Friction Characteristics in Light Weight Design Focusing Bolted Joints

Author(s):  
H. Kopfer ◽  
C. Friedrich ◽  
M. De Agostinis ◽  
D. Croccolo

Today more and more light weight structures are used for moved components in automotive, aviation and general industry. Often assemblies of these structures are realized by friction-based connections. Therefore precise information about the friction coefficients are necessary, because in light weight design no simple approximation of the loading capacity with significant over-dimensioning is possible. Furthermore, the short-time tribology of mechanical contacts plays an important role during tightening of bolted joints, especially when using light metals and coated surfaces. Still today, the tightening method of torque controlled screw assembly is the most used. For this tightening method the friction coefficients have to be well-known for an efficient design of the bolted joint. Up to now analytical calculations do not consider any local deviation of friction behavior in component systems, only average values are taken. This is the reason why in modern engineering processes extended friction laws are necessary. A suitable formulation should take contact pressure and sliding velocity into account. Based on this, the contribution shows experimental examples for main uncertainties of frictional behavior during tightening with different material combinations (results from assembly test stand).

2019 ◽  
Vol 61 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Ali Rıza Yıldız ◽  
Ulaş Aytaç Kılıçarpa ◽  
Emre Demirci ◽  
Mesut Doğan

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 537
Author(s):  
Hongxiang Gu ◽  
Miodrag Potkonjak

Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.


2011 ◽  
Vol 94 (1) ◽  
pp. 246-252 ◽  
Author(s):  
G. Catalanotti ◽  
P.P. Camanho ◽  
P. Ghys ◽  
A.T. Marques

2021 ◽  
Author(s):  
Stefano Fini ◽  
Massimiliano De Agostinis ◽  
Dario Croccolo ◽  
Giorgio Olmi ◽  
Francesco Robusto ◽  
...  

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