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Logo Institut für Werkzeugmaschinen und Fertigungstechnik der TU Braunschweig
Robar Arafat, M.Sc.
  • Team
    • Prof. Dr.-Ing. Christoph Herrmann
    • Dr.-Ing. Mark Mennenga
    • Steffen Blömeke
    • Gabriela Ventura Silva
    • Anne-Marie Schlake
    • Siavash Aghaei
    • Abdur-Rahman Ali
    • Robar Arafat
    • Kilian Dickel
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Robar Arafat, M.Sc.

Robar Arafat, M.Sc.
Contact
+49 531/391-7657
r.arafat(at)tu-braunschweig.de

Head of group "Sustainable manufcaturing and biologicalisation"

Institute of Machine Tools and Production Technology
Langer Kamp 19b
38106 Braunschweig
Germany

Office: Old Building, 3. OG, Room 313 A

Fields of research

  • Sustainable cutting fluids and cutting fluid applications
  • Eco-efficient machine tools
  • Energy efficiency in production

Current research projects

  • Development and use of biostatics from renewable raw materials to stabilise water-based fluids such as cutting fluids (Biostatic)
  • Principles of action of nanoparticles with defined interfaces in cutting fluids (Nano-KSS)

 

Teaching

  • Supervision of  "Environmental and Sustainability Management in Industrial Application"
  • Supervision of the laboratory "Data Mining in Production"
  • Coordination of Protohaus-Courses for students of mechanical engineering department
  • Supervision of  student theses

Student theses

KI-basierte Analyse und Klassifikation von Verschleißbildern in der Metallbearbeitung | AI-based Analysis and Classification of Wear Images in Metal Processing

Ausschreibung Masterarbeit (6 Monate)
Titelvorschlag:

KI-basierte Analyse und Klassifikation von Verschleißbildern in der Metallbearbeitung

Hintergrund:

In der metallverarbeitenden Industrie ist die frühzeitige Erkennung und Bewertung von Werkzeugverschleiß entscheidend für die Qualitätssicherung und Prozessoptimierung. Bisher erfolgt die Verschleißanalyse oft manuell, was zeitaufwendig und subjektiv ist. Der Einsatz von Künstlicher Intelligenz (KI) eröffnet hier neue Möglichkeiten zur automatisierten, objektiven Auswertung von Verschleißbildern.

Ziel der Arbeit:
Ziel dieser Masterarbeit ist die Entwicklung eines KI-gestützten Verfahrens zur automatisierten Erkennung und Klassifikation von Verschleißformen anhand von mikroskopischen und makroskopischen Bilddaten aus der Metallbearbeitung.

Mögliche Aufgabeninhalte:

  • Literaturrecherche zu den Themen:
    • Verschleißmechanismen und -formen in der Metallbearbeitung (z. B. Adhäsion, Abrasion, Oxidation, Tribochemie)
    • Gängige Klassifikationskriterien bei Reibung und Verschleiß (z. B. Kolkverschleiß, Kantenverschleiß, Freiflächenverschleiß, Aufbauschneidenbildung)
    • Bildverarbeitung und KI-Methoden im Bereich der Materialcharakterisierung
    • Bestehende öffentlich zugängliche Bilddatenbanken und wissenschaftliche Veröffentlichungen mit geeigneten Verschleißbildern
  • Entwicklung einer geeigneten KI-Architektur (z. B. Convolutional Neural Networks) zur Bildklassifikation
  • Datenerhebung und Datenaufbereitung:
    • Sammlung eigener Bilddaten mittels Licht- oder Rasterelektronenmikroskopie
    • Ergänzung durch Bildmaterial aus wissenschaftlichen Publikationen
  • Training und Validierung des Modells:
    • Training der KI mit Verschleißbildern
    • Evaluation der Aussagekraft
  • Automatisierte Charakterisierung des Verschleißes nach etablierten tribologischen Kriterien
  • Bewertung der Aussagegüte und Übertragbarkeit des entwickelten KI-Modells auf neue Bilddaten
  • Zusammenfassung der Ergebnisse und Ausblick auf mögliche Weiterentwicklungen (z. B. Integration in Inline-Prüfsysteme oder Erweiterung auf andere Werkstoffe und Bearbeitungsverfahren)

Voraussetzungen:
Interesse an KI, Werkstofftechnik und Bildverarbeitung.

Kontakt: Robar Arafat (IWF): r.arafat@tu-braunschweig.de


_________________________________________________________________________


Master’s Thesis Announcement (6 months)
Proposed Title:

AI-based Analysis and Classification of Wear Images in Metal Processing

Background:
In the metal processing industry, the early detection and assessment of tool wear is crucial for quality assurance and process optimization. So far, wear analysis is often carried out manually, which is time-consuming and subjective. The use of Artificial Intelligence (AI) opens up new possibilities for the automated, objective evaluation of wear images.

Objective of the Thesis:
The objective of this master’s thesis is to develop an AI-based approach for the automated detection and classification of wear patterns using microscopic and macroscopic image data from metal processing.

Possible Tasks:

  • Literature review on the following topics:
    • Wear mechanisms and types in metal processing (e.g., adhesion, abrasion, oxidation, tribochemistry)
    • Common classification criteria in friction and wear (e.g., crater wear, edge wear, flank wear, built-up edge formation)
    • Image processing and AI methods in material characterization
    • Existing publicly available image databases and scientific publications with suitable wear images
  • Development of a suitable AI architecture (e.g., Convolutional Neural Networks) for image classification
  • Data collection and preprocessing:
    • Acquisition of own image data using light or scanning electron microscopy
    • Supplementation with image material from scientific publications
  • Training and validation of the model:
    • Training the AI with wear images
    • Evaluation of performance and reliability
  • Automated characterization of wear according to established tribological criteria
  • Assessment of the reliability and transferability of the developed AI model to new image data
  • Summary of results and outlook on potential future developments (e.g., integration into inline inspection systems or extension to other materials and machining processes)

Requirements:
Interest in AI, materials science, and image processing.

Contact: Robar Arafat (IWF): r.arafat@tu-braunschweig.de


Type:
  • Masterarbeit

Subjects: Maschinenbau, Wirtschaftsingenieurwesen, Informatik, Computational Sciences in Engineering, etc.
Start of thesis: sofort
Last change: 5/21/2025

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