Comparative Assessment of Coated CBN and Multilayer Coated Carbide Tools on Tool Wear in Hard Turning AISID2 Steel

2018 ◽  
Author(s):  
M.Junaid Mir ◽  
M.F. Wani ◽  
Summera Banday ◽  
Shuhaib Mushtaq ◽  
Jebran Khan ◽  
...  
Author(s):  
Anshuman Das ◽  
Miyaz Kamal ◽  
Sudhansu Ranjan Das ◽  
Saroj Kumar Patel ◽  
Asutosh Panda ◽  
...  

AISI D6 (hardness 65 HRC) is one of the hard-to-cut steel alloys and commonly used in mould and die making industries. In general, CBN and PCBN tools are used for machining hardened steel but its higher cost makes the use for limited applications. However, the usefulness of carbide tool with selective coatings is the best substitute having comparable tool life, and in terms of cost is approximately one-tenth of CBN tool. The present study highlights a detailed analysis on machinability investigation of hardened AISI D6 alloy die steel using newly developed SPPP-AlTiSiN coated carbide tools in finish dry turning operation. In addition, a comparative assessment has been performed based on the effectiveness of cutting tool performance of nanocomposite coating of AlTiN deposited by hyperlox PVD technique and a coating of AlTiSiN deposited by scalable pulsed power plasma (SPPP) technique. The required number of machining trials under varied cutting conditions (speed, depth of cut, feed) were based on L16 orthogonal array design which investigated the crater wear, flank wear, surface roughness, chip morphology, and cutting force in hard turning. Out of the two cutting tools, newly-developed nanocomposite (SPPP-AlTiSiN) coated carbide tool promises an improved surface finish, minimum cutting force, longer tool life due to lower value of crater & flank wears, and considerable improvement in tool life (i.e., by 47.83%). At higher cutting speeds, the crater wear length and flank wear increases whereas the surface roughness, crater wear width and cutting force decreases. Chip morphology confirmed the formation of serrated type saw tooth chips.


2017 ◽  
Vol 65 (4) ◽  
pp. 553-559 ◽  
Author(s):  
D. Rajeev ◽  
D. Dinakaran ◽  
S.C.E. Singh

AbstractNowadays, finishing operation in hardened steel parts which have wide industrial applications is done by hard turning. Cubic boron nitride (CBN) inserts, which are expensive, are used for hard turning. The cheaper coated carbide tool is seen as a substitute for CBN inserts in the hardness range (45–55 HRC). However, tool wear in a coated carbide tool during hard turning is a significant factor that influences the tolerance of machined surface. An online tool wear estimation system is essential for maintaining the surface quality and minimizing the manufacturing cost. In this investigation, the cutting tool wear estimation using artificial neural network (ANN) is proposed. AISI4140 steel hardened to 47 HRC is used as a work piece and a coated carbide tool is the cutting tool. Experimentation is based on full factorial design (FFD) as per design of experiments. The variations in cutting forces and vibrations are measured during the experimentation. Based on the process parameters and measured parameters an ANN-based tool wear estimator is developed. The wear outputs from the ANN model are then tested. It was observed that as the model using ANN provided quite satisfactory results, and that it can be used for online tool wear estimation.


2010 ◽  
Vol 33 ◽  
pp. 173-176
Author(s):  
X.Y. Wang ◽  
S.Q. Pang ◽  
Q.X. Yu

The aim of this work is to investigate the machinability of new coated carbide cutting tools that are named C7 plus coatings under turning of superalloy GH2132. This achieved by analysis of tool life at different cutting conditions .Investigations of tool wear and tool life testing are intended to establish T-V formulas, and then analyzed the characteristics of coating . Through a series of comparative tests, Using TiAlN coatings as the contrast materialthe results show that the new coating tools that are named C7 plus coatings are suitable for cutting superalloy GH2132. The cutting speed and processing efficiency can be increased effectively.


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