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 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 Experimenting and Predicting Flatness and Surface Roughness during Face Milling Operation of CF8M Grade A-351

10.29007/dcj5 ◽  
2018 ◽  
Author(s):  
Purvi Chauhan ◽  
Sagar Patel ◽  
Karan Patel
Keyword(s):  
Surface Roughness ◽  
Regression Model ◽  
Process Parameters ◽  
Manufacturing Process ◽  
Check Valve ◽  
Face Milling ◽  
Machining Process ◽  
Milling Operation ◽  
Surface Flatness ◽  
Two Parameters

Surface flatness and roughness has a pivotal role in the functioning of any kind of check valve. These two parameters are mainly obtained by face milling[16] during the manufacturing process of valves. The values of these affect during leakage rejection and redesign of check valves. To achieve the desired values of surface flatness and roughness here analysis is carried out. A regression model is generated to predict the values of surface roughness and flatness. ANOVA is also developed to see the effect of machining process parameters on the surface roughness and flatness.

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.

Failure time prognosis in manufacturing process using multi-dislocated time series convolutional neural network

2020 ◽  
pp. 095440892097414
Author(s):  
Bohao Li ◽  
Liping Zhao ◽  
Yiyong Yao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Failure Time ◽  
Great Difficulty ◽  
Milling Process ◽  
Machining Process ◽  
Massive Data ◽  
The Difference

Failure time prognosis in manufacturing process plays a crucial role in guaranteeing manufacturing safety and reducing maintenance loss. However, most current prognosis methods face great difficulty when handling massive data collected from manufacturing process. Convolutional neural network (CNN) provides an effective way to extract features with massive data. Due to the difference between images and multisensory signals, CNN is not suitable for machining process. Inspired by the idea of CNN, a novel prognosis framework is proposed based on the characteristics of multisensory signals, which is called multi-dislocated time series convolutional neural network (MDTSCNN). The proposed MDTSCNN is composed of multi-dislocate layer, convolutional layer, pooling layer and fully connected layer. By adding a multi-dislocate layer, this model can learn the relationship between different signals and different intervals in periodic multisensory signals. The effectiveness of proposed method is validated by a milling process. Compared to other prognosis method, the proposed MDTSCNN shows enhanced performances in prediction accuracy.

scholarly journals Automatic Temperature Measurement and Monitoring System for Milling Process of AA6041 Aluminum Aloy using MLX90614 Infrared Thermometer Sensor with Arduino

2021 ◽  
Vol 82 (2) ◽  
pp. 1-14
Author(s):  
Agus Sudianto ◽  
Zamberi Jamaludin ◽  
Azrul Azwan Abdul Rahman ◽  
Sentot Novianto ◽  
Fajar Muharrom
Keyword(s):  
Aluminum Alloy ◽  
Temperature Measurement ◽  
Monitoring System ◽  
Measurement System ◽  
Manufacturing Process ◽  
Milling Process ◽  
Numerical Control ◽  
Machining Process ◽  
Infrared Thermometer ◽  
Temperature Measurement System

Manufacturing process of metal part requires real-time temperature monitoring capability to ensure high surface integrity is upheld throughout the machining process. A smart temperature measurement and monitoring system for manufacturing process of metal parts is necessary to meet quality and productivity requirements. A smart temperature measurement can be applied in machining processes of conventional, non-conventional and computer numerical control (CNC) machines. Currently, an infrared fusion based thermometer Fluke Ti400 was employed for temperature measurement in a machining process. However, measured temperature in the form of data list with adjustable time range setting is not automatically linked to the computer for continuous monitoring and data analysis purposes. For this reason, a smart temperature measurement system was developed for a CNC milling operation on aluminum alloy (AA6041) using a MLX90614 infrared thermometer sensor operated by Arduino. The system enables data linkages with the computer because MLX90614 is compatible and linked to Microsoft Exel via the Arduino. This paper presents a work-study on the performance of this Arduino based temperature measurement system for dry milling process application. Here, the Arduino based temperature measurement system captured the workpiece temperature during machining of Aluminum Alloy (AA6041) and data were compared with the Fluke Ti400 infrared thermometer. Measurement results from both devices showed similar accuracy level with a deviation of ± 2 oC. Hence, a smart temperature measurement system was succeesfully developed expanding the scopes of current system setup.

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.

scholarly journals An Experimental Study on the Precision Abrasive Machining Process of Hard and Brittle Materials with Ultraviolet-Resin Bond Diamond Abrasive Tools

Materials ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 125 ◽  
Author(s):  
Lei Guo ◽  
Xinrong Zhang ◽  
Shibin Chen ◽  
Jizhuang Hui
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Manufacturing Process ◽  
Material Removal ◽  
Removal Rate ◽  
Machining Process ◽  
Abrasive Machining ◽  
Machining Performance ◽  
Machined Surface ◽  
Ultraviolet Curable

Ultraviolet-curable resin was introduced as a bonding agent into the fabrication process of precision abrasive machining tools in this study, aiming to deliver a rapid, flexible, economical, and environment-friendly additive manufacturing process to replace the hot press and sintering process with thermal-curable resin. A laboratory manufacturing process was established to develop an ultraviolet-curable resin bond diamond lapping plate, the machining performance of which on the ceramic workpiece was examined through a series of comparative experiments with slurry-based iron plate lapping. The machined surface roughness and weight loss of the workpieces were periodically recorded to evaluate the surface finish quality and the material removal rate. The promising results in terms of a 12% improvement in surface roughness and 25% reduction in material removal rate were obtained from the ultraviolet-curable resin plate-involved lapping process. A summarized hypothesis was drawn to describe the dynamically-balanced state of the hybrid precision abrasive machining process integrated both the two-body and three-body abrasion mode.

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