RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8

Surface defect detection in chips is crucial for ensuring product quality and reliability. This paper addresses the challenge of low identification accuracy in chip surface defect detection, which arises from the similarity of defect characteristics, small sizes, and significant scale differences. We propose an enhanced chip surface defect detection algorithm based on an improved version of YOLOv8, termed RST-YOLOv8. This study introduces the C2f_RVB module, which incorporates RepViTBlock technology. This integration effectively optimizes feature representation capabilities while significantly reducing the model’s parameter count. By enhancing the expressive power of deep features, we achieve a marked improvement in the identification accuracy of small defect targets. Additionally, we employ the SimAM attention mechanism, enabling the model to learn three-dimensional channel information, thereby strengthening its perception of defect characteristics. To address the issues of missed detections and false detections of small targets in chip surface defect detection, we designed a task-aligned dynamic detection head (TADDH) to facilitate interaction between the localization and classification detection heads. This design improves the accuracy of small target detection. Experimental evaluations on the PCB_DATASET indicate that our model improved the [email protected] by 10.3%. Furthermore, significant progress was achieved in experiments on the chip surface defect dataset, where [email protected] increased by 5.4%. Simultaneously, the model demonstrated significant advantages in terms of computational complexity, as both the number of parameters and GFLOPs were effectively controlled. This showcases the model’s balance between high precision and a lightweight design. The experimental results show that the RST-YOLOv8 model has a significant advantage in detection accuracy for chip surface defects compared to other models. It not only enhances detection accuracy but also achieves an optimal balance between computational resource consumption and real-time performance, providing an ideal technical pathway for chip surface defect detection tasks

Stress-based topology optimization of concrete structures with prestressing reinforcements

Following the extended two-material density penalization scheme, a stress-based topology optimization method for the layout design of prestressed concrete structures is proposed. The Drucker-Prager yield criterion is used to predict the asymmetrical strength failure of concrete. The prestress is considered by making a reasonable assumption on the prestressing orientation in each element and adding an additional load vector to the structural equilibrium function. The proposed optimization model is thus formulated as to minimize the reinforcement material volume under Drucker-Prager yield constraints on elemental concrete local stresses. In order to give a reasonable definition of concrete local stress and prevent the stress singularity phenomenon, the local stress interpolation function and the ε -relaxation technique are adopted. The topology optimization problem is solved using the method of moving asymptotes combined with an active set strategy. Numerical examples are given to show the efficiency of the proposed optimization method in the layout design of prestressed concrete structures. © 2013 Taylor & Francis

Reliability based topology optimization for continuum structures with local failure constraints

This paper presents an effective method for stress constrained topology optimization problems under load and material uncertainties. Based on the Performance Measure Approach (PMA), the optimization problem is formulated as to minimize the objective function under a large number of (stress-related) target performance constraints. In order to overcome the stress singularity phenomenon caused by the combined stress and reliability constraints, a reduction strategy on target reliability index is proposed and utilized together with the ε-relaxation approach. Meanwhile, an enhanced aggregation method is employed to aggregate the selected active constraints using a general K-S function, which avoids expensive computational cost from the large-scale nature of local failure constraints. Several numerical examples are given to demonstrate the validity of the present method. © 2014 Elsevier Ltd. All rights reserved

Optimal topology design of steel–concrete composite structures under stiffness and strength constraints

This study presents a three-phase topology optimization model and an effective solution procedure to generate optimal material distributions for complex steel-concrete composite structures. The objective is to minimize the total material cost (or mass) while satisfying the specified structural stiffness requirements and concrete strength constraints. Based on the Drucker-Prager criterion for concrete yield behaviour, the extended power-law interpolation for material properties and a cosine-type relaxation scheme for Drucker-Prager stress constraints are adopted. An enhanced aggregation method is employed to efficiently treat the large number of stress constraints, and the optimal topology is obtained through a standard gradient-based search. Several examples are provided to demonstrate the capability of the proposed optimization method in automatically finding the reasonable composite layout of steel and concrete. © 2012 Elsevier Ltd. All rights reserved

Topology optimization of reinforced concrete structures considering control of shrinkage and strength failure

To take into account the shrinkage effect in the early stage of Reinforced Concrete (RC) design, an effective continuum topology optimization method is presented in this paper. Based on the power-law interpolation, shrinkage of concrete is numerically simulated by introducing an additional design-dependent force. Under multi-axial stress conditions, the concrete failure surface is well fitted by two Drucker-Prager yield functions. The optimization problem aims at minimizing the cost function under yield strength constraints on concrete elements and a structural shrinkage volume constraint. In conjunction with the adjoint-variable sensitivity information, the enhanced aggregation method is utilized to efficiently reduce the computational effort arisen from large-scale strength constraints. Numerical results reveal that the proposed approach can produce a reasonable solution with the least steel reinforcements to ensure the structural safety under the combined action of external loads and shrinkage