Storch
Keyword(s):  
Surface Roughness ◽  
Milling Process ◽  
Face Milling ◽  
Physical Factors ◽  
Milling Cutter ◽  
Workpiece Surface ◽  
The Face ◽  
Cutting Inserts ◽  
Carbide Inserts

In this work, the milling process realizing by carbide inserts along with durability identification of each of individual inserts fixed in the cutting inserts-based multiple tool has been presented. The adopted hypothesis about a concurrent wear of the inserts, under conditions in which the relevant technological and physical factors were set, has been verified experimentally. The verification was based on analysis of workpiece surface roughness.

Tool wear monitoring in drilling of high-strength compacted graphite cast irons

2020 ◽  
Vol 235 (1-2) ◽  
pp. 207-218 ◽  
Author(s):  
LRR da Silva ◽  
VTS Del Claro ◽  
CLF Andrade ◽  
WL Guesser ◽  
MJ Jackson ◽  
...  
Keyword(s):  
Tool Wear ◽  
High Strength ◽  
Quality Parameters ◽  
Output Parameter ◽  
Cutting Condition ◽  
Tool Wear Monitoring ◽  
Cast Irons ◽  
Compacted Graphite ◽  
Wear Monitoring ◽  
Coated Carbide

Machining is such a complex system that any foreseen result is practically impossible. However, research always helps to further understand the process, which contributes to providing positive results. Tool wear is always difficult to foresee, but it can be measured and related to several output parameters. In each individual application, there will be the best parameter that most reliably represents the tool wear. In the present investigation, the hole quality parameters (roughness and cylindricity), power consumption, electrical consumption of the machine tool and the acoustic emission signals were recorded and correlated to the tool condition in order to find the best output parameter for tool wear monitoring during the drilling of compact graphite cast irons. Two high-strength grades of compacted graphite cast irons (both CGI 500 with changes in the matrix and graphitic structure) were machined and compared to the standard grade (CGI 450) usually used in the manufacturing of engines using TiAlN-coated carbide drills at a constant cutting condition. The results showed that the best output parameter to monitor the tool wear was the electric current signal.

scholarly journals Prediction of Tool Lifetime and Surface Roughness for Nickel-Based Waspaloy

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shao-Hsien Chen ◽  
Chung-An Yu
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
High Speed ◽  
Cutting Speed ◽  
High Strength ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Predictive Values ◽  
Actual Surface ◽  
Wide Range

In recent years, most of nickel-based materials have been used in aircraft engines. Nickel-based materials applied in the aerospace industry are used in a wide range of applications because of their strength and rigidity at high temperature. However, the high temperatures and high strength caused by the nickel-based materials during cutting also reduce the tool lifetime. This research aims to investigate the tool wear and the surface roughness of Waspaloy during cutting with various cutting speeds, feed per tooth, cutting depth, and other cutting parameters. Then, it derives the formula for the tool lifetime based on the experimental results and explores the impacts of these cutting parameters on the cutting of Waspaloy. Since the impacts of cutting speed on the cutting of Waspaloy are most significant in accordance with the experimental results, the high-speed cutting is not recommended. In addition, the actual surface roughness of Waspaloy is worse than the theoretical surface roughness in case of more tool wear. Finally, a set of mathematical models can be established based on these results, in order to predict the surface roughness of Waspaloy cut with a worn tool. The errors between the predictive values and the actual values are 5.122%∼8.646%. If the surface roughness is within the tolerance, the model can be used to predict the residual tool lifetime before the tool is damaged completely. The errors between the predictive values and the actual values are 8.014%∼20.479%.

scholarly journals Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3817 ◽  
Author(s):  
Xuefeng Wu ◽  
Yahui Liu ◽  
Xianliang Zhou ◽  
Aolei Mou
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Automatic Detection ◽  
Milling Process ◽  
Face Milling ◽  
Machining Process ◽  
Model Parameters

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

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