Kinematic draping simulation optimization of a composite B-pillar geometry using particle swarm optimization

Fitas, Ricardo; Hesseler, Stefan; Wist, Santino; Greb, Christoph

doi:10.1016/j.heliyon.2022.e11525

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…Mozafary and Payvandy 17 proposed a loop mesh on the basis of the mass-spring system, in which the distances between mass points are optimized with the imperialistic competitive algorithm (ICA). Fitas et al 18 enhanced the system with particle swarm optimization (PSO), and presented a kinetic model based on the particle constraint.…”

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confidence: 99%

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A kinetic grid for fabric draping simulation

Chen

Cao

et al. 2023

Textile Research Journal

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In allusion to the hot issue of fabric simulation, this paper proposes a kinetic grid for reproducing the drape profile of fabric in three-dimensional space. In our model, the interaction inside fabric is built up through a constraint factor as well as an attenuation factor, and the contact between the flexible body of the fabric and the rigid plane is emulated with a touch-counteract mechanism. The forces on the particles in the grid, which are calculated with grid deformation, are then used to generate the additional displacement of the grid. Evolution of the grid is led by step-by-step iteration so that the draping kinetics of the virtual fabric can be achieved. When the draping process meets its steady state, the drape coefficient is worked out through identifying and quantifying the projection area of the grid. The unevenness of the ripples along the drape surface is characterized and manifested by emulating the mechanical anisotropy of the fabric through differentiated constraint factors. The evolution algorithm is improved and an optimized version that reduces calculation error using the third-order Taylor theorem is presented, by which a smoother curved surface for the drape can be produced. The convergence of the grid under different fineness values is explored to reveal the capacity of the model, and a reasonable grid scale has been identified. The computed drape is then compared to a real drape featuring different strengths in the warp and weft, and adjusted to meet the target drape coefficient, as well as drape unevenness considering the anisotropy index. The result shows they are properly matched. The virtual drape is also tested in additional scenes including square support with sharp corners, one-way overhanging and mixed. It has turned out to be a simple, rapid and precise model for fabric drape simulation and assessment.

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confidence: 99%

“…Fitas et al. 18 enhanced the system with particle swarm optimization (PSO), and presented a kinetic model based on the particle constraint.…”

mentioning

confidence: 99%

A kinetic grid for fabric draping simulation

Chen

Cao

et al. 2023

Textile Research Journal

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“…where PSO is a meta-algorithm that was inspired by social behaviors, including bird flocking, fish schooling, and, particularly, swarming theory [40]. The new velocity and the new position of each particle are determined by ( 8) and ( 9) [41].…”

Section: Gray Wolf Optimizer (Gwo)mentioning

confidence: 99%

Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings

Tao,

Chen,

Xiao

et al. 2024

Applied Sciences

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Ground vibrations induced by underground mining blasting has a significant impact on the stability and safety of surface buildings near mines. Due to the thick rock layers overlying underground mines, there is presently limited accuracy in regard to predicting ground vibrations induced by underground mine blasting. Therefore, this study aims to improve the accuracy of predicting ground vibrations induced by underground blasting by comprehensively measuring the peak particle velocity (PPV) in all three directions and independently considering on the impact of vertical distance. Random forest regression (RFR), bagging regression (BR), and gradient boosting regression (GBR) were used to regress the X-axis PPV (X-PPV), Y-axis PPV (Y-PPV), and Z-axis PPV (Z-PPV) based on blasting records measured at an iron mine. In addition, a genetic algorithm, gray wolf optimizer (GWO), and a particle swarm optimization were used to optimize the parameters of the RFR, BR, and GBR. The comparison results show that GWO-GBR is the optimal model for the prediction of the X-PPV (R2 = 0.8072), Y-PPV (R2 = 0.9147), and Z-PPV (R2 = 0.9265), respectively. Thus, the GWO-GBR model proposed in this study is considered a highly reliable model for predicting ground vibrations induced by underground mine blasting to ensure the safety of the mines’ surroundings.

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“…They improve the overall optimization process computational power even more. These models can also handle variables restricted to integer or discrete values [24], which may prove difficult for some local algorithms but can be solved by most global algorithms.…”

Section: Background Of Concepts 21 Optimization-definition and Strate...mentioning

confidence: 99%

A Review of Optimization for Corrugated Boards

Fitas,

Schaffrath,

Schabel

2023

Sustainability

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This paper presents a comprehensive review of optimization practices in the corrugated board industry, which has recently experienced significant interest in using optimization methodologies driven by sustainable demands and increasing computational capabilities. The authors cover different review perspectives, including historical context, manufacturing applications, design optimization, and numerical optimization algorithms used. The main findings of this study indicate that the corrugated board industry has experienced a shift from trial-and-error and expert-driven approaches to data-centric strategies, particularly since the beginning of the 21st century. Interestingly, the industry has also adopted Multi-Disciplinary Optimization techniques from other fields, which demonstrates the importance of knowledge convergence across sectors. However, due to the complex nature of corrugated boards—including materials, design, and manufacturing processes—there is still much research to be done in this area. This work provides guidance for future research directions and encourages innovation and improvement in corrugated board optimization practices. In particular, the strong developments of material models for paper in recent years will boost the use of optimization tools in this field.

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Kinematic draping simulation optimization of a composite B-pillar geometry using particle swarm optimization

Cited by 5 publications

References 32 publications

A kinetic grid for fabric draping simulation

A kinetic grid for fabric draping simulation

Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings

A Review of Optimization for Corrugated Boards

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