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B.Sc. Thesis – Franz Waldsam: EAGLE – N²ET
Estimating Aerospace manufacturing time from Geometry Leveraging Encoder Neural Network

Supervisor: Univ.-Prof. Dr Elmar Rückert
Start date: 1st March 2023

Involved Company: voestalpine Böhler Aerospace GmbH & Co KG 

 

Theoretical difficulty: mid
Practical difficulty: mid

Abstract

Geometric data of a requested forging is important as a source to estimate feasibility and offer realistic pricing. However, every bigger deviation in such calculation regarding technical viability costs involved companies’ possible revenue. 

To mitigate this issue and support the technologists and sales department an autoencoder (unsupervised learning) with an attached regression model was developed (pre-existing). Nevertheless, this system still needs adaptation/improvement to meet the operational requirements. 

This bachelor thesis proposes a way to implement an optimization process for adjusting the layer structure and possible scaling of a given autoencoder system. The autoencoder itself uses 3D surface data in form of a “.stl” to create a point cloud in x, y, and z. A docker image containing the autoencoder then extracts the most significant 3D features and provides an estimation for feasibility and price. The focus lies on creating a wrapper function to test different hyperparameters in an automated way. Strategies like random search, grid search, and Bayesian optimization will be applied. The results of the optimized framework will be challenged with the pre-existing autoencoder model.

Tentative Work Plan

To achieve our objective, the following concrete tasks will be focused on:

  • Literature research
  • Evaluation of the SOTA / the current model
  • Identification of network / hyperparameter optimization options
  • Model optimization / improvement
  • Evaluation and Testing on new data



Franz Waldsam

Bachelor Thesis Student at the Montanuniversität Leoben

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Short bio: Franz has already a master degree in metallurgy but seeks additional expertise in data analysis and machine learning, therefore currently revisiting the Montanuniversität Leoben as bachelor student in Industrial Data Science.

Graduated in 2015 he went into the quality management of the domestic steel industry. Working in a laboratory environment within a very dynamic market he quickly noticed the unstoppable tendencies to more and more data driven process planning, monitoring and production itself. Therefore, as of March 2023, he is writing his bachelor thesis at the Chair of Cyber-Physical Systems in cooperation with voestalpine Boehler Aerospace GmbH & Co KG.

Research Interests

  • Robotics

Thesis

  • EAGLE – N²ET Estimating Aerospace manufacturing time from Geometry Leveraging Encoder Neural Network
  • Supervision: Elmar Rueckert

Contact

Franz Waldsam
Bachelor Thesis Student at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 




Meeting Notes March 2023

Meeting 10/03

Research

  • Plan to participate in the air hockey challenge
  • Literature review for the right model

PhD Registration

  • todo: prepare Email

M.Sc. Students/Interns

  • Iye Szin work plan
  • Internship will lead to her thesis

ML Assistantship

  • Syllabus
  • Prepare exercises 

ML Course

  • Moodle to upload files (discussed)
  • Link to latex for the report (done)

Miscellaneous

  • No time to attend the research seminar, ML course takes too much of my time. (discussed)
  • 2 days work from home 31.05 & 01.06
  • Vacation 02.06 – 11.06
  • Medium GPUs for WS in the lab (RTX 3060 or 3070)

 

Meeting 13/03

Research

  • Rebuttal

ML Course

  • Assignment 1 preparation

Meeting 23/03

Research

  • respond to ICML Chairs about reviewer 1
  • Searched for alternative conferences
    • ECAI
    • BCCV
  • Literature review on SSL problems
  • RL Revision

M.Sc. Students/Interns

  • Iye Szin steady progress

Ph.D. registration

  • Email send to Toussaint

ML Course

  • Assignment 1 grades
  • post pdf

Miscellaneous

  • Summer School Applications
  • Paper Review accepted for IROS 2023
  • fill the form for IAS retreat

 

Meeting 30/03​

Research

  • waiting for ICML final decision
  • when out, I will compile the comments
    • data augmentation influence on MI
    • etc
  • submit to
  • ECAI
    • ICVS ranking is C
  • Next on: Dimensionality collapse in representation learning
    • currently reading about it
  • Air hockey challenge
    • start with SAC
    • continue with a model-based RL method, like world models

M.Sc. Students/Interns

  • Iye Szin struggling with ROS2 but in a logical frame

Ph.D. registration

  • Email sent to Toussaint. Waiting for responce

ML Course

  • Assignment 3 is out

Miscellaneous

  • Summer School Applications
  • Paper Review for IROS 2023
  • submitted the application for IAS retreat

 

Li Jing, Pascal Vincent, Yann LeCun, & Yuandong Tian (2021). Understanding Dimensional Collapse in Contrastive Self-supervised Learning. arXiv preprint arXiv:2110.09348.



