Högskolan i Skövde

Among the various driving forces involved in the molten pool during keyhole laser welding, the vaporization-induced recoil pressure is the dominant one. This study experimentally measured the process-induced recoil force during laser welding of aluminium and copper. A customized measurement setup was used to measure the specimen displacement caused by the recoil force, which was then determined by means of a finite element (FE) analysis. Furthermore, multi-physics computational fluid dynamics (CFD) models of the laser welding process were developed. After calibration, these models were used to predict the recoil force and its dependence on various process parameters. When only the recoil pressure acting on regions where vaporization occurs was considered, excluding the gaseous phases in the model, the total recoil force was underestimated. To account for that the formed gas contributes to the total recoil force as it rises and exits the keyhole, the total recoil force was calculated based on the predicted net mass flow due to vaporization and condensation. This simplified model showed good agreement between predicted and experimentally measured recoil forces, demonstrating that the observed consistent recoil force with increasing laser power may be due to a corresponding increase in the condensation rate. This highlights the importance of understanding the behaviour of the vaporized gas phase to determine appropriate simplifications and assumptions in laser welding process modelling. The findings of this study support the development and validation of multi-physics process models, further advancing knowledge of relevant modelling approximations.

Low-austenitizing temperature practices resulted in substantial changes in both microstructure and mechanical properties of the fully ferritic as-cast Compacted Graphite Irons (CGI). The austempering processes were accomplished through first austenitizing at 850 °C for 60 min followed by quenching in a salt-bath at 275, 325, and 375 °C for times ranging from 30, 60, 90, and 120 min. In contrast with the austenitizing performed at 900 °C performed on the same material, the microstructure consisted of a notable volume fraction of proeutectoid ferrite, which was not observed under similar austempering temperature and time conditions. Lowering the austenitizing temperature to 850 °C resulted in decreased untransformed austenite. Depending on the austempering conditions, a notable improvement was achieved in both Brinell and Vickers hardness compared to the as-cast CGI. The ausferrite matrix led to remarkable increases in yield strength (YS), ultimate tensile strength (UTS), and a decrease in total elongation to failure. The highest YS and UTS values were achieved for specimens austempered at 275 °C while increasing the austempering temperature decreased both YS and UTS. Furthermore, the results showed that the austempering temperature had a more significant impact on YS and UTS than the austempering time. All austempered CGI specimens exhibited primarily brittle failure attributes, while ferritic CGIs showed a mixed failure mode.

Laser beam welding (LBW) involves complex and rapid interactions between the laser and material, often resulting in defects such as pore formation. Emissions collected during the process offer valuable insight but are difficult to interpret directly for defect detection. In this study, we propose a data-driven framework to interpret electromagnetic emissions in LBW using both supervised and unsupervised learning. Our framework is implemented in the post-process monitoring stage and can be used as a real-time framework. The supervised approach uses labeled data corresponding to predefined defects (in this work, pore formation is an example of a defined defect). Meanwhile, the unsupervised method is used to identify anomalies without using predefined labels. Supervised and unsupervised learning aims to find reference values in the emissions data to determine the values of signals that lead to defects in welding (enabling quantitative monitoring). A total of 81 welding experiments were conducted, recording real-time emission data across 42 spectral channels. From these signals, statistical, temporal, and shape-based features were extracted, and dimensionality was reduced using Principal Component Analysis (PCA). The LSTM model achieved an average mean squared error (MSE) of 0.0029 and mean absolute error (MAE) of 0.0288 on the testing set across five folds. The Isolation Forest achieved 80% accuracy and 85.7% precision in detecting anomalous welds on a subset with validated defect labels. The proposed framework enhances the interpretability of 4D photonic data and enables both post-process analysis and potential real-time monitoring. It provides a scalable, data-driven approach to weld quality assessment for industrial applications.

For battery pack assemblies, it is crucial that the laser welded cell-to-busbar joints demonstrate both high mechanical strength and minimal electrical resistance. The present study investigates the effect of different laser welding parameters, on the mechanical strength, electrical resistance, porosity formation and joint microstructure, for dissimilar material cell-to-busbar joints. Laser welding experiments are performed, on thin nickel-plated copper and steel plates. The plates are joined in an overlap configuration, using laser beam wobbling and power modulation. Both circular and sinusoidal laser beam wobbling are used as selected strategies to increase the interface width of the joints, where also a comparison is made between the two methods. The joint quality is evaluated using joint geometry analysis, shear strength tests, computed tomography scanning and electrical resistance measurements. The results show that circular laser beam wobbling gives a larger joint shear strength compared with sinusoidal laser beam wobbling. In addition, it is observed that both the total pore volume and material mixing are significantly increased with increasing laser power and wobbling frequency for circular laser beam wobbling. However, for the sinusoidal laser beam wobbling the wobbling frequency does not show a significant impact on the total pore volume.

