Offer 1
Foundation Models for Process Monitoring in Vibration-Assisted Drilling
English summary
The project investigates foundation models for process monitoring in vibration-assisted drilling and compares them with classical machine-learning models. Students will review open-source models, evaluate data formats for process data, benchmark models, and investigate fine-tuning strategies and their effects on accuracy and robustness.
Overview
- Project details
- Foundation Models for Process Monitoring in Vibration-Assisted Drilling
Source excerpt · Page 1
- “# **Foundation Models for Process Monitoring in Vibration-Assisted Drilling**” Page 1 ↗
- Organization
- Not stated in the PDF
- Project formats
- Not stated in the PDF
- Degree levels
- Not stated in the PDF
- Project goal
- Systematically investigate the suitability of foundation models for process monitoring in VAD and compare them with small, classical machine-learning models.
Source excerpt · Page 1
- “The goal of this work is to systematically investigate the suitability of such foundation models for process monitoring in VAD and compare them with small, classical machinelearning models.” Page 1 ↗
Topics and work
- Subjects
- Foundation models for time-series and tabular data
Source excerpt · Page 1
- “Large, pre-trained models for time-series and tabular data could also be used for tool-wear estimation with only a small amount of process-specific data.” Page 1 ↗
- Process monitoring in vibration-assisted drilling (VAD)
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- “The goal of this work is to systematically investigate the suitability of such foundation models for process monitoring in VAD and compare them with small, classical machinelearning models.” Page 1 ↗
- Tool-wear estimation
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- “Large, pre-trained models for time-series and tabular data could also be used for tool-wear estimation with only a small amount of process-specific data.” Page 1 ↗
- Foundation models for time-series and tabular data
- Application areas
- Aerospace industry
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- “In the aerospace industry, holes in multi-layer material stacks are generally produced using industrial robots.” Page 1 ↗
- Vibration-assisted drilling of multi-layer material stacks
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- “During vibration-assisted drilling (VAD), multi-channel sensor signals and quality data are recorded.” Page 1 ↗
- Aerospace industry
- Methods and tools
- Open-source models
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- “Literature review and overview of suitable open-source models” Page 1 ↗
- Multi-channel sensor signals and quality data
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- “During vibration-assisted drilling (VAD), multi-channel sensor signals and quality data are recorded.” Page 1 ↗
- Classical machine-learning models
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- “Benchmarking against classical machine-learning models” Page 1 ↗
- Fine-tuning strategies
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- “Investigation of fine-tuning strategies and their influence on accuracy and robustness” Page 1 ↗
- Open-source models
- Activities
- Review literature and survey suitable open-source models
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- “Literature review and overview of suitable open-source models” Page 1 ↗
- Evaluate data formats and applicability to process data
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- “Evaluation of data formats and applicability to process data” Page 1 ↗
- Benchmark against classical machine-learning models
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- “Benchmarking against classical machine-learning models” Page 1 ↗
- Investigate fine-tuning strategies and their influence on accuracy and robustness
Source excerpt · Page 1
- “Investigation of fine-tuning strategies and their influence on accuracy and robustness” Page 1 ↗
- Review literature and survey suitable open-source models
- Kinds of work
- Expected outputs
- Not stated in the PDF
Requirements
- Required skills
- Programming skills
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- “Programming skills, ideally in Python and PyTorch” Page 1 ↗
- Interest in machine learning and process monitoring
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- “Interest in machine learning and process monitoring” Page 1 ↗
- Initiative
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- “Initiative, a structured approach to work, reliability, and commitment” Page 1 ↗
- A structured approach to work
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- “Initiative, a structured approach to work, reliability, and commitment” Page 1 ↗
- Reliability
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- “Initiative, a structured approach to work, reliability, and commitment” Page 1 ↗
- Commitment
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- “Initiative, a structured approach to work, reliability, and commitment” Page 1 ↗
- Programming skills
- Recommended skills
- Eligible study fields
- Not stated in the PDF
- Programming in the project
- Not stated in the PDF
- Programming prerequisite
- Required
Source excerpt · Page 1
- “Programming skills, ideally in Python and PyTorch” Page 1 ↗
Practical details
- Team size
- Not stated in the PDF
- Duration
- Not stated in the PDF
- Start
- Not stated in the PDF
- Application deadline
- Not stated in the PDF
- Work location
- Not stated in the PDF
- Location mode
- Not stated in the PDF
- Working language
- Not stated in the PDF
Learning and support
- Learning opportunities
- Not stated in the PDF
- Support offered
- Supervision by Prof. Dr.-Ing. Michael F. Zäh
Source excerpt · Page 1
- “**Supervising Professor:** Prof. Dr.-Ing. Michael F. Zäh” Page 1 ↗
- Supervision by Prof. Dr.-Ing. Michael F. Zäh
Application and contacts
- Contacts
- M. Sc. Charlotte Winkler · charlotte.winkler@iwb.tum.de
Source excerpt · Page 1
- “**M. Sc. Charlotte Winkler** Machine Tools Department charlotte.winkler@iwb.tum.de” Page 1 ↗
- M. Sc. Charlotte Winkler · charlotte.winkler@iwb.tum.de
- How to apply
- Not stated in the PDF
- Further information
- Not stated in the PDF