scholarly journals Prediction of cutting temperature in the milling of wood-plastic composite using artificial neural network

BioResources ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. 6993-7005
Author(s):  
Feng Zhang ◽  
Zhanwen Wu ◽  
Yong Hu ◽  
Zhaolong Zhu ◽  
Xiaolei Guo
Keyword(s):  
Neural Network ◽  
Tool Life ◽  
Great Influence ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Measurement Technology ◽  
Plastic Composite ◽  
Prediction And Control ◽  
Cutting Zone ◽  
Infrared Temperature Measurement

In the milling of wood-plastic composites, the cutting temperature has a great influence on tool life and cutting quality. The effects of cutting parameters on the cutting temperatures in the cutting zone were analyzed using infrared temperature measurement technology. The results indicated that the cutting temperature increased with the increase of spindle speed and cutting depth but decreased with the increase of feed rates. In addition, based on experimental data, a BP neural network model was proposed for predicting the cutting temperatures. The value of R2 was 0.97354 for the testing data, which indicates that the developed model achieved high prediction accuracy, respectively. The results of the study can play a guiding role in the prediction and control of cutting temperature, which is of great importance in the improvement of tool life, machining quality, and machining efficiency.

Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm

2015 ◽  
Vol 761 ◽  
pp. 132-136
Author(s):  
Adnan Jameel Abbas ◽  
Mohammad Minhat ◽  
Md Nizam Abd Rahman
Keyword(s):  
Surface Roughness ◽  
Mild Steel ◽  
Feed Rate ◽  
Cutting Tool ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Optimum Cutting ◽  
Cutting Velocity ◽  
Cutting Zone ◽  
The Ideal

. The minimum cost and high productivity of the recent industrial renaissance are its main challengers. Selecting the optimum cutting parameters play a significant role in achieving these aims. Heat generated in the cutting zone area is an important factor affecting workpiece and cutting tool properties. The surface finish quality specifies product success and integrity. In this paper, the temperature generated in the cutting zone (shear zone and chip-tool interface zone) and workpiece surface roughness is optimized using an artificial immune system (AIS) intelligent algorithm. A mild steel type (S45C) workpiece and a tungsten insert cutting tool type (SPG 422) is subjected to dry CNC turning operation are used in experiments. Optimum cutting parameters (cutting velocity, depth of cut, and feed rate) calculated by the (AIS) algorithm are used to obtain the simulated and ideal cutting temperature and surface roughness. An infrared camera type (Flir E60) is used for temperature measurement, and a portable surface roughness device is used for roughness measurement. Experimental results show that the ideal cutting temperature (110°C) and surface roughness (0.49 μm) occur at (0.3 mm) cut depth, (0.06 mm) feed rate, and (60 m/min) cutting velocity. The AIS accuracy rates in finding the ideal cutting temperature and surface roughness are (91.70 %) and (90.37 %) respectively. Analysis shows that the predicted results are close to the experimental ones, indicating that this intelligent system can be used to estimate cutting temperature and surface roughness during the turning operation of mild steel.

scholarly journals Monitoring and neural network modeling of cutting temperature during turning hard steel

2018 ◽  
Vol 22 (6 Part A) ◽  
pp. 2605-2614
Author(s):  
Mirfad Taric ◽  
Pavel Kovac ◽  
Bogdan Nedic ◽  
Dragan Rodic ◽  
Dusan Jesic
Keyword(s):  
Neural Network ◽  
Cubic Boron Nitride ◽  
Cutting Tools ◽  
Cutting Temperature ◽  
Tool Material ◽  
Cutting Parameters ◽  
Neural Network Modeling ◽  
Machining Parameters ◽  
Average Temperature ◽  
Hard Steel

In this study, cutting tools average temperature was investigated by using thermal imaging camera of FLIR E50-type. The cubic boron nitride inserts with zero and negative rake angles were taken as cutting tools and round bar of EN 90MnCrV8 hardened steel was used as the workpiece. Since the life of the cutting tool material strongly depends upon cutting temperature, it is important to predict heat generation in the tool with intelligent techniques. This paper proposes a method for the identification of cutting parameters using neural network. The model for determining the cutting temperature of hard steel, was trained and tested by using the experimental data. The test results showed that the proposed neural network model can be used successfully for machinability data selection. The effect on the cutting temperature of machining parameters and their interactions in machining were analyzed in detail and presented in this study.

