Increasing the use of Ultra High Strength Steels (UHSS) in vehicle bodystructures is important for reducing weight and cutting CO2 emissions. This thesis investigates challenges in resistance welding that can be a barrier to implementing UHSS as a replacement for low strength steels in vehicle structures. Empirical research has been performed to offer new approaches for improved joint strength and to increase knowledge on cracking mechanisms in resistance projection welding and resistance spot welding of UHSS. By optimising the current build-up phase and peak current during the first milliseconds of weld time, it was shown that the strength could be improved by up to two-fold for projection welded joints. An approach to improve the ductility and strength of resistance spotwelds in UHSS using reduced cooling time was unsuccessful. The reduced cooling rate after weld metal solidification did not fully create the desired softened microstructure. The study on the surface cracking mechanism in resistance spot welded dual-phase UHSS showed that cracking is linked to the galvanization method. It is proposed that formation of aluminium oxide layers on the electrode tips increases the surface temperature and thereby increases the probability for liquid metal embrittlement and surface cracking. / <p>QC 20150526</p>

Capacitor discharge welding is an efficient, cost-effective and stable process. It is mostly used for projection welding. Real-time monitoring is desired to ensure quality. Until this point, measured process quantities were evaluated through expert systems. This method takes much time for developing, is strongly restricted to specific welding tasks and needs deep understanding of the process. Another possibility is quality prediction based on process data with machine learning. This method can overcome the downsides of expert systems. But it requires classified welding experiments to achieve a high prediction probability. In industrial manufacturing, it is rarely possible to generate big sets of this type of data. Therefore, semi-supervised learning will be investigated to enable model development on small data sets. Supervised learning is used to develop machine learning models on large amounts of data. These models are used as a comparison to the semi-supervised models. The time signals of the process parameters are evaluated in these investigations. A total of 389 classified weld tests were performed. With semi-supervised learning methods, the amount of training data necessary was reduced to 31 classified data sets.

In an effort to increase the fuel efficiency of cars more widespread use of higher strength steels is seen for their high strength-to-weight ratio. Thesesteels are more limited in their formability and tendency to harden than conventional steels, complicating manufacturing. This thesis summarizes the available research on resistance projection nut welding to higher strength steels and investigates the accuracy of the simulation program SORPAS when simulating projection nut welds to AlSi-coated Boron steel. It was found that the greatest difficulties in welding coated ultra high strength steels were the metallurgical effects of both the high alloying content of the steel and the coatings interacting with the weld when melting. Although SORPAS was an intuitive program to use for resistance welding and had a wide library of materials available, it was not found to be able to predict the resistance characteristics or results of projection nut welds to coated Boron steel without significant changes to default material parameters. The biggest issue was the delaying effect the coating layer had on the peak resistance, something not observed experimentally. Better results are suggested to be possible after experimentally ensuring the properties of the materials used and importing those values into SORPAS. / I ett försök att öka bilars bränsleeffektivitet används i allt större utsträckning stål med högre hållfasthet på grund av deras goda förhållande mellan styrka och vikt. Dessa stål är mer begränsade i sin formbarhet och tendens att härda än konventionella stål, vilket försvårar tillverkning. Denna uppsats sammanfattar den tillgängliga forskningen om motståndssvetsning med projektionsmutter i höghållfasta stål och undersöker noggrannheten hos simuleringsprogrammet SORPAS vid simulering av projektionsmuttersvetsar i AlSi-belagt borstål. Det konstaterades att de största svårigheterna vid svetsning av belagda ultrahöghållfasta stål var de metallurgiska effekterna av både stålets höga legeringsinnehåll och beläggningarnas interaktion med svetsen vid smältning. Trots att SORPAS var ett intuitivt program att använda för motståndssvetsning och hade ett brett bibliotek av tillgängliga material kunde det inte förutsäga motståndsegenskaperna eller resultaten av projektionsmuttersvetsar mot belagt borstål utan betydande förändringar av standardmaterialparametrarna. Det största problemet var den fördröjande effekt som beläggningsskiktet hade på maximala resistansen, något som inte observerades experimentellt. Bättre överenstämmelse föreslås vara möjlig efter att experimentellt säkerställa egenskaperna hos de material som används och importera dessa värden till SORPAS.