Mass Optimization of Gears

The paper deals with the mass optimization of gear pairs. The proposed material science based selection strategy uses an extended version of Ashby model, where the minimum value of mass as function of material parameters and density can be calculated. Comparative analyses have proved that applying the method proposed, various gear materials can be classified and ranked. It could be concluded that case or induction hardened steels and titanium alloys with appropriate surface treatment are the best solutions for high quality gear materials. The presented relationships for material parameters and geometry of gears help empirical or heuristic selection of proper materials

Ruptured Aortic Aneurysm and Dissection Related Death: an Autopsy Database Analysis

Acute aortic catastrophes (AAC), mainly ruptured aneurysms and dissections, lead all other vascular conditions in morbidity and mortality, even if intervention occurs. The aim of our study was to give a descriptive overview of the demographic and pathological characteristics of AAC. Between 1994 and 2013, 80,469 autopsies were performed at Semmelweis University hospitals in Budapest. After collecting the autopsy reports we were able to create the AAC database upon which we conducted our analysis. We found 567 cases of AAC. The cause of death in 120 of them was classified as a non-ruptured aorta with malperfusion or distal embolization. Of the remaining 447 cases, in 305 the cause of death was a ruptured aortic aneurysm (rAA), and in 142 it was a ruptured aortic dissection (rAD). The distribution of rAA cases was 34.4% thoracal, 4.3% thoracoabdominal, and 61.3% abdominal. We found female dominance where the rAA was thoracal. In rAD cases, 84% were Stanford A and 16% Stanford B type. In both groups we found different pathological distributions. In the prehospital group, the number of thoracal ruptures was considerable. 88% of the patients with Stanford A dissection died in the prehospital or perioperative period. The most progressive AACs were ruptures of intrapericardial aneurysms and Stanford A dissections., however survival rate can be elevated by using rapid imaging examination and immediate surgical intervention. We want to highlight that our study contains such gender differences, which are worth to be taken into consideration

Comparative study of the application of steels and aluminium in lightweight production of automotive parts

The automotive industry has a decisive role in the economy of developed countries. Sheet metal forming – as one of the main processes in car manufacturing – has similarly important role. Concerning the recent trends in car production, the application of lightweight constructions principles is the main priority to meet customers' demands and the increased legal requirements. To meet these expectations, the application of high strength materials is regarded as one of the most promising possibilities. Applying high strength materials – beside high strength steels like DP1000, TRIP780 recently aluminium alloys, e.g. AA7021 or AA7075 – have a positive response for many of the requirements: increasing strength results in the application of thinner sheets together with significant mass reduction, leading to lower consumption with increased environment protection; however it can often lead to the decrease of formability, too. In this paper, an introduction on recent material developments in the automotive industry concerning both the use of new generation high strength steels and light metals with particular emphasis on aluminium alloys will be given. A comparison between steel and aluminium application in the automotive industry is given from various points of view. Since our main topic is the sheet metal forming, in this paper the body-in-white manufacturing in the automotive industry is the primary focus field. Keywords: High strength steels, Aluminium, Automotive industry, Low cost manufacturin

Effect of Roller Levelling on Tensile Properties of Aluminum Sheets

The straightening of sheets, bars and profiles plays an important role in many machining processes. The aim of sheet straightening in the rolling mill is to ensure that the deviation of sheets from flatness is within the tolerances specified in the standards or delivery conditions. There is a wide range of information available on the roller levelling process used to meet these quality requirements. However, little attention has been paid to the effects of levelling, namely the change in properties of the sheets before and after roller levelling. The aim of the present publication is to investigate how the levelling process affects tensile test results. The experiments have shown that levelling increases the yield strength of the sheet by 14–18%, while it decreases its elongation by 1–3% and hardening exponent by 15%. The mechanical model developed allows changes to be predicted, so that a plan can be made regarding roller levelling technology that has the least effect on the properties of the sheet while maintaining the desired dimensional accuracy

Application of artificial neural networks for characterisation of formability properties of sheet metals

Artificial neural network models were developed to estimate forming limit diagrams from tensile test results based on our own experiments and data from the literature for steel and aluminium sheet metals. Experimental data were obtained from tensile tests and Nakazima tests. The input parameters used in the models were yield strength, ultimate tensile strength, uniform elongation, elongation at fracture, anisotropy coefficient and hardening exponent or combinations of these. The forming limit curves were defined by the measured minor and major strains using seven standard test specimens. After training the artificial neural network, the difference between measured and predicted results was evaluated by linear regression parameters and by the absolute errors. For steel sheet data taken from the literature, the estimated outputs of ANN models were compared with the results of empirical formulae developed by different authors. It was found that there was a high correlation coefficient between predicted and measured values for models using neural networks, which gave better approximations than other linear and non-linear models