Uncertainties of Surface Roughness and Tool Wear in Machining of AISI 4140 Alloy Steel

Abstract

Uncertainty is the probability of statistical dispersion of a quantity from the desired value. Every manufacturing process has some types of uncertainties. In this investigation, individual, combined and expanded uncertainties of surface roughness and tool wear in machining of AISI 4140 alloy steel are quantified and compared. First, the individual and combined uncertainty of two responses namely tool wear, and surface roughness are measured using a deterministic method called the law of propagation of uncertainties (LPUs), and the principal contributors to uncertainties are identified. This method produces uncertainty of surface roughness as 0.1162[Formula: see text][Formula: see text]m and that of tool wear as 7.4036[Formula: see text][Formula: see text]m. It is further observed that cutting speed contributes 77.946% and 57.4853% on overall uncertainties for surface roughness and tool wear respectively. A stochastic method called Monte Carlo simulation (MCS) technique is further employed using 100,000 no. of iterations to compute the uncertainties in surface roughness and tool wear. This method produces uncertainties as 0.0581[Formula: see text][Formula: see text]m and 3.687[Formula: see text][Formula: see text]m in surface roughness and tool wear respectively. The results obtained from the deterministic model (LPU) and the stochastic model (MCS) are compared and it is observed that the MCS method is more reliable and accurate than the LPU method.