Heat fux in machining processes : a review.

Abstract

The models of temperature prediction in manufacturing processes have advanced considerably in the last decades, either by applying numerical methods or by the development of techniques and methods of temperature measurement, which feed and compare the results of models. Associated with the advancement of prediction models is the improvement in the analysis of heat generation and distribution during materials machining. This work presents state of the art in research related to heat fux estimation in metal cutting processes using direct and inverse methods, through analytical, numerical, and empirical models. Pioneering and current research approaching the problem of estimating heat fux, as the main focus or means to predict the temperature distribution during the process, are reviewed. Its particularities, such as boundary conditions, techniques used, and innovations concerning previous works, are discussed. Therefore, this paper will present and detail diferent methods to estimate the heat fux during machining, aiming to help researchers identify the advantages and limitations in several cases discussed. The heat fux estimation using inverse methods can be more accurate with the development of data acquisition systems, reducing errors in measured temperatures during the process. In addition, multiphysics numerical simulations characterizing plastic deformation and heat transfer can be improved to help estimate the heat generated in machining.