Email Verteiler – Mailing Lists

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Forschungsnetzwerk

  • robotics-worldwide@lists.kit.edu
  • https://groups.google.com/g/ML-news
  • https://lists.psfc.mit.edu/mailman/listinfo/machine_learning



How to setup Gitea SSH Connection

Follow the following Tutorial:

https://www.techaddressed.com/tutorials/add-verify-ssh-keys-gitea/




13.03.2023 – Innovative Research Discussion

Meeting notes on the 13th of March, 2023

Location: Chair of CPS

Date & Time: 13th March, 2023, 11:45 pm to 12:45 pm

Participants: Univ.-Prof. Dr. Elmar Rueckert, Linus Nwankwo, M.Sc.

Agenda

  1. General Discussion
  2. Discussion on research progress
  3. Next action

General Discussion

  1.  The applied machine and deep learning course start on 02.10.2023.
  2.  Study the publication [1] for the next work.

Do next

  1. 3D complete coverage on quadruped robot for mine inspection tasks.
  2. Prior in SLAM from architectural floor plans.


Reference

[1]    Song, Soohwan & Kim, Daekyum & Jo, Sungho. (2020). Online coverage and inspection planning for 3D modelling. Autonomous Robots. 44. 10.1007/s10514-020-09936-7.




190.015 Applied Machine and Deep Learning (5SH IL, WS)

Course Content

In the first week, advanced machine and deep learning methods like multi-layer-perceptrons, convolutional neural networks, variational autoencoder, transformers, simultaneous navigation and mapping approaches, and more will be presented.

These methods can be tested using interactive tools like for example using   https://playground.tensorflow.org. To deepen the knowledge, students will answer well-crafted scientific questions using latex handouts alone or in teams of two students in the lecture room. 

Additionally, Jupyter notebook files were prepared to implement advanced machine and deep learning approaches without installing any software. For all participants of the course user accounts will be created using our JupyterHub at https://jupyter.cps.unileoben.ac.at. The accounts will remain active till the end of the semester. 



MON TUE WED THUR FRI


02.10.2023 03.10.2023 04.10.2023 05.10.2023 06.10.2023
Topic Intro to ML Organisation Neural Networks Representation Learning Robot Learning AML Projects
10 am




:15 Quizz on ML Quizz on Neural Nets Introduction to Deep Representation Learning: Core Methods & Coding Examples
Quizz on AML

:30 Introduction to ML Introduction to Multi-Layer-Perceptrons
Project Topic Presentations

:45
11 am 15 min Break 15 min Break
:15 Statistics, Model Validation, Figures & Evaluations Handout on Neural Networks using playground.tensorflow
Team Ass., Git Repos & Wiki Instructions

:30 30 min Break

:45
AML Summary
12 pm 30 min Lunch Break 30 min Lunch Break Curiosity (MLPs), Imagination (Dreamer) and Information (Empowerment)

:15 Quizz on Robotics

:30 Course Organisation & Grading Intro to Timeseries & Databases Introduction to Robot Learning

:45
1 pm 15 min Break 15 min Break Quizz Summary
:15 Python Programming with our JupyterHub JupyterHub NB on MLPs & Databases
15 min Break

:30
Handout on Robot Learning (Model Learning & RL)

:45 Quizz Summary Quizz Summary

2 pm



:15


15 min Break

:30


Introduction to Mobile Robotics & SLAM

:45



3 pm


JupyterHub NB on Path Planning
:15




:30


Quizz Summary

:45













Legend





Quizz on ML Online Quizz using https://tweedback.de




Course Content Presentation Using google slides, etc.







15 min Break Breaks to recover or to continue programming




Organisation & Instructions Using google slides, etc.







Practical Exercise Using online tools, our JupyterHub, etc.







Latest Research State-of-the-art research





Prerequisites & If you Miss Course Contents

During the first week, a laptop or tablet will be needed to use the interactive tools and the Jupyter notebooks. 