In the battery pack assembly, it is essential to ensure that the cell-to-busbar joints can be produced with high quality and with minimal impact on the individual battery cells. This study examines the influence of process parameters on the joint quality for nickel-plated copper and steel plates, laser welded in an overlap configuration. Artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, are used to predict and evaluate the joint quality. A set of optimized process parameters is identified, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and in-process temperatures. In an meta model-based multi-objective optimization approach, the non-dominated sorting genetic algorithm II (NSGA-II) is used to efficiently search for trade-off solutions and the meta models are used for objective approximation. As a result, the objective evaluation time is decreased from around 9 h, when evaluated directly from numerical simulations, to only tenths of a second. From the Pareto-optimal front of trade-off solutions, three optimal solutions are selected for validation. The selected solutions are validated through laser welding experiments and numerical simulations, resulting in joints with large interface widths and low in-process temperatures without a full penetration.

To advance quality assurance in the welding process, this study presents a deep learning (DL) model that enables the prediction of two critical welds’ key performance characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a wide range of laser welding key input characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were obtained from hairpin welding experiments. Two DL networks are employed with multiple hidden dense layers and linear activation functions to investigate the capabilities of deep neural networks in capturing the complex nonlinear relationships between the welding input and output variables (KPCs and KICs). Applying DL networks to the small numerical experimental hairpin welding dataset has shown promising results, achieving mean absolute error values of 0.1079 for predicting welding depth and 0.0641 for average pore volume. This, in turn, promises significant advantages in controlling welding outcomes, moving beyond the current trend of relying only on defect classification in weld monitoring to capture the correlation between the weld parameters and weld geometries.

A quantitative strategy was reported to design and develop Mg-Al-based alloys to achieve high thermal conductivity, in which the specific RE elements can be introduced to reduce the Al concentration in Mg matrix and to suppress the formation of Mg17Al12 phase through the formation of new intermetallic phases. Based on quantitative calculations, the strategy was demonstrated by a novel die-cast Mg3.2Al4.4La0.4Nd (in wt.%) alloy, which provided the thermal conductivity of 114.3 W/(m∙K) at ambient temperature and 137.5 W/(m∙K) at 300 °C, ∼25% higher than the commercial Mg4Al4RE (AE44) alloy. Meanwhile, the alloy also offered excellent ambient yield strength of 143.2 MPa and elongation of 8.2%, and superior strength and ductility than the AE44 alloy at elevated temperatures.

Aluminium is essential in automobile industry together with cast iron. Because of its lightweight property and good mechanical properties, aluminium reinforced with silicon carbide have found application as brake discs. Aluminium reinforced with 15%and 20% silicon carbide were high-pressure die-cast (HPDC) and Rheo-HPDC cast in the current paper. Micro-Vickers hardness and Rockwell C hardness showed different trends with the increasing amounts of SiCp-particles. Scratch resistance of the surface on micro-scale was analysed using a micro-scratch test to study the mechanics of the wear process. Reciprocating sliding wear of the composites was considered, using the HPDC cast aluminium with 20% silicon carbide of liquid casting as the sliding surface. The wear showed a combination of abrasive wear and adhesive wear. The metallography of the wear surfaces showed deep abrasive wear grooves. Wear debris from both the surfaces were forming a tribolayer. The formation of this layer decided the friction and wear performance as a result of the abrasive and adhesive wear mechanisms seen both in the micromechanics of the scratch test and in the friction behaviour.

Significant research has been done to improve the wear properties of the components used in internal combustion engines. Excessive wear is observed in components such as cylinder liners and rings, which can lead to lower volumetric efficiency of the engine, increase oil consumption, polluting emissions, and scuffing related issues. Since tribological systems in internal combustion engines are complex, the different wear mechanisms involved need to be investigated to improve the life of components. Cast irons are commonly used for engine components, especially compacted graphite irons (CGI) for piston rings and gray cast irons (GCI) for cylinder liners. This work aims to evaluate the tribological behavior of two different microstructures of CGI (pearlitic and ausferritic), sliding on pearlitic GCI. The samples of CGI with different microstructures and hardness were evaluated in a short-stroke reciprocating sliding tester, using Petronas Urania SAE 30 API CF lubricant oil at 100 degrees C for four hours. The characterization of worn surfaces was made using a scanning electron microscope (SEM) and 3D roughness measurements. The coefficient of friction (COF) comparison between the two CGI microstructures showed very similar results with COF =0.11. The pearlitic CGI showed more severe wear than the austempered one, confirmed by SEM images and the difference in topography parameters before and after the tests. Phosphorus, sulfur, and zinc were detected by EDS analyses in the samples' worn-out regions, indicating the formation of tribo-films, which was further confirmed by the friction tests.

This study investigates the effect of austempering temperature and time on the microstructural and mechanical properties of unalloyed Compacted Graphite Iron (CGI) with an initially ferritic matrix structure. The as-cast CGI samples were first austenitised at 900 °C for 60 min in a furnace, then austempered in a closed salt bath at three austempering temperatures – 275, 325, and 375 °C – for different times; 30, 60, 90, and 120 min. Tensile properties, Brinell, Vickers and Rockwell C hardness values were evaluated for the as-cast and austempered CGI ones. LOM and SEM, EBSD analysis techniques were used for microstructure and phase analysis. A mixture of acicular ferrite and retained austenite was achieved in the austempered CGI samples. In general, a decrease in austempering temperature resulted in a decrease in retained austenite content, corresponding improvements in hardness and tensile strength, and a decrease in elongation values.