cost reduction by improvement of productivity and quality.
In the context of productivity, improvements in machining,
manufacturing cost, tool cost, etc., must all be considered. It
is, therefore, essential to consider economics when selecting
cutting conditions for industrial applications. The economic
aspect was first studied by Wu [6], and various conclusions
have emerged so far. Armarego and Russell [7] studied the
cutting conditions for maximum profitability, and reported that
maximum profit is made at the point when the differentiation
of the speed function is “0”. Wu and Ermer [8] reported that
owing to the influence of machining cost and time, the cutting
speed for maximum profit is a compromise between minimum
machining cost and maximum production rate. However, they
explained [9] that profit is also greatly influenced by the toollife
equation. Field et al. [10] proposed cutting conditions that
take economic considerations onto account for turning, milling
and drilling, but it was estimated that to apply these in industry
is difficult. Crookall [11] tried to define optimum cutting
conditions by computing machining cost on the basis of a
function that took account of time and cost variables. This
study helped to find the characteristics of an objective function,
but did not consider the constraints involved. Ermer and Morris
[12] proposed a unit production cost model using error estimation
for the selection of optimum cutting conditions, and
Bhattacharyya et al. [13] presented an optimum cutting condition
model incorporating constraints. However, these models
have the shortcoming that they require a different correction
factor for every workpiece. Iwata et al.