Light-Weight Design of Automotive Track Rod Based on Design of Experiment

2021 ◽  
Vol 45 (4) ◽  
pp. 339-344
Author(s):  
Kee Joo Kim
Keyword(s):  
Design Of Experiment ◽  
Light Weight ◽  
Weight Design

Light-weight design of automotive suspension link based on design of experiment

2015 ◽  
Vol 16 (1) ◽  
pp. 67-71 ◽  
Author(s):  
J. H. Park ◽  
K. J. Kim ◽  
J. W. Lee ◽  
J. K. Yoon
Keyword(s):  
Design Of Experiment ◽  
Light Weight ◽  
Weight Design ◽  
Automotive Suspension

Topography and topology optimization of diesel engine components for light-weight design in the automotive industry

2019 ◽  
Vol 61 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Ali Rıza Yıldız ◽  
Ulaş Aytaç Kılıçarpa ◽  
Emre Demirci ◽  
Mesut Doğan
Keyword(s):  
Topology Optimization ◽  
Diesel Engine ◽  
Automotive Industry ◽  
Light Weight ◽  
And Topology ◽  
Weight Design ◽  
Engine Components

scholarly journals Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 537
Author(s):  
Hongxiang Gu ◽  
Miodrag Potkonjak
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Evolution Strategies ◽  
Light Weight ◽  
Network Configuration ◽  
System Complexity ◽  
Physical Unclonable Functions ◽  
Output Stability ◽  
Output Mapping ◽  
Weight Design

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.

Recent Achievements and Future Challenges for R&D on Aluminium Alloys and Processing

2013 ◽  
Vol 765 ◽  
pp. 47-53
Author(s):  
Helmut Kaufmann
Keyword(s):  
Aluminium Alloys ◽  
Mechanical Treatment ◽  
Materials Science ◽  
New Products ◽  
High Strength ◽  
Light Weight ◽  
Process Chain ◽  
Future Challenges ◽  
Weight Design ◽  
Close Cooperation

The focus of this paper is set on technical achievements and challenges - however, these are most often closely linked to economical or ecological targets set by customers or society. Ideally, an alloy or process optimization leads to improved properties, reduced cost, and reduced emissions. With a continuously growing understanding of the underlying materials science, supported by novel computer simulation, improved alloys and processing routes have been developed. Many of the recent improvements were related to the thermal-mechanical treatment of high strength alloys for enhanced light weight design. Currently and in the future, the focus will be on sustainable development along the entire process chain, with special attention to the recycling of used products and high recycled content in new products. The optimized utilization of resources (e.g. materials, energy, etc.) will require the close cooperation of materials suppliers, product designers and manufacturers as well as R&D facilities to reconsider given material specifications and processing routes.

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