3D characterization of hot metallic shells during industrial forging

The three-dimensional (3D) size and morphology of high-temperature metal components need to be measured in real time during manufacturing processes, such as forging and rolling. Since the surface temperature of a metal component is very high during the forming and manufacturing process, manually measuring the size of a metal component at a close distance is difficult; hence, a non-contact measurement technology is required to complete the measurement. Recently, machine vision technology has been developed, which is a non-contact measurement technology that only needs to capture multiple images of a measured object to obtain the 3D size and morphology information, and this technology can be used in some extreme conditions. Machine vision technology has been widely used in industrial, agricultural, military and other fields, especially fields involving various high-temperature metal components. This paper provides a comprehensive review of the application of machine vision technology in measuring the 3D size and morphology of high-temperature metal components. Furthermore, according to the principle and method of measuring equipment structures, this review highlights two aspects in detail: laser scanning measurement and multi-view stereo vision technology. Special attention is paid to each method through comparisons and analyses to provide essential technical references for subsequent researchers.

Section: Laser Scanning Measurement Methodsmentioning

confidence: 99%

Three-Dimensional Morphology and Size Measurement of High-Temperature Metal Components Based on Machine Vision Technology: A Review

Wen

Wang

Zhang

et al. 2021

“…In 2011, Du et al [3,4] improved the aforementioned system by replacing the time-of-flight component with a two-dimensional laser range scanner that generates a line of data at every scan to accelerate the measurement process. Furthermore, in 2012, a shape measurement system with two 3D laser radars was developed by Youssef et al [5]. It acquires an entire area of point cloud data in one shot, but the data are sparse and only reaches a precision of 6 mm per point.…”

Section: Introductionmentioning

confidence: 99%

A Robot-Driven 3D Shape Measurement System for Automatic Quality Inspection of Thermal Objects on a Forging Production Line

Han

Cheng

et al. 2018

The three-dimensional (3D) geometric evaluation of large thermal forging parts online is critical to quality control and energy conservation. However, this online 3D measurement task is extremely challenging for commercially available 3D sensors because of the enormous amount of heat radiation and complexity of the online environment. To this end, an automatic and accurate 3D shape measurement system integrated with a fringe projection-based 3D scanner and an industrial robot is presented. To resist thermal radiation, a double filter set and an intelligent temperature control loop are employed in the system. In addition, a time-division-multiplexing trigger is implemented in the system to accelerate pattern projection and capture, and an improved multi-frequency phase-shifting method is proposed to reduce the number of patterns required for 3D reconstruction. Thus, the 3D measurement efficiency is drastically improved and the exposure to the thermal environment is reduced. To perform data alignment in a complex online environment, a view integration method is used in the system to align non-overlapping 3D data from different views based on the repeatability of the robot motion. Meanwhile, a robust 3D registration algorithm is used to align 3D data accurately in the presence of irrelevant background data. These components and algorithms were evaluated by experiments. The system was deployed in a forging factory on a production line and performed a stable online 3D quality inspection for thermal axles.

“…Automatic visual inspection allows for detection of defects by image analysis. This process improves product quality, increases the production rate, avoids errors caused by subjectivity, integrates other systems into the production line, and reduces costs [ 1 , 2 , 3 ]. Industrial machine vision applications are classified into four quality categories according to the characteristics of the inspected product.…”

Section: Introductionmentioning

confidence: 99%

A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor

Madrigal

Branch

Restrepo

et al. 2017

Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generates topological deformations that are not shown with strong contrast in the 2D image. In this paper, we present a method for recognizing surface defects in 3D point clouds. Firstly, we propose a novel 3D local descriptor called the Model Point Feature Histogram (MPFH) for defect detection. Our descriptor is inspired from earlier descriptors such as the Point Feature Histogram (PFH). To construct the MPFH descriptor, the models that best fit the local surface and their normal vectors are estimated. For each surface model, its contribution weight to the formation of the surface region is calculated and from the relative difference between models of the same region a histogram is generated representing the underlying surface changes. Secondly, through a classification stage, the points on the surface are labeled according to five types of primitives and the defect is detected. Thirdly, the connected components of primitives are projected to a plane, forming a 2D image. Finally, 2D geometrical features are extracted and by a support vector machine, the defects are recognized. The database used is composed of 3D simulated surfaces and 3D reconstructions of defects in welding, artificial teeth, indentations in materials, ceramics and 3D models of defects. The quantitative and qualitative results showed that the proposed method of description is robust to noise and the scale factor, and it is sufficiently discriminative for detecting some surface defects. The performance evaluation of the proposed method was performed for a classification task of the 3D point cloud in primitives, reporting an accuracy of 95%, which is higher than for other state-of-art descriptors. The rate of recognition of defects was close to 94%.