Investigations on Machining Performances and Tool Wear Surface Characterization During CNC Hard Turning of AISI 4140 Alloy Steel

2021 ◽  
Author(s):  
Shuvajit Ghosh ◽  
Nirmal Kumar Mandal ◽  
Tuhin Kar
Keyword(s):  
Tool Wear ◽  
Alloy Steel ◽  
Wear Surface ◽  
Surface Characterization ◽  
Hard Turning ◽  
Aisi 4140

Investigation on Effects of Cutting and Jet Parameters in Turning of AISI 4140 Hardened Alloy Steel

2019 ◽  
Vol 969 ◽  
pp. 732-737
Author(s):  
Sandip Mane ◽  
Sanjay Kumar
Keyword(s):  
Alloy Steel ◽  
Feed Rate ◽  
Hard Turning ◽  
Cutting Speed ◽  
Cutting Fluid ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Hardened Alloy ◽  
Aisi 4140 ◽  
Cutting Zone

Turning of hardened alloy steel (Hard turning) is a replacement for grinding operation. The heat generation and temperature during hard turning at the cutting zone and due to the friction at tool-chip-workpiece interface are significant parameters which influence chip formation mechanism, tool wear, tool life, surface integrity and hence the machining quality. Cutting fluid performs key role in metal cutting due to its cooling and lubrication action. Flood cooling is a common method of cutting fluid application, in which large quantity of cutting fluid is applied at the cutting zone. Due to environmental, health and safety concerns, the usage of cutting fluid in abundant quantity is being restricted. Most of the researchers have varied the cutting parameters like cutting speed, feed rate and depth of cut to machine different work materials with different cutting tools and studied its effects on cutting force and cutting temperature. It is thus essential to study the combine effect of cutting and jet parameters in machining. This research article focusses on study and optimization of cutting and jet parameters on tool-chip interface temperature and cutting forces during turning hardened alloy steel AISI 4140 steel of 50 HRC using Finite Element Analysis and Taguchi’s Technique. Three levels of cutting speed, feed rate, depth of cut, jet angle and jet velocity are chosen. A suitable L27 Orthogonal array is selected based on Taguchi’s Design of Experiments (DoE) and the output quality characteristics such as tool-chip interface temperature and cutting forces are analyzed by Signal-to-Noise (S/N) ratio. Analysis of Variance is performed to determine the most contributing factor, which shows that the feed and depth of cut are the most prominent contributing parameter followed by cutting speed, jet impingement angle and jet velocity.

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.

MODELING AND INVESTIGATION OF THE WEAR RESISTANCE OF SALT BATH NITRIDED AISI 4140 VIA ANN

2013 ◽  
Vol 20 (03n04) ◽  
pp. 1350033 ◽  
Author(s):  
ŞERAFETTIN EKINCI ◽  
AHMET AKDEMIR ◽  
HUMAR KAHRAMANLI
Keyword(s):  
Wear Resistance ◽  
Friction Coefficient ◽  
Surface Characterization ◽  
Wear Behavior ◽  
Surface Hardness ◽  
Salt Bath ◽  
Back Propagation Algorithm ◽  
Aisi 4140 Steel ◽  
Aisi 4140 ◽  
Sliding Distance

Nitriding is usually used to improve the surface properties of steel materials. In this way, the wear resistance of steels is improved. We conducted a series of studies in order to investigate the microstructural, mechanical and tribological properties of salt bath nitrided AISI 4140 steel. The present study has two parts. For the first phase, the tribological behavior of the AISI 4140 steel which was nitrided in sulfinuz salt bath (SBN) was compared to the behavior of the same steel which was untreated. After surface characterization using metallography, microhardness and sliding wear tests were performed on a block-on-cylinder machine in which carbonized AISI 52100 steel discs were used as the counter face. For the examined AISI 4140 steel samples with and without surface treatment, the evolution of both the friction coefficient and of the wear behavior were determined under various loads, at different sliding velocities and a total sliding distance of 1000 m. The test results showed that wear resistance increased with the nitriding process, friction coefficient decreased due to the sulfur in salt bath and friction coefficient depended systematically on surface hardness. For the second part of this study, four artificial neural network (ANN) models were designed to predict the weight loss and friction coefficient of the nitrided and unnitrided AISI 4140 steel. Load, velocity and sliding distance were used as input. Back-propagation algorithm was chosen for training the ANN. Statistical measurements of R2, MAE and RMSE were employed to evaluate the success of the systems. The results showed that all the systems produced successful results.

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