The student Muhammad Sajjad obtained an EXCELLENT
The student Muhammad Sajjad obtained an EXCELLENT
The student Muhammad Sajjad obtained an EXCELLENT
- Thesis title: Digital Twin Development for the Prediction and Optimization of the Near Solidus Forming process at the Industrial Scale
Court:
- Presidency: Carl Slater (University of Warwick)
- Vocal: Eduardo Garcia Gil (Universidad de Deusto)
- Vocal: Gorka Plata Redondo (Hephae Energy Technology)
- Vocal: Lander Galdos Errasti (Mondragon Unibertsitatea)
- Secretary: Zigor Azpilgain Balerdi (Mondragon Unibertsitatea)
Abstract:
The reduction of raw materials has become a critical concern across all industries due to the growing awareness of the global environmental crisis. As a result, industries are increasingly adopting sustainable manufacturing practices that prioritize low energy consumption and minimize material waste. These measures are crucial in directly reducing greenhouse gas emissions.
Forging process is one of them, which is widely used in the automobile, aerospace, shipbuilding and construction sectors. Alone in the automotive industry the forging market was worth 33.5 billion USD in 2020 and expected to grow with a compound annual growth rate of 6.2 % each year. The sheer size of the process and its impact on the environment represent a great opportunity for the researchers to optimize the process.
Many industries tackle the challenges of energy and material waste by employing advanced materials such as lightweight alloys and composites. Others adopt different manufacturing techniques, such as Near Solidus Forming (NSF), which remains relatively rare in industrial applications. The NSF process operates at temperatures near the solidus state of the material, blending the advantages of both classical hot forging and casting. This approach enables manufacturing with low forces while achieving good mechanical properties, thereby saving energy and reducing raw material consumption. Despite its great potential, the complexity of the NSF process presents significant challenges for large-scale industrial implementation.
Hence, to solve this, the objective of the work is to create a Digital Twin capable of predicting and optimizing the NSF process at the industrial scale.
For this reason, this document aims to provide an overview of the NSF process, including its parameters and a sensitivity analysis. A comprehensive literature review has been conducted for this purpose. Based on this critical review, a research plan has been designed to understand, characterise, and model the Digital Twin of the NSF for three industrial components. Later various characterization techniques with both experimental and numerical analysis were deployed to optimize the developed DT at the NSF conditions. Finally, the preliminary results obtained from the Digital Twin is validated through experimental trials.