Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly

Ieamsaard, Jirarat; Muneesawang, Paisarn; Sandnes, Frode Eika

doi:10.1007/s11668-018-0426-4

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Initially, the captured images undergo preprocessing, which typically includes normalization [14], filtering [16], and enhancement [17,18]. Following this, localization and segmentation tasks are performed, using techniques like template matching [17,19], thresholding [8,20], or segmentation based on edges [21,22]. Algorithms then apply rules based on human experience to define target features by shape, area, grayscale, and texture.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Network for Aviation Plug Defect Detection

Ji,

Huang

2024

Aerospace

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Ensuring the integrity of aviation plug components is crucial for maintaining the safety and functionality of the aerospace industry. Traditional methods for detecting surface defects often show low detection probabilities, highlighting the need for more advanced automated detection systems. This paper enhances the YOLOv5 model by integrating the Generalized Efficient Layer Aggregation Network (GELAN), which optimizes feature aggregation and boosts model robustness, replacing the conventional Convolutional Block Attention Module (CBAM). The upgraded YOLOv5 architecture, incorporating GELAN, effectively aggregates multi-scale and multi-layer features, thus preserving essential information across the network’s depth. This capability is vital for maintaining high-fidelity feature representations, critical for detecting minute and complex defects. Additionally, the Focal EIOU loss function effectively tackles class imbalance and concentrates the model’s attention on difficult detection areas, thus significantly improving its sensitivity and overall accuracy in identifying defects. Replacing the traditional coupled head with a lightweight decoupled head improves the separation of localization and classification tasks, enhancing both accuracy and convergence speed. The lightweight decoupled head also reduces computational load without compromising detection efficiency. Experimental results demonstrate that the enhanced YOLOv5 architecture significantly improves detection probability, achieving a detection rate of 78.5%. This improvement occurs with only a minor increase in inference time per image, underscoring the efficiency of the proposed model. The optimized YOLOv5 model with GELAN proves highly effective, offering significant benefits for the precision and reliability required in aviation component inspections.

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Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 Network for Aviation Plug Defect Detection

Ji,

Huang

2024

Aerospace

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“…Several 2D AOI systems have been used with success for the identification of defects in surface mount assemblies. 2–5 As mentioned by Richter and Schambach, 6 these systems rely on diffusive reflection of visible radiation and a high contrast between light and dark regions. This makes them inadequate for the inspection of highly reflective and sloped solder joints like those in through hole technology (THT).…”

Section: Introductionmentioning

confidence: 99%

Infrared nondestructive detection of defective visible-reflective solder joints in solenoid coil connectors

Dong

Oviedo-Tolentino

Hernández-Guerrero

et al. 2022

Advances in Mechanical Engineering

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Nondestructive infrared detection of solder defects in solenoid coil connectors was investigated. A detailed experimental setup and a meticulous and repeatable experimental procedure were developed. The experimental methods were firmly based on the application of heat transfer science. Time averaged excess temperature ([Formula: see text]) was the sole discrimination parameter, and a difference larger than 2°C between good and defective solders was conservatively considered for good discrimination. Analysis of variance determined the minimum differences in [Formula: see text] between good and defective solder joints. The effects in discriminability of heating time ( th), time integration interval length ( ti), region of interest (ROI) geometry, and solder radiative properties were investigated. A rectangular ROI tightly enclosing the good solder was found as the best discrimination ROI. Matte black paint increasing radiative emission of solder joints significantly reduced discriminability. The defects were discriminated from the good solder for a rectangular ROI, th = 78 s, and ti = 20 s. Moreover, an excellent total inspection time of 34 s per solenoid coil is obtained by considering a well-controlled inspection environment during manufacturing, severe defects detection, and a possible 1°C difference for discrimination. Several aspects of the novel inspection procedure developed can greatly benefit the thermal imaging scientific community.

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The Research of Qualification Detection of Cable Joint Solder Joint Based on DCNN

Lei

Zhao

2019

2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

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Automatic Optical Inspection of Solder Ball Burn Defects on Head Gimbal Assembly

Cited by 4 publications

References 22 publications

Improved YOLOv5 Network for Aviation Plug Defect Detection

Improved YOLOv5 Network for Aviation Plug Defect Detection

Infrared nondestructive detection of defective visible-reflective solder joints in solenoid coil connectors

The Research of Qualification Detection of Cable Joint Solder Joint Based on DCNN

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