Development of Tooling Cost Model for High Speed Hard Turning

Konneh

Iqbal

et al. 2015

AMR

High speed turning (HST) is an advanced machining process that uses higher cutting speeds than those used in conventional machining. HST enables manufacturers to shorten machining times. Therefore, this approach should be followed and justified by economic study. One of the most effective tools for economic study is by developing a target-cost model to control the machining cost. The aim of this research is to develop a target costing model for high speed turning. To achieve the aim of this research, a set of experimental data was obtained in the following cutting levels: cutting speed (500-700 m/min), feed rate (1000-2000 mm/min), and depth of cut of (0.1-0.3) mm. The materials used in this research were AISI 304 stainless steel as a work piece material and coated carbide as a cutting tool. The output data was used to develop a target costing model. The desirability function has been used to optimize the model.

Section: Box Behnken Designmentioning

confidence: 99%

Using the Desirability Function as an Effective Tool in Target Costing Model

Konneh

Iqbal

et al. 2015

AMR

“…The machining time components used in this research was based on machining time model developed by [6]. However, the final tooling cost was calculated based on the following equation [3]: However, all the experimental results is concluded in Table 2. The results was analysed using the DoE 6.0.8.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Cutting Parameters to Minimize Tooling Cost in High Speed Turning of SS304 using Coated Carbide Tool using Genetic Algorithm Method

Najwa

2016

IJEMM

High speed turning (HST) is an approach that can be used to increase the material removal rate (MRR) by higher cutting speed. Increasing MRR will lead to shortening time to market. In contrast, increasing the cutting speed will lead to increasing the flank wear rate and then the tooling cost. However, the main factor that will justify the best level of cutting speed is the tooling cost which merges all in one understandable measurable factor for manufacturer. The aim of this paper is to determine experimentally the optimum cutting levels that minimize the tooling cost in machining AISI 304 as a work piece machined by a coated carbide tool using one of the non-conventional methods: Genetic Algorithm (GA). The experiments were designed using Box Behnken Design (BBD) with three input factors: cutting speed, feeding speed and depth of cut and three machining levels.Keywords: High speed turning, tooling cost, AISI 304, MRR 1 INTRODUCTION The development of advanced manufacturing technology has been growing up rapidly. One of the advanced approaches is by increasing the machining speed to increase material removal rate and then shortening time to market, lowering cost, high accuracy and better quality. One approach for reducing the machining time in machining is by increasing the speed turning. High speed turning is difficult to define due to the fact of materials are varied for their hardness. Therefore, high speed turning for one material may still be a low speed for another for example; the high speed for titanium is a low speed for aluminium [1]. However, these technologies should be justified by economic study. One of the most effective tools for economic study is by developing a cost model.In high speed turning the machining zone will be under high temperature and high sliding velocity. Therefore, the wear progress will be difficult to estimate and predict. However, the wear rate of the cutting tool may give unacceptable outputs and that will result a low quality of surface roughness [2]. However, estimating the tool wear is highly valuable to estimate the tooling cost due to the relationship of tool life and material removal during the life of tool. However, tool insert may reaches its life and should be removed and changed before the tool insert edge cannot give the desired and accepted roughness. If the cutting tool reaches its life very fast then this will lead to increase the tooling cost becomes. Therefore, the manufacturer needs to determine the best cutting levels that minimize the tooling cost. Thus, estimating and then determining the best levels of the independent factors in machining becomes critical and important.Determining the input level that can give the optimum values in machining process for one output response is very useful if the need for that response is important for one application but needs a validate and reliable mathematical model. In this research a regression empirical model will be developed and then the genetic algorithm method will be used to determine the minimum tooling...

“…High speed machining of hard alloy steels have many advantages such as reduction of machining time, higher metal removal rates, lower machining costs and better surface roughness. However, in high speed machining the temperature in both work material and cutting tool increases substantially due to a severe cutting condition [1]. Thus, predicting and modelling the responses of the machining process such as the temperature, cutting forces, surface roughness and wear progress before the machining process in certain cutting levels become an important issue.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

Adesta

Suprianto

et al. 2012

AMR

Self Cite

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.