In this paper, we present an improved version of our Dual Laser Triangulation System, a low-cost color 3D model acquisition system built with commonly available machine vision products. The system produces a color point cloud model of scanned objects that can be used to perform completeness inspection tasks on assembly lines. In particular, we show that model acquisition and reconstruction can be achieved in real-time using such a low-cost solution. Our results demonstrate that 3D-based inspection can be achieved readily and economically in a real industrial production environment
Cracks are the main source of failure in the production of metal parts: systems for checking their presence are therefore crucial for defect-free production. In this paper, an autonomous system for performing this quality control is presented. The system is equipped with a heating tool, a thermocamera, and a robot to handle the part. The inspection process is based on the observation of the propagation of thermal waves through the inspected part, a method that can highlight very small cracks with high reliability. A knowledge-based approach to visual inspection is exploited for detecting the cracks: all the system parameters are known by means of an accurate calibration of the workcell. The system was tested on a large dataset and demonstrated its capability of detecting tiny production defects, that can lead to dangerous failures when the metal components are put under strong mechanical stress
This paper describes an innovative visual inspection system for the detection of small cracks in metal parts. Given the extremely low dimension of the defects to be detected, the system is based on a thermographic approach: defects are recognized analyzing the heat flux induced by an excitation. The system is able to analyze parts of very high complexity, like a crankshaft, thanks to the introduction of an articulated robot, used for moving the part. The system also benefits from a deep knowledge of the inspected part and of the imaging system: this is exploited to reduce the high number of artifacts and reflections that appear in thermographic images when heat sources are employed. The core of the inspection mechanism is a computer vision algorithm that is capable of analyzing the thermographic images, extract the thermal information, and exploit a Support Vector Machine (SVM) classifier to provide a final decision on the presence of a crack in the analyzed part.
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