robust design
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2022 ◽  
Vol 73 ◽  
pp. 102232
Author(s):  
Patrick Mesmer ◽  
Michael Neubauer ◽  
Armin Lechler ◽  
Alexander Verl

2022 ◽  
Author(s):  
SURESHKUMAR P ◽  
suresh kumar ◽  
T. Jagadeesha ◽  
L. Natrayan ◽  
M. Ravichandran ◽  
...  

Abstract The present research study investigates the Mechanical, Physical, and Tribological properties of powder metallurgy (PM) produced AA6063 alloy reinforced with silicon nitride (Si3N4) and copper nitrate (CuN2O6). Incorporation of Si3N4 & CuN2O6 reinforcement in matrix material ranged from 6 to 12 % Si3N4 in a 6-step interval and 2 to 6 %CuN2O6 in a two-step interval. The characterizations were made on the PM-produced specimens using OM, EDS, XRD, and Hardness. The reinforcement particles were uniformly distributed, which was attributed to a homogeneous mixer of matrix and reinforcements. The test findings show that as the reinforcing percentage of the ceramic and inorganic compound increases, properties such as hardness and density rise considerably and monolithically. The existence of phases such as Si3N4 and CuN2O6 reinforcement in the AA6063 matrix was ensured by X-ray diffraction. The hardness of AA6063/12%Si3N4/6%CuN2O6 increased by 88% over the base alloy due to a mismatch in thermal expansion between the Al matrix and reinforcement, which causes massive internal stress, causing the aluminium matrix to plastically deform to accommodate the reduced volume expansion of Si3N4 and CuN2O6 particles. The dry sliding wear test was determined using the Pin-on-Disc method, and the results show that the composite is more wear-resistant. An orthogonal array and analysis of variance were utilized to evaluate the solution, including parameters using the Taguchi robust design technique. The weight percentage of the Si3N4/CuN2O6 compound and the relationship between weight % of reinforcement and applied load had the most significant impact on composite wear resistance. The produced composite's wear morphology was studied using images from a scanning electron microscope and energy dispersive spectroscopy.


Author(s):  
Anand Balu Nellippallil ◽  
Parker R. Berthelson ◽  
Luke Peterson ◽  
Raj Prabhu

Abstract Government agencies, globally, strive to minimize the likelihood and frequency of human death and severe injury on road transport systems. From an engineering design standpoint, the minimization of these road accident effects on occupants becomes a critical design goal. This necessitates the quantification and management of injury risks on the human body in response to several vehicular impact variables and their associated uncertainties for different crash scenarios. In this paper, we present a decision-based, robust design framework to quantify and manage the impact-based injury risks on occupants for different computational model-based car crash scenarios. The key functionality offered is the designer's capability to conduct robust concept exploration focused on managing the selected impact variables and associated uncertainties, such that injury risks are controlled within acceptable levels. The framework's efficacy is tested for near-side impact scenarios with impact velocity and angle of impact as the critical variables of interest. Two injury criteria, namely, Head Injury Criterion (HIC) and Lateral Neck Injury Criteria (Lateral Nij), are selected to quantitatively measure the head and neck injury risks in each crash simulation. Using the framework, a robust design problem is formulated to determine the combination of impact variables that best satisfice the injury goals defined. The framework and associated design constructs are generic and support the formulation and decision-based robust concept exploration of similar problems involving models under uncertainty. Our focus in this paper is on the framework rather than the results per se.


2022 ◽  
Vol 8 ◽  
Author(s):  
Yinshuang Xiao ◽  
Zhenghui Sha

Abstract Seasonal effects can significantly impact the robustness of socio-technical systems (STS) to demand fluctuations. There is an increasing need to develop novel design approaches that can support capacity planning decisions for enhancing the robustness of STS against seasonal effects. This paper proposes a new network motif-based approach to supporting capacity planning in STS for an improved seasonal robustness. Network motifs are underlying nonrandom subgraphs within a complex network. In this approach, we introduce three motif-based metrics for system performance evaluation and capacity planning decision-making. The first one is the imbalance score of a motif (e.g., a local service network), the second one is the measurement of a motif’s seasonal robustness, and the third one is a capacity planning decision criterion. Based on these three metrics, we validate that the sensitivity of STS performance against seasonal effects is highly correlated with the imbalanced capacity between service nodes in an STS. Correspondingly, we formulate a design optimisation problem to improve the robustness of STS by rebalancing the resources at critical service nodes. To demonstrate the utility of the approach, a case study on Divvy bike-sharing system in Chicago is conducted. With a focus on the size-3 motifs (a subgraph consisting three docked stations), we find that there is a significant correlation between the difference of the number of docks among the stations in a motif and the return/rental performance of such a motif against seasonal changes. Guided by this finding, our design approach can successfully balance out the number of docks between those stations that have caused the most severe seasonal perturbations. The results also imply that the network motifs can be an effective local structural representation in support of STS robust design. Our approach can be generally applied in other STS where the system performances are significantly impacted by seasonal changes, for example, supply chain networks, transportation systems and power grids.


2022 ◽  
Vol 16 (2) ◽  
pp. 1
Author(s):  
SALIH OSMAN Duffuaa ◽  
Ahmed M. Ghaithan ◽  
Ahmed M. Attia

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Yu-Jie Liu ◽  
Qun Sun ◽  
Qiang Han ◽  
Hai-Gang Xu ◽  
Wen-Xiao Han ◽  
...  

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