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2 Bulletin of the Polish Academy of Sciences. Technical Sciences

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Optimization on friction and wear behaviour of Al-Si alloy reinforced with B4C particles by Powder Metallurgy using Taguchi design

Jeya Raj R., Lenin Anselm W. A., Jinnah Sheik Mohamed M., Christopher Ezhil Singh S., John T. D., Rajeev D., Glan Devadhas G., Jaya Christyan K. G., Malkiya Rasalin Prince R., Jeen Robert R. B.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2020

Vol. 68, nr 6

1393--1402

This research paper discusses the friction and wear behaviour of Al-12Si alloy reinforced with B4C prepared through Powder Metallurgy (P/M) method by varying the weight percentage of reinforcement (x = 2, 4, 6, 8, and 10) content. The samples were prepared by using die and punch assembly and the lubricant used to eject the sample from the die was molybdenum disulfide. The compaction was done by using a compression testing machine by applying a pressure of 800 MPa. The dry sliding friction and wear behaviour of the sample was conducted on a Pin-on-Disc machine and the experimental values of friction and wear were calibrated. The Taguchi design experiment was done by applying an L25 orthogonal array for 3 factors at 5 levels for the response parameter Coefficient of Friction (CoF) and wear loss. The SEM images show the shape, size and EDX confirm the existence of Al, Si, B4C particles in the composites. Analysis of Variance (ANOVA) for CoF of S/N ratio, shows that the reinforcement having 34.92% influence towards the S/N ratio of CoF, ANOVA for wear loss of S/N ratio shows that the sliding distance having 46.76% influence towards the S/N ratio of wear loss, when compared to that of the other two input parameters. The interaction line plot and the 2Dsurface plot for CoF and wear loss show that the increase in B4C content decreases the wear loss and CoF. The worn surface shows that the B4C addition will increase the wear resistance.

Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Rajeev D., Dinakaran D., Singh S. C. E.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2017

Vol. 65, nr 4

553--559

Nowadays, 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.