Thesis
A cost effective approach to enhance surface integrity and fatigue life of precision milled forming and forging dies
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2019
- Thesis identifier
- T15180
- Person Identifier (Local)
- 201455943
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- The machining process determines the overall quality of produced forming andforging dies, including surface integrity. Previous research found that surfaceintegrity has a significant influence on the fatigue life of the dies. This thesisaims to establish a cost-effective approach for precision milling to obtainforming and forging dies with good surface integrity and long fatigue life. Itcombined experimental study accompanied by Finite Element Modelling andArtificial Intelligence soft modelling to predict and enhance forming and forgingdie life.Four machining parameters, namely Surface Speed, Depth of cut, Feed Rateand Tool Lead Angle, each with five levels, were investigated experimentallyusing Design of Experiment. An ANOVA analysis was carried out to identifythe key factor for every Surface Integrity (SI) parameter and the interaction ofevery factor. It was found that the cutting force was mostly influenced by thetool lead angle. The residual stress and microhardness were both significantlyinfluenced by the surface speed. However, on the surface roughness it wasfound that the feed rate had the most influence.After the machining experiments, four-point bending fatigue tests were carriedout to evaluate the fatigue life of precision milled parts at an elevatedtemperature in a low cycle fatigue set-up imitated for the forming and forgingproduction. It was found that surface roughness and hardness were the mostinfluential factors for fatigue life. A 3D-FE-Modelling framework including a newmaterial model subroutine was developed; this led to a more comprehensivematerial model. A fractional factorial simulation with over 180 simulations wascarried out and validated with the machining experiment.Based on the experimental and simulation results, a soft prediction model forsurface integrity was established by using Artificial Neural Networks (ANN)approach. These predictions for SI were then used in a Genetic Algorithmmodel to optimise the SI. The confirmation tests showed that the machiningstrategy was successfully optimised and the average fatigue duration wasincreased 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 toollead angle of 16.045° provided the good surface integrity and increased fatigueperformance. Overall, these findings conclude that the fundamentals andmethodology utilised have developed a further understanding betweenmachining and forming/forging process, resulting in a good foundation for aframework to generate FE and soft prediction models which can be used to inoptimisation of precision milling strategy for different materials.
- Advisor / supervisor
- Fitzpatrick, Stephen
- Luo, Xichun (Manufacturing teacher)
- Resource Type
- Note
- Previously held under moratorium from 8 August 2019 until 19 January 2022
- DOI
- Funder
Relations
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