Webex Online Sessions of the 1st Week

Find here the link to the online stream during the first week in October, 2023: https://unileoben.webex.com/unileoben/j.php?MTID=m5492385776dd885ca5dde72e52563c61

When you miss some course contens

If you miss some course contents due to overlapping events, you can watch recordings of the sessions online. All recordings will be hosted via Moodle at https://moodle.unileoben.ac.at/course/view.php?id=3082.

Course Description

Modern machine learning methods and in particular deep learning methods are entering almost all areas of engineering. 

The integrated course enables the students to apply these methods in the application domains of their study.

For this purpose, current problems from the industry are investigated and the possibilities of machine and deep learning methods are tested.

Students gain a deep understanding of method implementations, how data must be prepared, which criteria are relevant for selecting learning methods, and how evaluations must be performed in order to interpret the results in a meaningful way.

Initially, the basics of learning methods are developed in 5-6 lecture units. Then, students select one of the listed industrial problems and work on it alone or as a team (with extended assignments). The project work is accompanied by weekly tutorials with tips and tricks. Finally, the project results are discussed in a written report and presented for a final 10-15min.

Grading is based on the quality of the code, the report, and the final short presentation.

Among others, one of the following industry problems can be chosen:

1. Application and comparison of deep neural networks for steel quality prediction in continuous casting plants with data from the ‘Stahl- und Walzwerk Marienhütte GmbH Graz’.

2. Predictive maintenance of bearing shells using frequency analysis in decision trees and deep neural networks based on acoustic measurement data.

3. Motion analysis and path planning for human-machine interaction in logistics tasks with mobile robots of the Chair of CPS.

4. Autonomous navigation and mapping with RGB-D cameras of the four-legged robot Unitree Go1 for excavation inspection in mining.

The project list is continuously extended.

Links and Resources

Location & Time

 

Previous Knowledge Expected

Formal Prerequisite: Basic Python programming skills, Fundamentals of Statistics.

Recommended Prerequisites:
Introduction to Machine Learning (“190.012” and “190.013”).

Slides

Learning objectives / qualifications

  • Implement or independently adapt modern machine learning methods and in particular deep learning methods in Python.
  • Analyze data of complex industrial problems, process (filter) the data, and divide it into training- and test data sets such that a meaningful interpretation is possible.
  • Define criteria and metrics to evaluate evaluations and predictions and generate statistics.
  • Develop, evaluate, and discuss meaningful experiments and evaluations.
  • Identify and describe assumptions, problems, and ideas for improvement of practical learning problems.

Grading

Continuous assessment: During the lectures and the tutorials 0-20 bonus points can be collected through active participation.

Project assignments: Alone or in small groups (2-3 students) one of the listed projects has to be implemented. A written report in form of a git repository wiki page have to be submitted.
– For the implementation (Python Code) 0-40 Points can be obtained.
– For the wiki page report, 0-60 Points will be given.

Grading scheme: 0-49,9 Points (5), 50-65,9 Points (4), 66-79 Points (3), 80-91 Points (2), 92-100 Points (1).

With an overall score of up to 79%, an additional oral performance review MAY (!) also be required if the positive performance record is not clear. In this case, you will be informed as soon as the grades are released. If you have already received a grade via MU online, you will not be invited to another oral performance review.

Literature

Maschine Learning and Data-modelling:

– Rueckert Elmar 2022. An Introduction to Probabilistic Machine Learning, https://cloud.cps.unileoben.ac.at/index.php/s/iDztK2ByLCLxWZA

– James-A. Goulet 2020. Probabilistic Machine Learning for Civil Engineers. MIT Press.

– Bishop 2006. Pattern Recognition and Machine Learning, Springer.

Learning method Programming in Python:

– Sebastian Raschka, YuxiH. Liu and Vahid Mirjalili 2022. Machine Learning with PyTorch and Scikit- Learn. Packt Publishing Ltd, UK.

– Matthieu Deru and Alassane Ndiaye 2020. Deep Learning mit TensorFlow, Keras und TensorFlow.js., Rheinwerk-verlag, DE. 

Problemspecific Litheratur:

– B. Siciliano, L.Sciavicco 2009. Robotics: Modelling, Planning and Control, Springer.

– Kevin M. Lynch and FrankC. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.

– E.T. Turkogan 1996. Fundamentals of Steelmaking. Maney Publishing,UK.




