Experimental investigation of tool wear in face milling of EN-31 steel under different machining environments

The aim of this study were to evaluate the performance of PVD (TiAlN+TiN) and CVD (TiCN+Al2O3+TiN) coated inserts in end milling of EN–31 hardened die steel of 43±1 HRC during dry and MQL (Minimum quantity lubrication) machining. The experiments were conducted at a fixed feed rate, depth of cut and varying cutting speed to measure the effect of cutting speed on cutting force and tool wear of CVD and PVD-coated inserts. The performance of CVD and PVD-coated inserts under dry and MQL condition by measuring the tool wear and cutting force were compared. During cutting operation, it was noticed that PVD inserts provide less cutting force and tool wear as compared to the CVD inserts under both dry as well as the MQL condition because PVD inserts have a thin insert coating and CVD inserts have a thick insert coating, but PVD inserts experience catastrophic failure during cutting operation whereas CVD inserts have a capability for continuous machining under different machining. Tool wear has measured by SEM analysis. The result shows that MQL machining provides the optimum results as compared to the dry condition. MQL machining has the ability to work under high cutting speed. As the cutting speed increases the performance of dry machining was decreased, but in MQL machining, the performance of the inserts was increased with increases of cutting speed. MQL machining generates less cutting force on the cutting zone and reduces the tool wear which further increase the tool life.

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

Sensors ◽

10.3390/s19183817 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3817 ◽

Cited By ~ 9

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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Performance comparison of coated carbide tool under different cooling/lubrication environments during face milling of Inconel-625 and Stainless Steel 304

World Journal of Engineering ◽

10.1108/wje-06-2018-0194 ◽

2019 ◽

Vol 16 (2) ◽

pp. 287-295 ◽

Cited By ~ 1

Author(s):

Pragat Singh ◽

J.S. Dureja ◽

Harwinder Singh ◽

Manpreet S. Bhatti

Keyword(s):

Tool Wear ◽

Wear Behavior ◽

Face Milling ◽

Cutting Fluid ◽

Inconel 625 ◽

Machining Performance ◽

Content Type ◽

Stainless Steel 304 ◽

Coated Carbide ◽

Ss 304

PurposeThis study aims to use nanofluid-based minimum quantity lubrication (NMQL) technique to minimize the use of cutting fluids in machining of Inconel-625 and Stainless Steel 304 (SS-304) (Ni-Cr alloys).Design/methodology/approachMachining of Ni-Cr-based alloys is very challenging as these exhibit lower thermal conductivity and rapid work hardening. So, these cannot be machined dry, and a suitable cutting fluid has to be used. To improve the thermal conductivity of cutting fluid, multi-walled carbon nanotubes (MWCNTs) were added to the soybean oil and used with MQL. This study attempts to compare tool wear of coated carbide inserts during face milling of Inconel-625 and SS-304 under dry, flooded and NMQL conditions. The machining performance of both materials, i.e. Inconel-625 and SS-304, has been compared on the basis of tool wear behavior evaluated using scanning electron microscopy-energy dispersive spectroscopy.FindingsThe results indicate higher tool wear and lower tool life during machining of Inconel-625 as compared to SS-304. Machining of Inconel-625 exhibited non-consistent tool wear behavior. The tool failure modes experienced during dry machining are discrete fracture, cracks, etc., which are completely eliminated with the use of NMQL machining. In addition, less adhesion wear and abrasion marks are noticed as compared to dry and flooded machining, thereby enhancing the tool life.Research limitations/implicationsInconel-625 and SS-304 have specific applications in aircraft and aerospace industry, where sculptured surfaces of the turbine blades are machined. The results of current investigation will provide a rich data base for effective machining of both materials under variety of machining conditions.Originality/valueThe literature review indicated that majority of research work on MQL machining has been carried out to explore machining of Ni-Cr alloys such as Inconel 718, Inconel 800, AISI4340, AISI316, AISI1040, AISI430, titanium alloys, hardened steel alloys and Al alloys. Few researchers have explored the suitability of nanofluids and vegetable oil-based cutting fluids in metal cutting operation. However, no literature is available on face milling using nanoparticle-based MQL during machining Inconel-625 and SS-304. Therefore, experimental investigation was conducted to examine the machining performance of NMQL during face milling of Inconel-625 and SS-304 by using soybean oil (vegetable oil) with MWCNTs to achieve ecofriendly machining.

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