Solder Paste Scooping Detection by Multilevel Visual Inspection of Printed Circuit Boards

Abstract

Abstract-In this paper we introduce an automated Bayesian visual inspection framework for Printed Circuit Board (PCB) assemblies, which is able to simultaneously deal with various shaped Circuit Elements (CE) on multiple scales. We propose a novel Hierarchical Multi Marked Point Process (H M MPP) model for this purpose, and demonstrate its efficiency on the task of solder paste scooping detection and scoop area estimation, which are important factors regarding the strength of the joints. A global optimization process attempts to find the optimal configuration of circuit entities, considering the observed image data, prior knowledge, and interactions between the neighboring CEs. The computational requirements are kept tractable by a data driven stochastic entity generation scheme. The proposed method is evaluated on real PCB data sets containing 125 images with more than 10.000 splice entities.

Hierarchical image content analysis with an embedded marked point process framework

“…Goal: shape extraction and grouping of Circuit Elements (CEs) in uniquely designed PCBs, detecting special soldering errors called scooping [7].…”

“…Model elements: parent objects are CEs of various shapes, child objects are scoops, modeled by pairs of concentric ellipses [7]. Groups are formed by CEs which likely have similar functionalities [10] (Fig 4a).…”

“…Simple prior interaction constraints such us non-overlapping or parallel alignment are also utilized there to refine the accuracy of detection, but in this way only very limited amount of high level structural information can be exploited from the global scenario. Previous attempts for multi-level image understanding followed either region based [6], object based [7,8] or hybrid [9] approaches. However, the above models were suited to a specific application areas with specific inputs: remotely sensed optical images [6,9] or Lidar point clouds [8], and Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCB), using µm resolution images [7].…”

“…Previous attempts for multi-level image understanding followed either region based [6], object based [7,8] or hybrid [9] approaches. However, the above models were suited to a specific application areas with specific inputs: remotely sensed optical images [6,9] or Lidar point clouds [8], and Automatic Optical Inspection (AOI) of Printed Circuit Boards (PCB), using µm resolution images [7]. Experiences show that for such complex, application dependent models, the adaption to another application domain is rarely straightforward, needing a significant modeling and implementation work.…”

Detection of PCB Soldering Defects using Template Based Image Processing Method

“…These faults, which are related to the human factor, usually lead to slower production, lower quality standards and increased cost [3]. In order to overcome these problems, computer vision systems are used for PCB soldering defect inspection [4,5]. The main components of computer vision systems consist of an external component, a camera and an image processing algorithm [6].…”