The quality of Remote Laser Welding (RLW) weldments is generally assessed by measuring multiple features classified as: (1) surface features (surface spatter, blowout, melt pool width, upper and bottom concavity, seam discontinuity); and, (2) sub-surface features (penetration depth, weld connection, porosity, crack). State-of-the art approaches for in-process monitoring involve the fusion of multiple sensors so multiple weld features can be detected. For example, in-process monitoring of surface features is a well-established area and comprises of CMOS/CCD camera-based or laser-based detectors – those detectors allow direct measurement of the surface features. Direct measurement of sub-surface features is yet outside the reach of current sensor technologies. Best-in-class approaches make use of indirect signals (for example, gathered via photodiodes, acoustic detectors and spectrometers), which are then correlated to the weld features via statistical and machine learning techniques. Those approaches have been proved successful only to monitor weld penetration and interface of weld connection. In-process monitoring of weld cracks, both at millimetre-scale or micron-scale levels, remains an un-solved area of investigation, and currently they are only detectable by destructive techniques or observation of cross-sections after metallographic preparation.
This case studentship is aimed to model and simulate the formation and propagation of weld cracks in Remote Laser Welding (RLW) of 6xxx high strength aluminium extrusions and to integrate those models with readily available sensors in order to enable in-process monitoring of RLW weldments. The work will support Constellium’s manufacturing concepts underpinning the development and deployment of innovative joining and assembly technologies for production of lightweight extrusion-intensive structures such as battery enclosures for electrified vehicles. A student working on this project will (1) review existing models for modelling and prediction of mechanisms of weld cracking; (2) develop novel physics-driven models for prediction of weld cracks; (3) propose strategy for integrating physics-driven simulation and sensor data streams. The modelling work will be verified and validated with relevant materials - alloys and core applications will be selected based on Constellium’s inputs.