Abstract
Remanufactured products can save up to 80% of production and energy costs whilst generating lower CO2 emissions. The key success factors for remanufacturing are quality, lead-time and cost. Extensive work within the industry and the detailed analysis of the remanufacturing process has shown that component inspection has significant bearing on overall productivity. Remanufacturing lacks automation because activities are predominantly manual. Automation of remanufacturing process will not only decrease the number of non-remanufacturable components, through decreasing cost and increasing consistency in quality, but also attract industries to design for remanufacture. A digital model of the component is required to automate the disassembly process and move towards industry 4.0 and cyber physical systems. There are several expensive techniques to create a digital model, which are not feasible for the remanufacturing industry. The research paper aims to check feasibility of using Visual Structure for Motion (VFM), a relatively low cost method, to develop a 3D digital model, for automation of the automotive engine (in as received condition) disassembly process using industrial robots. These experiments assess the scientific feasibility of using Videogrammetry to acquire pre-disassembly 3D model of the engine. Multiple 2D images were acquired and processed to find matching common features. The location of the camera was calculated through the matching features, producing a three-dimensional digital representation of the captured volume. A sparse point cloud was initially created and was then converted into a dense 3D point cloud. The 3D point cloud was converted into a meshed model. 2D images were stitched together to create a virtual model of the engine with surface texture and colour. Small features were clearly visible in the 3D model.
Highlights
Remanufactured products can save up to 80% in production and energy costs whilst generating lower CO2 emissions
The 3D point cloud was converted into a meshed model. 2D images were stitched together to create a virtual model of the engine with surface texture and colour
Visual Structure for Motion (VSFM) compares every image in a set with every other one to find the best match
Summary
Remanufactured products can save up to 80% in production and energy costs whilst generating lower CO2 emissions. Initial experiments are performed to assess the feasibility of Videogrammetry to acquire pre-disassembly three dimensional (3D) model of the engine. The application of this vision based technique will help automate the remanufacturing process. 3D reconstructed model with colour texture will aid machine vision tools to be implemented, and will pave way for the use of machine learning algorithms. Both these methodologies, machine vision and machine learning, will be used for robust identification and calculating location of features, for example, bolts type and location. Accuracy of the process will be assessed in the second phase of research. 3D model of the engine, acquired using a conventional 3D scanner, will be compared to the 3D reconstructed model using VSFM technique
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