Master Thesis: Development of Method for Prediction of Tooth Interior Fatigue Fracture in Gears
Göteborg, SE, 417 15
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Position Description
Gears are an important component of a gearbox. When designing gears, various requirements for strength, service life, power loss, and noise must be balanced. Gears for electric vehicles generally face different requirements from gears in conventional drivetrains, and the design must account for other potential failure modes. For example, gears in electric-vehicle drivetrains are subjected to more frequent changes in load direction because electric machines are often used to convert kinetic energy into electrical energy, thereby breaking the vehicle. When a gear is subjected to frequent changes in torque direction, this must be taken into account in its design.
For some years, Volvo has conducted gear bending-fatigue tests on a test rig that makes it possible to apply many load-reversal cycles to rotating gears in a short time. Under some conditions, failures have initiated at the root, which is the “normal” failure mode. In other cases, tooth failure has initiated beneath the case-hardening layer, a so-called Tooth Interior Fatigue Fracture (TIFF) failure.
Description of thesis work
The proposed thesis concerns developing a method for predicting risk of TIFF fracture in gears subjected to alternating loading with basis in prior performed tests. More specifically, this would entail:
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Literature study on TIFF fracture and similar failure mechanisms (like TFF, see ISO/TS 6336-4 for reference)
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Evaluation and analysis of results from prior performed tests (e.g. using statistical analysis methods)
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Development of method for evaluating risk of TIFF, taking into account multiaxial stress state in the subsurface material where TIFF fractures normally happen
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Validation of prior performed test data against developed method (and possibly a representative FE-model)
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(If time allows) Performance of further bending fatigue tests to expand sample space and validation of developed method against new test data
Qualifications & Key Attributes:
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Master student in Engineering Physics, Mathematics, Applied Mechanics or equivalent
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Positive attitude, willing to learn, disciplined, good communication skills, humble, result oriented
Information
Thesis Level: Master
Language: English
Starting date: 2027-01-18
Number of students: 2
Tutor : Lennart Johansson, Senior Expert Gear Specialist, Powertrain Technology, Trucks Technology & Industrial Division
Last application: 8:th of November.
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