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This article presents an experimental study to investigate wear resistance of grey cast iron samples with different additions of copper from 0.00 to 3.15 wt%. A pin-on-disc apparatus was designed and built to conduct the experimental trials and assess the influence of wear time, rotational speed and normal load on wear behaviour. Experiments on wear time were from 5 to 20 min, rotational speed from 90 to 1400 r/min and applied pressure from 2.0 to 8.0 MPa, all in dry conditions. Results showed that morphology and microstructure are important parameters in relation to the wear regime exhibited by the material. It is confirmed that wear losses increased with increase in wear time, rotational speed and applied pressure. Also, it is clearly seen that adding copper to the cast iron changed the ferrite matrix into a pearlite matrix. This led to a significant improvement in the mechanical properties, especially wear resistance, with slight increase in the hardness. Generally, wear losses were quantified for cast iron with added copper; an increase in copper from 0.00 to 3.15 wt% reduced the material loss by about 30%.

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This paper presents the development and experimental validation of a modeling approach that was proposed to predict the surface generation process during ultraprecision turning. In particular, in addition to the kinematic paramters, the proposed model takes into consideration the effects of the minimum chip thickness and elastic recovery along side their associated uncertainity attributable to the blend nature of the multi-phase materials. The model amis to eliminate the contribution of the uncertainty errors due to the stochastic behavior of the phases presents within the material microstructe. Thus, it allows predicting the achievable surface roughness more preciously under different cutting conditions. The developed model was experimentally validated by machining dual-phase material, Brass 6040, under a range of processing parameters. The roughness of the generated surface was measured and compared with those estimated by the model under similar conditions. Prelimenrary implementation of the model indicated that the model predictions relatively agreed with the experimental results. After conducting a calibration procedure, lower error was obtained 20.45%. However, by excluding the results at very low feed rates to duduct its erratic influence, the average error substantially reduced to 11.18% using cutting tools with nose radius of 200 µm.