Loading classification proposal for fatigue design of automotive chassis-parts: A relevant process for variable amplitude and multi-input load cases

Bellec, Enora; Doudard, Cédric; Facchinetti, Matteo Luca; Calloch, Sylvain; Moyne, Sylvain

doi:10.1016/j.ijfatigue.2022.107284

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…The fatigue strength was evaluated in terms of fatigue strength (σ f ) at 2•10 6 cycles, which is defined as the fatigue limit for the high cycle fatigue (HCF) regime [35] and is commonly used in the automotive industry [36]. It was determined through the conventional testing method following the ISO 1099 recommendations.…”

Section: Fatigue Testsmentioning

confidence: 99%

Understanding the Fatigue Notch Sensitivity of High-Strength Steels through Fracture Toughness

Parareda

Frómeta

Casellas

et al. 2023

Metals

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This study presents an innovative approach for selecting high-strength materials for fatigue dimensioning parts, considering both fracture toughness and fatigue performance. Warm and hot forming processes enable the construction of high-strength parts above 1000 MPa with complex geometries, making them suitable for lightweight chassis in automotive and freight applications. This research reveals that high-strength steels can experience up to a 40% reduction in fatigue performance due to manufacturing defects introduced during punching and trimming. Fracture toughness has been proposed as a good indicator of notch sensitivity, with a strong correlation of 0.83 between fracture toughness and fatigue notch sensitivity. Therefore, by combining fracture toughness measurements and fatigue resistance obtained through the rapid fatigue test, it becomes possible to quickly identify the most fatigue-resistant materials to deal with defects. Among the nine materials analysed, warm-formed steels show promising characteristics for lightweight chassis construction, with high fatigue resistance and fracture toughness exceeding the proposed fracture threshold of 250 kJ/m2.

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Section: Fatigue Testsmentioning

confidence: 99%

Understanding the Fatigue Notch Sensitivity of High-Strength Steels through Fracture Toughness

Parareda

Frómeta

Casellas

et al. 2023

Metals

View full text Add to dashboard Cite

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Load balancing of multi-AGV road network based on improved Q-learning algorithm and macroscopic fundamental diagram

Zhang,

Li,

et al. 2024

Complex Intell. Syst.

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To address the challenges of traffic congestion and suboptimal operational efficiency in the context of large-scale applications like production plants and warehouses that utilize multiple automatic guided vehicles (multi-AGVs), this article proposed using an Improved Q-learning (IQL) algorithm and Macroscopic Fundamental Diagram (MFD) for the purposes of load balancing and congestion discrimination on road networks. Traditional Q-learning converges slowly, which is why we have proposed the use of an updated Q value of the previous iteration step as the maximum Q value of the next state to reduce the number of Q value comparisons and improve the algorithm’s convergence speed. When calculating the cost of AGV operation, the traditional Q-learning algorithm only considers the evaluation function of a single distance and introduces an improved reward and punishment mechanism to combine the operating distance of AGV and the road network load, which finally equalizes the road network load. MFD is the basic property of road networks and is based on MFD, which is combined with the Markov Chain (MC) model. Road network traffic congestion state discrimination method was proposed to classify the congestion state according to the detected number of vehicles on the road network. The MC model accurately discriminated the range near the critical point. Finally, the scale of the road network and the load factor were changed for several simulations. The findings indicated that the improved algorithm showed a notable ability to achieve equilibrium in the load distribution of the road network. This led to a substantial enhancement in AGV operational efficiency.

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Time-domain extrapolation of vehicle durability load spectrum based on load decomposition and POT model threshold preference

Yang,

Zhang,

Zhang

et al. 2025

Mechanical Systems and Signal Processing

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Loading classification proposal for fatigue design of automotive chassis-parts: A relevant process for variable amplitude and multi-input load cases

Cited by 3 publications

References 33 publications

Understanding the Fatigue Notch Sensitivity of High-Strength Steels through Fracture Toughness

Understanding the Fatigue Notch Sensitivity of High-Strength Steels through Fracture Toughness

Load balancing of multi-AGV road network based on improved Q-learning algorithm and macroscopic fundamental diagram

Time-domain extrapolation of vehicle durability load spectrum based on load decomposition and POT model threshold preference

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