Machinability Investigation in Dry Turning of Ti–6Al–4V with a Novel TiB2–ZrC Cermet Tool Using RSM

2020 ◽  
Author(s):  
Yikun Yuan ◽  
Wenbin Ji ◽  
Shijie Dai
Keyword(s):  
Surface Roughness ◽  
Tool Life ◽  
Wear Mechanism ◽  
Cutting Forces ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Processing Efficiency ◽  
Response Factors ◽  
Dry Turning ◽  
Tool Wear Mechanism

Abstract To ensure accuracy and improve the processing efficiency of Ti–6Al–4V alloys, dry turning experiment of Ti–6Al–4V was carried out using a novel TiB2–ZrC cermet tool. The tool was reinforced by nanoscale VC additive and exhibited excellent hardness and fracture toughness. Response surface methodology (RSM) was used in the experiment to verify and evaluate the cutting performance of TiB2–ZrC cermet tool. The cutting forces and surface roughness (Ra) were selected as the optimization objective. Then the analysis of variance (ANOVA) was employed to ascertain the effective cutting parameters on response factors and demonstrate accuracy of the models. It was found that the effective cutting parameters on surface roughness was feed rate, while cutting depth significantly affected cutting forces. And the confirmation experiments showed that the predicted values coincide with experimental values nearly. Based on the optimized cutting parameters, the tool life and tool wear mechanism were investigated. When the vc, ap and f were 100 mm/min, 0.16 mm, 0.1 mm/rev, respectively, the cutting length and tool life could reach to 3233 m and 29.4 min, respectively, due to the excellent wear resistance and stability of TiB2–ZrC cermet tool at high cutting temperature. In this case, the main wear mechanism was adhesive wear and diffusion wear.

Investigation on Effect of Machining Parameters of End Milling on Surface Finish and Temperature Using Taguchi Technique

2016 ◽  
Vol 16 (4) ◽  
pp. 255-261
Author(s):  
Tamiloli N ◽  
Venkatesan J
Keyword(s):  
Surface Roughness ◽  
Taguchi Method ◽  
End Milling ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Milling Process ◽  
Machining Parameters ◽  
Cutting Zone ◽  
Machining Optimization ◽  
Quality Surface

AbstractMachining of alloy materials at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality. Thus, developing a model for estimating the cutting parameters and optimizing this model to minimize the surface roughness and cutting temperatures becomes utmost important to avoid any damage to the quality surface. This paper presents the development of new models and optimizing these models of machining parameters to minimize the surface roughness and cutting temperature in end milling process by Taguchi method with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and cutting temperature. Due to the complexity of this machining optimization problem, a single objective Taguchi method has been applied to resolve the problem, and the results have been analyzed.

CFD Analysis on the Flow Field of Minimum Quantity Lubrication during External Thread Turning

2012 ◽  
Vol 723 ◽  
pp. 113-118 ◽  
Author(s):  
Ming Chen ◽  
Li Jiang ◽  
Bo Wen Shi ◽  
Zhi Qiang Liu ◽  
Qing Long An
Keyword(s):  
Flow Field ◽  
Flow Rate ◽  
Great Influence ◽  
Cutting Temperature ◽  
Minimum Quantity Lubrication ◽  
Droplet Breakup ◽  
Machining Process ◽  
Minimum Quantity ◽  
Cutting Zone ◽  
Oil Mist

Minimum quantity lubrication (MQL) is a new cooling technology used in machining process, which is friendly to environment and lower cost. This paper applied computational fluid dynamics (CFD) to analyze flow field of MQL in thread turning. Comprehensively applying droplet breakup method and capillary method, flow field of MQL in cutting zone were analyzed in detail. The results showed that oil mist could enter into cutting zone from sides of chip. And two vortexes existed around cutting zone which could reduce cutting temperature by accelerating heat exchange. Furthermore, flow rate of compressed air had great influence on velocity and diameter of droplets, velocity and pressure distribution of flow field. The effects of different flow rates indicated the larger flow rate was benefit for entering into cutting zone of oil mist.

A study on cutting temperature for wood–plastic composite

2016 ◽  
Vol 29 (12) ◽  
pp. 1627-1640
Author(s):  
Zhijian Pei ◽  
Nanfeng Zhu ◽  
Yu Gong
Keyword(s):  
Imaging System ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Processing Parameters ◽  
Numerical Control ◽  
Wood Plastic Composite ◽  
Control Machine Tool ◽  
Infrared Thermal Imaging ◽  
Plastic Composite ◽  
Thermal Imaging System

Wood–plastic composite (WPC) material has been developed rapidly and used widely to replace wood production in recent years. The cutting process of WPC material is the key to directly affect the efficiency of utilization and processing. The infrared thermal imaging system and numerical control machine tool were used in this article to analyze the cutting temperature under different cutting parameters, which was further compared with massoniana wood cutting procedure to provide theoretical basis for WPC material processing. Under certain conditions, the cutting depth was the most important factor on the cutting temperature, followed by spindle speed, while cutting width was the least affected. In the cases of similar processing parameters, although cutting temperature for massoniana wood is always higher than WPCs, the change trends of their cutting temperature are similar. Besides, shear heat moderately affected the cutting temperature during cutting.

