Thermobot: towards semi-autonomous, thermographic detection of cracks

THERMOBOT: TOWARDS SEMI-AUTONOMOUS, THERMOGRAPHIC DETECTION OF CRACKS

C. Eitzinger⁽¹⁾, S. Ghidoni⁽²⁾, E.Menegatti ⁽²⁾

Via Gradenigo 6/A I-35131 Padova, Italy

Despite considerable progress in the automation of production processes visual inspection of products and parts is still done manually in a wide variety of inspection tasks. The current procedure for crack detection is a process that dates back to the 1920s and is called “magnetic particle inspection”. This method is infamous in industry, because it is a cumbersome, dirty process that is often done manually even in otherwise fully automatic production lines. The component to be tested is magnetized before applying a suspension of finely divided coloured or fluorescent magnetic particles. Cracks or inclusions cause the magnetic flux to break the surface forming free magnetic poles and the magnetic particles will collect at these locations indicating a crack. Using UV-light the fluorescent particles in the suspension are activated to increase the visibility of the cracks. Inspection of complex parts is usually done manually, whereas for simple geometries machine vision systems can be used for automatic detection.

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  thermographic processes and process models for the automatic detection of cracks in parts of complex geometry, varying surface structures and for different materials.
  
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  Automatic path and motion planning that uses a thermographic process model to automatically generate a path for the inspection robot from 3D CAD data.
  
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  Thermo-image analysis methods that not only aim at detecting cracks and making an accept/reject decision for the whole part, but also have the capability of self-evaluating their performance in the crack detection.
  

Models of thermographic imaging

Most frequently the models used in applications of thermography have their focus on conduction of heat in solids in the context of the energy source, which is used to heat up the specimen under inspection [1, 2, 3, 4]. Heat transport is the main mechanism that enables thermography to detect defects such as cracks. In addition to heat flow inside the part two additional models are required to fully cover the thermographic image acquisition process. The first one deals with the radiation from the surface of a specimen, which includes the actual (heat) signal as well as components [5] such as noise, reflections from environment or the influence of the surface properties. The second one, which is much simpler, deals with the properties of sensor and imaging considerations [6, 7]. Adding radiation from environment, one creates a model that describes the “radiometric measurement problem” [8]. The model for description of the surface temperature distribution and evolution is based on heat production and transfer in a solid. For crack detection an in-plane flux must be induced, to obtain measurable temperature differences along the crack. This can be achieved by heating up only a small area of the surface. From this “hot spot”, heat will flow mainly in parallel to the surface, and this flow is interrupted by a crack. To improve robustness of the detection, an evaluation method [9] was developed, that identifies heat flux phenomena by watching the evolution of temperature profiles. Description of temperature as consequence of heat conduction is based on the differential equations of heat conduction, for which explicit solutions exist only for simple models [10, 11]. In more complex situations simulations [12] such as finite elements methods (FEM) are used to predict signals [13]. In case of real time applications, current simulation methods would take too much time, so it is mainly used for design purposes.

Robot path planning for inspection tasks

To calculate a path on a 3D object that can be used for inspection, several constraints need to be adhered. For example, the camera has to be in a certain (range of) distances from the part and it has to view the relevant surface under a certain angle. This problem is inherent in many camera-based inspection problems, such as completeness inspection [14] or for automated 3D object reconstruction [15]. It aims at the automatic generation of an (optimal) sequence of viewpoints that satisfy certain constraints, such as full coverage of the part [16]. This is known as “view planning problem”. In order to solve the associated optimization problem algorithms that can deal with discrete search spaces, such as genetic algorithms [17] or particle swarm optimization are regularly used.

Thermo-image analysis

Most of the systems proposed in literature for thermographic image processing are quite basic: the processing work flows are composed of thresholding, edge detection, region growing, and template matching [18]. However, more advanced image processing techniques have been proposed. Besides traditional techniques coming from the field of computer vision, several specific methods have been developed for thermal image processing [19]. These unique techniques are sometimes based on the underlying heat-conduction physics. The methods are used either at image preprocessing and/or processing stages [20]. For instance, thermal contrasts have the advantages of being little sensitive to noise and to the surface optical properties. Another image processing technique which proved to be useful for image segmentation is pulsed phase thermography (PPT). This is a processing method in which the thermal images are transformed from the time domain to the frequency domain to calculate the phase and the amplitude of the signal in the images [21]. The phase is particularly advantageous since it is less affected by environmental reflections, emissivity variations, non-uniform heating, surface geometry and orientation. It should be noted for the sake of completeness that all the image segmentation algorithms developed in computer vision are also applicable in thermo-image analysis. We just cite [22] and [23] as recent results that explain adaptive image segmentation methods as well as optimization methods that can automatically tune segmentation algorithms to a particular task.
OUR APPROACH

System overview

The hardware components of the automatic inspection system include a robot, a heating system (in our case a laser unit), a thermocamera and a processing unit. While the robot itself can be any general purpose robot, the robot’s control unit has to fulfill the requirement that accurate, time-stamped position information (joint angles) can be acquired at high frequency. This is required to synchronize image acquisition and robot motion. The laser unit’s main function is to locally heat the part. The specific parameters of the laser substantially depend on the thermodynamic properties of the parts to be inspected. For the thermocamera the key feature – aside from high resolution – is a sufficiently high frame rate to allow the acquisition of image sequences at high frame rates. This is particularly important for metallic parts, where the heat dissipates in less than a second. A central processing unit links all the component and collects the robot’s position as well as the camera image for each time instance. These data are then processed by image segmentation and classification algorithms to distinguish between different types of defects and to find a final good/bad classification for the whole part.

It should be noted that for practical reasons in many applications the robot will be holding the part and not the camera/laser unit.

Model of thermographic imaging

The thermographic imaging model is at the heart of the path planning algorithm. The model basically answers the question which area on the part can be inspected, if the part is placed in a certain position and orientation under the camera.


Figure 1. Area that can be inspected (basic concept).
The area that can be inspected is determined by multiple factors. In the most simple case of a flat surface placed at an ideal angle under the camera and laser the area will have a ring-shaped form as shown in figure 1. At the centre of the ring there is the laser spot that results in a bright, white region in the image, where the pixels of the thermocamera are fully saturated and no analysis is possible. As the heat dissipates isotropically the signal becomes weaker until no more contrast is achieved. This area defines the outer edge of the ring-shaped region. During inspection the ring is wandering across the surface leaving a trail where the part has been inspected. Cracks will become visible as a temperature gradient that is caused by the interruption of the heat flow. It should be noted that the sensitivity of the detection depends on the orientation of the crack relative to the laser spot. If the crack is propagating radially from the laser spot, it will not be visible as there is no heat flow across the crack.

In the more realistic case of a non-flat part, the situation becomes significantly more complex. The model has to consider that laser and camera are not placed in an ideal position relative to the part’s surfaces and that the heat propagates in a non-flat area. An approximation of the area that can be checked, may be obtained by projecting the ring-shaped region onto the part’s 3D surface. Additionally, self-occlusions of the part have to be considered as well as areas of high curvature, where the above mentioned approximation is invalid. Those areas have to be excluded from the region that can be checked. The calculations that are involved in such estimations can only be done numerically.

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