PhD position in digital manufacturing and automation

We are offering a PhD position in the field of manufacturing and automation systems. You will become a member of Manufacturing Processes and Systems (MaPS) division of the Mechanical Engineering Department at KU Leuven. MaPS is active in academic research in the field. MaPS also has a very strong focus on application-driven research and development in close collaboration with industry. The knowledge gained from steady research over the years has resulted in multiple spin-off companies.

Project

The process of creating a finished part from the CAD file is commonly called the CAD-to-CAM process resulting in a workplan. Due to the high complexity, this process is typically performed by an experienced operator. It includes the planning of different processing steps, e.g., cutting methods, machining patterns, and sequences. A traditional Computer-Aided Design & Manufacturing (CAD-to-CAM) workflow assists in this, but is limited to validating the outcome of the operator decisions. Selecting the tool, clamping strategy, cut patterns, and cutting parameters requires the availability of experienced operators, which is a challenge today. Especially in case of complex parts, CAD-to-CAM is a tedious manual process and often results in suboptimal designs, both in terms of material removal rate, path length, idle time, tool change frequency and quality. 

The proposed thesis intends to support the operator by largely automating the workplan generation, both for existing and new parts. In this project you will focus on mainstream machining processes namely turning, milling and drilling. This is realized by leveraging 3 data sources: (i) the product design data (CAD files), (ii) historical workplans and process information, including machining parameters, constraints, tools and strategies (i.e., the selection of processing types and their sequence), and (iii) historic machining quality data. You will be first creating virtual machines with minimum uncertainty. Once an acceptable match between the real and virtual machine is achieved, you would leverage machine learning machine learning to generate new and improve existing workplans.

Profile

You share strong interest in design and manufacturing, and you have:

• MSc in Mechanical engineering. Computer science or Mechatronics (or equivalent)
• some experience (courses/projects) with programming (C++ or python)
• any prior experience with CNC machine tools is beneficial but not mandatory
• affinity for both model based (physical models) and machine learning analysis
• an analytical mind
• technical/practical implementation skills
• good English written and oral communication skills.

Offer

We offer a full-time position as researcher with a PhD trajectory in mechanical engineering:

• you will join a supportive and collaborative team in which you can develop know-how and expertise in state-of-the-art (digital-) manufacturing systems
• you will have the opportunity to build up research and innovation skills that are essential for a future career in academia or in industry
• you will work in a multicultural working environment at the KU Leuven High Tech Campus

Interested?

For more information please contact Prof. Dominiek Reynaerts, tel.: +32 16 32 26 40, mail: dominiek.reynaerts@kuleuven.be or Dr. Shashwat Kushwaha, tel.: +32 16 32 64 50, mail: shashwat.kushwaha@kuleuven.be.

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