Selection of Tool Materials and Cutting Parameters Optimization for Turning Nickel-Based Powder Metallurgy Superalloy

2010 ◽  
Vol 154-155 ◽  
pp. 708-711 ◽  
Author(s):  
Yang Qiao ◽  
Xing Ai ◽  
Zhan Qiang Liu
Keyword(s):  
Powder Metallurgy ◽  
Tool Wear ◽  
Cutting Force ◽  
Tool Life ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Dry Turning ◽  
Nickel Based Superalloy ◽  
Wide Range ◽  
Cutting Parameters Optimization

Powder metallurgy (PM) nickel-based superalloy is regarded as one of the most important aerospace industry materials. A series of turning tests in a wide range of speeds with different inserts were carried out to select the proper tool material. Then, the effect of cutting parameters on the cutting force, cutting temperature and tool wear was investigated for the selected insert. The effect of cutting parameters on the tool wear was examined through SEM and TEM micrographs. The experiential functions of tool life, cutting force and cutting temperature were developed. Finally, the cutting parameters in PM nickel-based superalloy dry turning were optimized based on tool life-efficiency contour analysis. The present approach and results will be helpful for understanding the machinability of PM nickel-based superalloy during dry turning for the manufacturing engineers.

Stable cutting zone prediction in computer numerical control turning based on empirical mode decomposition and artificial neural network approach

2018 ◽  
Vol 41 (1) ◽  
pp. 193-209 ◽  
Author(s):  
Yogesh Shrivastava ◽  
Bhagat Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Quality ◽  
Empirical Mode Decomposition ◽  
Cutting Parameters ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Mode Decomposition ◽  
Cutting Zone ◽  
Tool Chatter

Stable cutting zone prediction is the key requirement for retaining high-productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for abrupt change in surface quality and productivity. In this research work, an optimum safe cutting zone has been predicted by analyzing the tool chatter so that higher productivity can be achieved. Initially, chatter signals have been recorded by performing experiments at different combinations of cutting parameters on computer numerical control trainer lathe. Further, these recorded signals have been preprocessed by empirical mode decomposition technique, followed by the selection of dominating intrinsic mode functions using Fourier transform. The preprocessed signals have been used to evaluate a new output parameter, that is, chatter index (CI). Artificial neural network (ANN) based on the feedforward backpropagation network has been proposed for predicting tool chatter in turning process. The input machining parameters considered are depth of cut, feed rate and cutting speed. It has also been deduced that from available different transfer functions, the Hyperbolic Tangent transfer function in ANN is best suitable to predict tool chatter severity in turning operation. Moreover, the safe cutting zone has been assessed by evaluating the dependency of CI on cutting parameters. Finally, more experiments have been conducted to validate the obtained cutting zone.

Research Status of Subsequent Machining of Laser Cladding Layers

2020 ◽  
Vol 15 ◽  
Author(s):  
Lei Li ◽  
Yujun Cai ◽  
Guohe Li ◽  
Meng Liu
Keyword(s):  
Tool Wear ◽  
Surface Quality ◽  
Laser Cladding ◽  
Wear Surface ◽  
Chip Formation ◽  
Cutting Temperature ◽  
Dimensional Accuracy ◽  
Cutting Parameters ◽  
Cladding Layer ◽  
Cladding Layers

Background: As an important method of remanufacturing, laser cladding can be used to obtain the parts with specific shapes by stacking materials layer by layer. The formation mechanism of laser cladding determines the “Staircase effect”, which makes the surface quality can hardly meet the dimensional accuracy of the parts. Therefore, the subsequent machining must be performed to improve the dimensional accuracy and surface quality of cladding parts. Methods: In this paper, chip formation, cutting force, cutting temperature, tool wear, surface quality, and optimization of cutting parameters in the subsequent cutting of laser cladding layer are analyzed. Scholars have expounded and studied these five aspects but the cutting mechanism of laser cladding need further research. Results: The characteristics of cladding layer are similar to that of difficult to machine materials, and the change of parameters has a significant impact on the cutting performance. Conclusion: The research status of subsequent machining of cladding layers is summarized, mainly from the aspects of chip formation, cutting force, cutting temperature, tool wear, surface quality, and cutting parameters optimization. Besides, the existing problems and further developments of subsequent machining of cladding layers are pointed out. The efforts are helpful to promote the development and application of laser cladding remanufacturing technology.

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