Development of a Predictive Model for Bolted Joints: An Analysis of Tightening and Untightening Processes

  • Forschungsthema:Modelling, Simulation, Data Science, AI, Machine Learning
  • Typ:Bachelor- / Masterarbeit
  • Betreuung:

    Nehal Afifi, M. Sc.

  • Bearbeitung:offen

Description of the Thesis


The goal of this thesis is to develop a predictive model for bolted joints, focusing on the effects of tightening and untightening processes. This model aims to improve our understanding of bolted joint behavior, thereby enhancing their design and performance.


The research will involve collecting data from an existing test bench (Screw Tribology Test-bench) that uses both normal and impact tightening and untightening processes. Key variables such as friction, tightening technique, contact area, thread types, control methods in tightening, tightening tools, and bolt preload will be investigated. These variables will be used to understand and predict the behavior of bolted joints under various conditions. Thus, predicting the functionally relevant variables, namely the load-bearing capacity of the bolted joint and the thread friction coefficient during tightening and untightening.

Expected Result:

The successful development of a predictive model for bolted joints based on the collected data. This model should provide insights into the effects of different tightening and untightening processes on the performance of bolted joints. It is expected that this research will contribute to the field of mechanical engineering by improving the reliability and efficiency of bolted joints.


  • You are studying mechanical engineering, mechatronics or a similar course of study.
  • You work purposefully and independently.
  • You are interested in the design.

If you are interested, I look forward to hearing from you.