Gabriel Brinkmann

Bachelor Thesis Student at the Montanuniversität Leoben

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Short bio: Gabriel is a Bachelor Student in Mechanical Engineering at Montanuniversität Leoben and, as of March 2023, is writing his Bachelors thesis at the Chair of Cyber-Physical Systems.

Research Interests

  • Robotics

Thesis

Contact

Gabriel Brinkmann
Master Thesis Student at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Email:   




B.Sc. Thesis – Gabriel Brinkmann: Simultaneous localization and mapping (SLAM) with a quadrupedal robot in challenging real-world environments

Supervisor: Linus Nwankwo, M.Sc.;
Univ.-Prof. Dr Elmar Rückert
Start date: 5th September 2022

 

Theoretical difficulty: mid
Practical difficulty: mid

Abstract

When observing animals in nature, navigation and walking seem like medial-side tasks. However, training robots to effectively achieve the same objective is still a challenging problem for roboticists and researchers. We aim to autonomously perform tasks like navigating traffic, avoiding obstacles, finding optimal routes, surveying human hazardous areas, etc with a quadrupedal robot. These tasks are useful in commercial, industrial, and military settings, including self-driving cars, warehouse stacking robots, container transport vehicles in ports, and load-bearing companions for military operations.

For over 20 years today, the SLAM approach has been widely used to achieve autonomous navigation, obstacle avoidance, and path planning objectives. SLAM is a crucial problem in robotics, where a robot navigates through an unknown environment while simultaneously creating a map of it. The SLAM problem is challenging as it requires the robot to estimate its pose (position and orientation) relative to the environment and simultaneously estimate the location of landmarks in the environment.

Some of the most common challenges with SLAM are the accumulation of localization errors over time, inaccurate pose estimation on a map, loop closure, etc. These problems have been partly overcome by using Pose Graphs for localization errors, Extended Kalman filters and Monte Carlos localization for pose estimation.

Quadrupedal robots are well-suited for challenging environments, where the surface conditions are non-uniform, e.g. in off-road environments or in warehouses where stairs or obstacles have to be overcome but have the difficulty of non-uniform dynamic movement which poses additional difficulty for SLAM. 

In the context of this thesis, we propose to study the concept of SLAM with its associated algorithms and apply it to a quadrupedal robot (Unitree Go1). Our goal is to provide the robot with certain tasks and commands that it will then have to autonomously execute. For example, navigate rooms, avoid slow-moving objects, follow an object (person), etc.

 

Tentative Work Plan

To achieve our objective, the following concrete tasks will be focused on:

  • Study the concept of SLAM as well as its application in quadrupedal robots.

  • Setup and familiarize with the simulation environment:
    • Build the robot model (URDF) for the simulation (optional if you wish to use the existing one)
    • Setup the ROS framework for the simulation (Gazebo, Rviz)
    • Recommended programming tools: C++, Python, Matlab
    •  
  • Develop a novel SLAM system for a quadrupedal robot to navigate and map challenging real-world environments:
    • 2D/3D mapping in complex indoor/outdoor environments

    • Localization using either Monte Carlo or extended Kalman filter

    • Establish a path-planning algorithm

  • Intermediate presentation:
    • Presenting the results of the literature study
    • Possibility to ask questions about the theoretical background
    • Detailed planning of the next steps
    •  
  • Implementation:

    • Simulate the achieved results in a virtual environment (Gazebo, Rviz, etc.)

    • Real-time testing on Unitree Go1 quadrupedal robot.

  • Evaluate the performance in various challenging real-world environments, including outdoor terrains, urban environments, and indoor environments with complex structures.
  • B.Sc. thesis writing.
  • Research paper writing (optional)

Related Work

[1]  Wolfram Burgard, Cyrill Stachniss, Kai Arras, and Maren Bennewitz , ‘SLAM: Simultaneous
Localization and Mapping’,  http://ais.informatik.uni-freiburg.de/teaching/ss12/robotics/slides/12-slam.pdf

[2]  V.Barrile, G. Candela, A. Fotia, ‘Point cloud segmentation using image processing techniques for structural analysis’, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W11, 2019 

[3]  Łukasz Sobczak , Katarzyna Filus , Adam Domanski and Joanna Domanska, ‘LiDAR Point Cloud Generation for SLAM Algorithm Evaluation’, Sensors 2021, 21, 3313. https://doi.org/10.3390/ s21103313.

Find more about the thesis at this address:

https://cloud.cps.unileoben.ac.at/index.php/s/RybJssqpq68KDYF