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idp-108 · Informatics IDP Hub

Online Process Monitoring to Enable Adaptive Drilling Processes

Automatically extracted from the linked project document. Check the original PDF before relying on a detail.

Chair listing page ↗Original PDF ↗

Document language: English · Pages: 1 · Extracted: 25 Sept 2026, 23:23

Offer 1

Online Process Monitoring to Enable Adaptive Drilling Processes

Page 1 ↗
English summary

The project investigates live monitoring for vibration-assisted drilling of multi-layer material stacks, aiming to estimate hole quality or tool wear in time to support process adaptation. Students evaluate sensor signals, determine usable data windows, detect parameter and material changes, compare methods for live signals, and assess latency, computational effort, and potential adaptation triggers.

Overview

Project details
Online Process Monitoring to Enable Adaptive Drilling Processes
Source excerpt · Page 1
  • “Online Process Monitoring to Enable Adaptive Drilling Processes” Page 1 ↗
Organization
Not stated in the PDF
Project formats
Not stated in the PDF
Degree levels
Not stated in the PDF
Project goal
Investigate reliable live monitoring and the proportion of drilling-process signals required to estimate hole quality or tool wear quickly enough to enable adaptation during drilling.
Source excerpt · Page 1, Page 1
  • “The goal is to investigate how reliable live monitoring can be implemented and what proportion of the signals from the entire drilling process is required.” Page 1 ↗
  • “Ideally, this should allow hole quality or tool wear to be estimated quickly enough to enable process adaptation during drilling.” Page 1 ↗

Topics and work

Subjects
  • online process monitoring
    Source excerpt · Page 1
    • “Evaluation of available sensor signals for online monitoring” Page 1 ↗
  • vibration-assisted drilling
    Source excerpt · Page 1
    • “During vibration-assisted drilling (VAD), multi-layer material stacks are machined, and their properties change during the drilling process.” Page 1 ↗
  • adaptive drilling processes
    Source excerpt · Page 1
    • “Online Process Monitoring to Enable Adaptive Drilling Processes” Page 1 ↗
  • change-point detection
    Source excerpt · Page 1
    • “Change-point detection for parameter and material changes” Page 1 ↗
Application areas
  • drilling multi-layer material stacks
    Source excerpt · Page 1
    • “During vibration-assisted drilling (VAD), multi-layer material stacks are machined, and their properties change during the drilling process.” Page 1 ↗
Methods and tools
  • forces, spindle currents, and machine-internal signals
    Source excerpt · Page 1
    • “Forces, spindle currents, and machine-internal signals can provide indications of parameter changes, material-layer changes, tool wear, and process quality.” Page 1 ↗
  • change-point detection
    Source excerpt · Page 1
    • “Change-point detection for parameter and material changes” Page 1 ↗
  • live signals
    Source excerpt · Page 1
    • “Comparison of suitable methods for live signals” Page 1 ↗
Activities
  • Evaluate available sensor signals for online monitoring
    Source excerpt · Page 1
    • “Evaluation of available sensor signals for online monitoring” Page 1 ↗
  • Determine the smallest usable data window within a drilling process
    Source excerpt · Page 1
    • “Determination of the smallest usable data window within a drilling process” Page 1 ↗
  • Detect parameter and material changes
    Source excerpt · Page 1
    • “Change-point detection for parameter and material changes” Page 1 ↗
  • Compare suitable methods for live signals
    Source excerpt · Page 1
    • “Comparison of suitable methods for live signals” Page 1 ↗
  • Evaluate latency, computational effort, and possible triggers for adaptive parameter adjustment
    Source excerpt · Page 1
    • “Evaluation of latency, computational effort, and possible triggers for adaptive parameter adjustment” Page 1 ↗
Kinds of work
  • Data analysis and ML
    Source excerpt · Page 1
    • “Evaluation of available sensor signals for online monitoring” Page 1 ↗
Expected outputs
Not stated in the PDF

Requirements

Required skills
  • Programming skills
    Source excerpt · Page 1
    • “Programming skills, ideally in Python and PyTorch” Page 1 ↗
  • Interest in machine learning and process monitoring
    Source excerpt · Page 1
    • “Interest in machine learning and process monitoring” Page 1 ↗
  • Initiative
    Source excerpt · Page 1
    • “Initiative, a structured approach to work, reliability, and commitment” Page 1 ↗
  • A structured approach to work
    Source excerpt · Page 1
    • “Initiative, a structured approach to work, reliability, and commitment” Page 1 ↗
  • Reliability
    Source excerpt · Page 1
    • “Initiative, a structured approach to work, reliability, and commitment” Page 1 ↗
  • Commitment
    Source excerpt · Page 1
    • “Initiative, a structured approach to work, reliability, and commitment” Page 1 ↗
Recommended skills
  • Python and PyTorch
    Source excerpt · Page 1
    • “Programming skills, ideally in Python and PyTorch” Page 1 ↗
  • Experience with time-series data
    Source excerpt · Page 1
    • “Experience with time-series data is advantageous, but not required” Page 1 ↗
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
Not stated in the PDF

Application and contacts

Contacts
How to apply
Not stated in the PDF
Further information
Not stated in the PDF