Project Description
The aim of this PhD is to develop new data analytic tools (e.g. machine learning, data mining) to support the understanding, the optimisation and the Multi-scale and multi-physics simulation of metal Additive Layer Machining (ALM) process chains.
These data analytics tools should meet the needs of the H2020 funded project MANUELA. In particular, to develop “intelligent” feedback loops enabling “online” manufacturing optimisation, design optimisation and tuning of Multi-scale and multi-physics models used for simulations and for the implementation of accurate digital twins of the investigated pilot lines.
Depending on the type of data available (e.g. temperature maps, machining parameters, localised acoustic information) and on the available controllable factors, various types of process modelling approaches could be used to extract knowledge and features. State of the art modeling, data mining and machine learning tools will be reviewed (e.g. techniques for data regression / classification / clustering such as deep neural network, support vector machine, and dimension reduction learning models, as well as image processing algorithms) and the most relevant will be implemented and enhanced to meet the demands of real data collected at different stages of the pilot line.
Specialist Equipment / Ressources available:
Among other standard computing and manufacturing equipment (manufacturing workshop, 3D printers, ) , the student will have access to the following ressources specific to the project needs:
- Metal Additive Layer Manufacturing Machine
- High-Performance Computing cluster
- Machine learning tool kit
- Indirect access to the H2020 project partners’ equipment (e.g. ALM machines, data analysis/control/simulation softwares)
Student Required Expertise/skills:
The work will require the development of software based solutions in the context of ALM pilot lines (real manufacturing, simulation and optimisation), the student should have strong interest and knowledge in the following:
- Object oriented programming (C++ or equivalent)
- Data mining/machine learning
- Additive Layer Manufacturing
These data analytics tools should meet the needs of the H2020 funded project MANUELA. In particular, to develop “intelligent” feedback loops enabling “online” manufacturing optimisation, design optimisation and tuning of Multi-scale and multi-physics models used for simulations and for the implementation of accurate digital twins of the investigated pilot lines.
Depending on the type of data available (e.g. temperature maps, machining parameters, localised acoustic information) and on the available controllable factors, various types of process modelling approaches could be used to extract knowledge and features. State of the art modeling, data mining and machine learning tools will be reviewed (e.g. techniques for data regression / classification / clustering such as deep neural network, support vector machine, and dimension reduction learning models, as well as image processing algorithms) and the most relevant will be implemented and enhanced to meet the demands of real data collected at different stages of the pilot line.
Specialist Equipment / Ressources available:
Among other standard computing and manufacturing equipment (manufacturing workshop, 3D printers, ) , the student will have access to the following ressources specific to the project needs:
- Metal Additive Layer Manufacturing Machine
- High-Performance Computing cluster
- Machine learning tool kit
- Indirect access to the H2020 project partners’ equipment (e.g. ALM machines, data analysis/control/simulation softwares)
Student Required Expertise/skills:
The work will require the development of software based solutions in the context of ALM pilot lines (real manufacturing, simulation and optimisation), the student should have strong interest and knowledge in the following:
- Object oriented programming (C++ or equivalent)
- Data mining/machine learning
- Additive Layer Manufacturing
https://www.findaphd.com/search/projectDetails.aspx?PJID=99195
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