Thesis Worker - Data-driven prediction of internal weld quality in hairpin laser welding

30 hp - Data-driven prediction of internal weld quality in hairpin laser welding based on external bead geometry and process parameters

Thesis work is an excellent way to get closer to Scania and build relationships for a future career while being a part of our ongoing transition to a sustainable transportation system. At the E-Machine prototype workshop, located in Scania`s Transmission Manufacturing facility, we are developing smart processes to support the future serial production of best-in-class electric machines for premium trucks and heavy-duty vehicles.

A crucial process in E-Machine production is the laser welding of hairpins and busbars. In this area, we see significant potential in analysing process parameters and external weld geometries to identify correlations with internal weld quality parameters. This could help reduce the need for destructive testing while improving process understanding and quality assurance.

Laser welding is a key process in stator production, making robust quality assurance of welded joints essential for the proper function of an E-Machine. While external weld properties can be inspected relatively easily, assessing internal weld quality is more challenging, as destructive testing is often required to examine the cross-section and material structure of the joint.

This project will investigate whether the need for destructive testing can be reduced by using accessible external weld indicators to predict internal weld quality.

This thesis aims to investigate the correlation between external weld geometry and internal weld quality in laser-welded stators, and evaluate whether non-destructive measurements can predict internal defects and connection quality.

The student will review relevant literature and investigate external input factors such as weld bead geometry and visual weld data, including OCT (Optical Coherence Tomography) sensor data, together with selected process parameters that may have a traceable influence on weld quality.

The collected data will then be analysed using a suitable statistical model to predict and evaluate the internal weld quality of the hairpin connection. Quality parameters such as penetration depth, metallurgical fusion, and porosity may be considered, provided that statistically significant correlations can be identified.

Investigating whether external weld geometry and selected process parameters can be used to predict internal weld qualityDeveloping and evaluating a model for predicting selected internal weld quality parameters based on external weld characteristics and process dataReducing the need for expensive and time-consuming destructive testing

A more detailed consideration of additional input factors, such as clamping and fixturing, as well as outputs from previous process steps including hairpin twisting and cuttingRecommendations for process improvements to achieve improved internal weld quality

The thesis worker will have access to a state-of-the-art laser welding machine equipped with an extensive range of built-in features. These include systems for optical detection as well as optical coherence tomography (OCT) for pre- and post-process measurements. In addition, access will be provided to a material laboratory for cross-section analysis and to computer tomography (CT) equipment.

Master's student in industrial engineering, materials engineering, production engineering or a related field.

Number of students: 1

Start date for the thesis work: To be agreed

Estimated time required: One semester / master's thesis period

Marius Mankel

[email protected]

+46720830284

Christian Ness

[email protected]

+46701659667

Your application must include a CV, personal letter and transcript of grades.

A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.