The student Jesús David Chaux Sánchez obtained an EXCELLENT CUM LAUDE

The student Jesús David Chaux Sánchez obtained an EXCELLENT CUM LAUDE

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• Thesis title: Predicting stable cutting conditions for aluminium alloys machining using PCD tools

Court:

• Presidency: Joaquín Barreiro García (Universidad de León)
• Vocal: Irene Buj Corral (Universitat Politècnica de Catalunya)
• Vocal: Rosario Domingo Navas (UNED)
• Vocal: Harry Yasir Otalora Ortega (Mondragon Unibertsitatea)
• Secretary: Iñaki Mirena Arrieta Galdos (Mondragon Unibertsitatea)

Abstract:

The automotive industry widely uses aluminium alloy components to reduce vehicle weight. Machining these components includes roughing operations to remove bulk material and finishing operations to create functional surfaces. Manufacturers must achieve minimal cycle time in roughing and extended tool life in finishing to maximise return on investment (ROI). Nevertheless, providing accurate estimates of cycle time and tool costs in the early stages, such as the quotation stage, remains challenging.
At this initial phase, only CAD geometries and material specifications exist without physical tools or validated parameters. Overestimating costs risks losing contracts whilst underestimating leads to losses from rework or delayed deliveries. In roughing operations, parameter selection must account for chatter stability, spindle limits, and restrictions on axis kinematics and feed forces. Predicting chatter-free conditions requires tool tip dynamics, but existing methods demand extensive experimental testing that is impractical. Production engineers need software that provides practical cutting parameter recommendations. In finishing operations, the cutting tool defines the machined surface quality. Polycrystalline diamond tools are commonly used for aluminium finishing, where tool life and replacement frequency directly affect production costs. However, tool selection relies on general manufacturer recommendations or trial-and-error approaches. Research concerning tool life under high-volume production conditions remains limited, with most studies conducted in controlled laboratory environments.

This research addresses these challenges through two complementary approaches for automotive aluminium component machining. For roughing operations, an integrated methodology enables parameter selection in early phases. Receptance coupling substructure analysis was developed to predict tool tip dynamics from CAD geometry. Machine learning models identify material variations along cutting tools directly from STL files. A framework integrates these predictions with chatter stability analysis, spindle limits, feed forces, and machine kinematics to select stable cutting conditions and predict cycle times. This framework was implemented in DIGICUT, a modular desktop software for industrial applications. Two case studies in steering knuckle machining validated the methodology, achieving cycle time reductions of 29% and 95% under stable conditions.

For finishing operations, the characterisation of the long-term performance of polycrystalline diamonds established criteria for tool selection under industrial conditions. Industrial trials monitored polycrystalline diamond tool wear, surface finish, and burr formation across 60,000 face-milled transmission cases. Microchipping was identified as the dominant wear mechanism. Medium-grained polycrystalline diamonds with larger nose radii demonstrated superior tool life compared to fine-grained alternatives with reduced nose radii. A three-phase wear progression analysis was presented to inform predictive tool replacement in the analysed case study.