3D temperature field reconstruction from local temperature monitoring in directed energy deposition PhD

We are seeking a highly motivated candidate to undertake a PhD program titled '3D Temperature Field Reconstruction from Local Temperature Monitoring in Directed Energy Deposition.' This exciting research opportunity focuses on advancing large-scale additive manufacturing using metal wire as feedstock and electric arc as the heat source. The project aims to develop an innovative and efficient method for reconstructing full 3D temperature fields from partial temperature measurements, utilising monitoring techniques deployable in industrial settings. This work will provide critical thermal insights for diagnosing and controlling defects and lay the foundation for a thermal physics-based approach to process qualification.

Additive manufacturing (AM) is a rapidly evolving technology that continues to drive innovation and find diverse applications across industries such as aerospace, energy, and automotive. Among its various techniques, wire-arc directed energy deposition (WA-DED) stands out as a highly promising approach for fabricating large-scale structural components. This process involves feeding a metal filler wire—either coaxially or off-axis—into an electric arc to generate a molten pool that solidifies on a substrate, enabling the layer-by-layer construction of complex 3D objects. The temperature field created by the interaction between the electric arc and the material is a critical factor influencing the microstructure, residual stress, and distortion of the deposited parts, all of which significantly impact their mechanical properties and overall performance. Consequently, accurately determining and effectively controlling the temperature field are essential to achieving a high-quality WA-DED process.

Accurately determining thermal variables during AM processes remains a significant challenge due to limitations in both measurement and modelling techniques. Current in-process measurement methods are restricted to surface-only monitoring devices (e.g., cameras and pyrometers), which fail to capture subsurface and internal temperature distributions. Semi-destructive approaches, such as embedding thermocouples by drilling holes, can provide internal data but often disrupt the process, alter the thermal fields, and risk damaging the part during fabrication. Finite element analysis (FEA) models, while capable of delivering detailed spatiotemporal distributions of thermal variables, suffer from limited predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing the computational inefficiencies of physics-based models and enabling faster, potentially more accurate predictions. However, AI models require substantial volumes of high-quality, labelled training data, which are often costly and time-intensive to generate experimentally in engineering applications.

This project seeks to overcome these challenges by integrating modelling and monitoring to achieve fast, accurate conversion of 2D surface temperature measurements into 3D temperature fields. High-fidelity FEA models will be developed to generate the necessary data for constructing a novel temperature reconstruction method. Ultimately, this reconstruction approach will be validated and applied in conjunction with industrially viable temperature monitoring techniques, paving the way for enhanced process control and defect mitigation in AM.

Key research objectives and activities include:
- Establishing a comprehensive understanding and database of WA-DED-induced surface and internal temperatures through FEA simulations, covering a range of typical part geometries and deposition strategies, complemented by experimental validation.
- Developing an efficient method for converting partial surface temperature data into full-surface temperature profiles. Key outcomes include defining the required locations and extent of temperature monitoring to enable accurate data conversion.
- Creating a practically deployable method for the rapid reconstruction of 3D temperature fields based on monitored surface temperatures.
- Validating the temperature reconstruction method through experiments, utilising temperature monitoring techniques suitable for industrial applications.

The student will be based at the Welding and Additive Manufacturing Centre (WAMC), a renowned hub for impactful research into advanced fusion-based processing and manufacturing methods. This project will be closely aligned with the ATI research program (I-Break: Wire-based DED Technology Maturation and Landing Gear Application) and other industrial research projects within WAMC. There will be opportunities to work with WAMC's industrial partners, such as WAAM3D and members of WAAMMat, gaining valuable industry experience and exposure.

The student is expected to acquire the following knowledge and skills:
- Techniques, requirements, and applications of metal additive manufacturing
- Analysis of the temperature evolution and distribution in wire-based additive manufacturing
- Calibration and validation experiments for modelling
- Temperature monitoring techniques
- Finite element analysis method
- Reviewing literature, planning and managing research, writing technical reports/papers, presenting in meetings/conferences, and teamwork

Entry Requirements:
Applicants should hold the equivalent of a first or second-class UK honours degree in a related discipline, such as mechanical, manufacturing, or materials engineering. International candidates must also meet the English language requirements set by Cranfield University. This project is ideal for individuals with a strong interest in modelling, monitoring and manufacturing, along with a solid understanding of process physics and heat transfer. Previous experience with thermal processes or additive manufacturing would be highly advantageous. The successful candidate should demonstrate self-motivation, proactivity, and good communication and teamwork skills.

Funding:
This is a self-funded PhD. The cost for running experiments and accessing research facilities will be supported by the Welding and Additive Manufacturing Centre.

Supervisors: Dr Yongle Sun (1st supervisor), Dr Shuo Feng (2nd supervisor).