Study on Parameter Optimization and Tool Wear of Milling Compacted Graphite Iron RuT450 Cylinder Block

In order to study the milling process of compacted graphite iron cylinder block in practical, cutting parameters optimization experiment was conducted when cutting parameters were set as independent variable, and cutting efficiency and spindle power were set as optimization objective. The results showed that spindle power increased as the cutting speed and feed per tooth increasing, but cutting torque increased initially and then decreased as the cutting speed increasing. Feed force decreased with the increasing cutting speed and decreasing feed per teeth. Matlab software was used to optimize the cutting parameters when cutting efficiency and empirical formula of spindle power were settled as the objective functions. The most suitable parameters were abtained as V=164m/min and fz=0.28mm/z. The tool rake face mainly took place crater wear and material sticking. The main wear mechanisms for rake wear and fland wear were abrasive wear and adhesion wear.

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Milling Process Optimization Based on Data from Industrial Shop Floor

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.268-270.1510 ◽

2012 ◽

Vol 268-270 ◽

pp. 1510-1516

Author(s):

Nivaldo Lemos Coppini ◽

Daniel Benedito da Rosa ◽

Edson Melo de Souza ◽

João Honorato ◽

Gerd Erwin Ernst Gojtan

Keyword(s):

Feed Rate ◽

Cutting Speed ◽

Cutting Parameters ◽

Milling Process ◽

Parameters Optimization ◽

Shop Floor ◽

Depth Of Cut ◽

Cutting Condition ◽

Deterministic Method ◽

Cutting Parameters Optimization

The purpose of this work is to develop an experiment applied to milling process cutting parameters optimization based on data collection from the factory shop floor. The cutting parameters to be optimized were cutting speed, feed rate and depth of cut. Hardened steel dies currently used for forging process were milled to generate data to be used during the experiments. The optimization focus was to minimize the milling process cost or to increase it’s the productivity. Two stages had been used: one based in Design of Experiment (DOE) technique and another based on a deterministic method. Both were used to optimize cutting condition parameters to be applied instead of that being used before the experiments. It was possible to conclude, following DOE method results that were recommended to use the smaller cutting speed, the smaller feed rate and the greatest depth of cut, considering the cutting parameters tested. These results were considered by the authors as initial values to apply a deterministic method. The greatest depth of cut and the smaller feed rate were compatible with the machining conditions of the part, considering the initial blank and surface finish required respectively. So, deterministic method was used only to optimize cutting speed. As final result, the authors conclude that minimum cost cutting speed, greatest around 10% to that one found by DOE method was much more convenient to be applied in shop floor, because the smaller cost involved. Finally, this work allowed also concluding that both DOE and deterministic methods can be used to optimize cutting process parameters taking a small number of results collected from factory shop floor during the process evolution.

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Estimation of tool life and cutting burr in high speed milling of the compacted graphite iron by DE based adaptive neuro-fuzzy inference system

Mechanical Sciences ◽

10.5194/ms-10-243-2019 ◽

2019 ◽

Vol 10 (1) ◽

pp. 243-254 ◽

Cited By ~ 2

Author(s):

Longhua Xu ◽

Chuanzhen Huang ◽

Rui Su ◽

Hongtao Zhu ◽

Hanlian Liu ◽

...

Keyword(s):

Tool Life ◽

Feed Rate ◽

High Speed ◽

Fuzzy Inference ◽

Cutting Speed ◽

Cutting Parameters ◽

Compacted Graphite Iron ◽

Inference System ◽

Compacted Graphite ◽

Neuro Fuzzy

Abstract. The studies of tool life and formation of cutting burrs in roughing machining field are core issues in high speed milling of compacted graphite iron (CGI). Changing any one of the cutting parameters like cutting speed or feed rate can result in varied tool life and different height of the cutting burrs. In this work in order to study the relationship between cutting parameters and tool life and height of the cutting burrs, a new differential evolution algorithm based on adaptive neuro fuzzy inference system (DE-ANFIS) as a multi-input and multi-output (MIMO) prediction model is introduced to estimate the tool life and height of the cutting burrs. In this model, the inputs are cutting speed, feed rate and exit angle, and the outputs are tool life and height of the cutting burrs. There are 12 fuzzy rules in DE-ANFIS architecture. Gaussian membership function is adopted during the training process of the DE-ANFIS. The proposed DE-ANFIS model has been compared with PSO-ANFIS, Artificial Neural Network (ANN) and Support Vector Machines (SVM) models. To construct the predictive models, 25 cutting data were obtained through the experiments. Compared with PSO-ANFIS, ANN and SVM models, the results indicate that DE-ANFIS can provide a better prediction accuracy of tool life and height of the cutting burrs, and achieve the required product and productivity. Finally, the analysis of variance (ANOVA) shows that the cutting speed and feed rate have the most effects on the tool life and height of cutting burrs, respectively.

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An Experimental Investigation of the Influence of Cutting-Edge Geometry on the Machinability of Compacted Graphite Iron

International Journal of Manufacturing Materials and Mechanical Engineering ◽

10.4018/ijmmme.2013010101 ◽

2013 ◽

Vol 3 (1) ◽

pp. 1-25 ◽

Cited By ~ 2

Author(s):

Varun Nayyar ◽

Md. Zubayer Alam ◽

Jacek Kaminski ◽

Anders Kinnander ◽

Lars Nyborg

Keyword(s):

Tool Life ◽

Metal Cutting ◽

Cutting Speed ◽

Cutting Parameters ◽

Cutting Edge ◽

Tool Geometry ◽

Compacted Graphite Iron ◽

Compacted Graphite ◽

Edge Geometry ◽

Area Of Interest

Compacted graphite iron (CGI) is considered as the potential replacement of flake graphite iron (FGI) for the manufacturing of new generation high power diesel engines. Use of CGI, that have higher strength and stiffness as compared to FGI, allows engine to perform at higher peak pressure with higher fuel efficiency and lower emission rate. However, not only for its potential, CGI is of an area of interest in metal cutting research because of its poor machinability as compared to that of FGI. The higher strength of CGI causes a faster tool wear rate in continuous machining operation even in low cutting speed as compared to that for FGI. This study investigated the influence of cutting edge geometry at different cutting parameters on the machinability of CGI in terms of tool life, cutting force and surface roughness and integrity in internal turning operation under wet condition. It has been seen that the cutting edge radius has significant effect on tool life and cutting forces. The results can be used to select optimum cutting tool geometry for continuous machining of CGI.

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