Utilization Of The Desired Machined Surface Roughness Number As A Criterion For Cutting Tool Replacement Strategy

Abstract

Current research in metal removal processes does not provide predictive relationships vitally needed for the selection of machining conditions or specifying limitations on the cutting tool and work material responses. Efforts in the study of tool wear have not progressed to the point that tool life can be predicted using tool and work material properties through fundamental science and engineering principles. Tool replacement decisions are dependent on sensing the wear on the tool or predicting its magnitude by empirical models. This study is an attempt to establish a tool replacement strategy that optimizes a selected performance objective. A quantitative model is presented to experimentally predict the roughness number of the machined surface from the flank wear value of the cutting tool in a turning application then to use it to develop a tool replacement strategy based on the desired machined surface roughness number as a tool failure criterion. Results prove that there is a distinct mathematical relation that ties the tool flank wear value to the resulting roughness number of the machined surface irrespective of the cutting conditions.