The machining process determines the overall quality of produced forming and
forging dies, including surface integrity. Previous research found that surface
integrity has a significant influence on the fatigue life of the dies. This thesis
aims to establish a cost-effective approach for precision milling to obtain
forming and forging dies with good surface integrity and long fatigue life. It
combined experimental study accompanied by Finite Element Modelling and
Artificial Intelligence soft modelling to predict and enhance forming and forging
die life.
Four machining parameters, namely Surface Speed, Depth of cut, Feed Rate
and Tool Lead Angle, each with five levels, were investigated experimentally
using Design of Experiment. An ANOVA analysis was carried out to identify
the key factor for every Surface Integrity (SI) parameter and the interaction of
every factor. It was found that the cutting force was mostly influenced by the
tool lead angle. The residual stress and microhardness were both significantly
influenced by the surface speed. However, on the surface roughness it was
found that the feed rate had the most influence.
After the machining experiments, four-point bending fatigue tests were carried
out to evaluate the fatigue life of precision milled parts at an elevated
temperature in a low cycle fatigue set-up imitated for the forming and forging
production. It was found that surface roughness and hardness were the most
influential factors for fatigue life. A 3D-FE-Modelling framework including a new
material model subroutine was developed; this led to a more comprehensive
material model. A fractional factorial simulation with over 180 simulations was
carried out and validated with the machining experiment.
Based on the experimental and simulation results, a soft prediction model for
surface integrity was established by using Artificial Neural Networks (ANN)
approach. These predictions for SI were then used in a Genetic Algorithm
model to optimise the SI. The confirmation tests showed that the machining
strategy was successfully optimised and the average fatigue duration was
increased by at least a factor of two. It was found that a surface speed of 270
m/min, a feed rate of 0.0589 mm/tooth, a depth of cut of 0.39 mm and a tool
lead angle of 16.045° provided the good surface integrity and increased fatigue
performance. Overall, these findings conclude that the fundamentals and
methodology utilised have developed a further understanding between
machining and forming/forging process, resulting in a good foundation for a
framework to generate FE and soft prediction models which can be used to in
optimisation of precision milling strategy for different materials.
| Date of Award | 17 May 2019 |
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| Original language | English |
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| Awarding Institution | - University Of Strathclyde
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| Sponsors | EPSRC (Engineering and Physical Sciences Research Council) |
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| Supervisor | Xichun Luo (Supervisor) & William Ion (Supervisor) |
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