The general manufacturing problem can be described as the achievement of a predefined product quality with given equipment, cost and time constraints. Unfortunately, for some quality characteristics of a product such as surface roughness it is hard to ensure that these requirements will be met. This paper aims at presenting the various methodologies and practices that are being employed for the prediction of surface roughness. The resulting benefits allow for the manufacturing process to become more productive and competitive and at the same time to reduce any re-processing of the machined workpiece so as to satisfy the technical specifications. Each approach with its advantages and disadvantages is outlined and the present and future trends are discussed. The approaches are classified into those based on machining theory, experimental investigation, designed experiments and artificial intelligence (AI).

Predicting surface roughness in machining: a review

Application of Taguchi method in the optimization of end milling parameters

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained. Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development. q 2004 Elsevier Ltd. All rights reserved.

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method

Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel

Cutting parameters, workpiece material and cutting tool geometry and material all have an essential influence on the achievement of desired product quality at acceptable cost. The machining of prior cold-formed workpieces is a common production route that is economically attractive. This paper deals with the issue of optimising the turning of raw workpieces of low-carbon steel with low cold pre-deformation to achieve acceptable surface roughness. An attempt was made to minimise the number of experimental runs and increase the reliability of experimental results. Conclusions include a description of the machining parameters' influence and levels that provide sufficient robustness of the machining process towards the achievement of the desired surface roughness.

Marko Bahor

Papers

Optimal machining parameters for achieving the desired surface roughness in fine turning of cold pre-formed steel workpieces