Automated fracture surface analysis

The system for image acquisition and analysis of BDWTT specimen fracture surfaces we are developing is of interest for all labs performing these material characterisation tests. It replaces a cumbersome, partly subjective procedure with an objective, reproducible automatic process.

Patrick Goes & Michiel Corryn

Drop weight tear testing is a material characterisation test aimed at avoiding brittle fracture and ensuring crack arrest in pipelines. The specimen of the full material thickness contains a shallow pressed notch and is impact loaded in 3-point bending by dropping a weight on it. A series of specimens is broken under impact loading at a variety of temperatures, and the proportions of ductile fracture (shear) and brittle fracture (cleavage) on the fracture surfaces are measured. In this way, a transition curve of percentage shear versus temperature is constructed for the material. The BDWTT (Battelle Drop Weight Tear Testing) approaches the full-scale behaviour much better than a Charpy test. As it correlates better with crack propagation and arrest behaviour, it is used as a qualification test for pipeline material, specified by standards (API 5L3 and EN10274).

IMAGE ACQUISITION

In the past, both fracture surfaces of the broken sample were put next to each other and photographed with an ordinary handheld camera. The resulting images were not always optimal, making an accurate measurement of the shear and cleavage zones difficult. So, OCAS designed a set-up to improve the image acquisition: it consists of a clamp to fix the specimen into a reproducible position, a flat dome light to obtain a controlled illumination of the fracture surface, and a camera with a telecentric lens to get distortion-free images. Obtaining high-quality and reproducible pictures of the specimen’s fracture surface is now a matter of seconds.

IMAGE ANALYSIS

To determine the shear percentage, instead of coarsely measuring the envelope of the shear and cleavage zones and applying some geometrical formulas to calculate their areas, the currently available calibrated images allow us to determine the areas accurately by counting the pixels in each zone. First, the fracture surface is identified by removing the background, the notch and the specimen sides that are visible in the image. What remains needs to be segmented into shear (ductile) zones on the one hand, and cleavage (brittle) zones on the other. Distinguishing between them is not straightforward and involves multiple criteria (luminance, contrast, texture, etc.). We applied machine learning (ML) algorithms to perform this segmentation, which enabled us to automatically obtain the required shear fracture percentage. ML can also be used to classify the surface fracture appearances into several types, depending on the relative positions of the shear and cleavage zones in the total fracture surface.