Abstract:
Cutting tool management in manufacturing firms constitutes an essential
element in production cost optimization. In order to optimize the cutting tool
stock level while concurrently minimizing production costs, a cost optimization
model which considers machining parameters is required. This inclusive
modeling consideration is a major step towards achieving effectiveness of
cutting tool management policy in manufacturing systems with stochastic
driven policies for tool demand. This paper presents a cost optimization
model for cutting tools whose utilization level is assumed to be optimized in
respect of the machining parameters. The proposed cost model in this research
incorporated the effects of diversified machining costs ranging from
operational through machining, shortage, holding, material and ordering
costs. The machining of parts was assumed to be a single cutting operation.
Holt-Winters forecasting technique was used to create a stochastic demand
dataset for a test scenario in the production of a high-end automotive part.
Some numerical examples used to validate the developed model were implemented
to illustrate the optimal machining and tool inventory conditions.
Furthermore, a sensitivity analysis was carried out to study the influence of
varying production parameters such as: machine uptime, demand and cutting
parameters on the overall production cost. The results showed that a desired low level of tool storage and holding costs were obtained at the optimal stock
levels. The machining uptime had a significant influence on the total cost
while tool life and cutting feed rate were both identified as the most influential
cutting variables on the total cost. Furthermore, the cutting speed rate had
a marginal effect on both costs and tool life. Other cost variables such as
shortage and tool costs had significantly low effect on the overall cost. The output trend showed that the feed rate is the most significant cutting parameter
in the machining operation, hence influencing the cost the most. Also,
machine uptime and demand significantly influenced the total production
cost.
