A region segmentation method to measure multiple features using a tactile scanning probe

Coordinate measuring machines (CMMs) have been widely used in industry to precisely measure parts for inspection or quality control. One of the main barriers to using a CMM touch-trigger probe is the cumbersome programming work required to identify the probing points and for scan path planning. In this paper, we propose a practical data-segmentation method to continuously measure multiple features of the workpiece using a scanning probe. This approach takes advantage of the fast data-capture capability of the scanning probe and, subsequently, the point dataset is segmented using the information extracted from the CAD model of the part. This methodology does not require tedious programming and all desired measurement results can be obtained from a single scan. The principle of the method is presented, and the feasibility of the method is experimentally verified on a bridge-type Hexagon DEA Global CMM equipped with a Leitz LSP-X1 probe. The proposed method avoids manual operation errors and generates more sampling points than traditional methods; therefore, theoretically providing lower measurement uncertainty. The test results also indicate that the new method using a scanning probe is easy to implement and can save more than 90% measurement time in comparison with a conventional touch-trigger method

Feature-based hybrid inspection planning for complex mechanical parts

Globalization and emerging new powers in the manufacturing world are among many challenges, major manufacturing enterprises are facing. This resulted in increased alternatives to satisfy customers\u27 growing needs regarding products\u27 aesthetic and functional requirements. Complexity of part design and engineering specifications to satisfy such needs often require a better use of advanced and more accurate tools to achieve good quality. Inspection is a crucial manufacturing function that should be further improved to cope with such challenges. Intelligent planning for inspection of parts with complex geometric shapes and free form surfaces using contact or non-contact devices is still a major challenge. Research in segmentation and localization techniques should also enable inspection systems to utilize modern measurement technologies capable of collecting huge number of measured points. Advanced digitization tools can be classified as contact or non-contact sensors. The purpose of this thesis is to develop a hybrid inspection planning system that benefits from the advantages of both techniques. Moreover, the minimization of deviation of measured part from the original CAD model is not the only characteristic that should be considered when implementing the localization process in order to accept or reject the part; geometric tolerances must also be considered. A segmentation technique that deals directly with the individual points is a necessary step in the developed inspection system, where the output is the actual measured points, not a tessellated model as commonly implemented by current segmentation tools. The contribution of this work is three folds. First, a knowledge-based system was developed for selecting the most suitable sensor using an inspection-specific features taxonomy in form of a 3D Matrix where each cell includes the corresponding knowledge rules and generate inspection tasks. A Travel Salesperson Problem (TSP) has been applied for sequencing these hybrid inspection tasks. A novel region-based segmentation algorithm was developed which deals directly with the measured point cloud and generates sub-point clouds, each of which represents a feature to be inspected and includes the original measured points. Finally, a new tolerance-based localization algorithm was developed to verify the functional requirements and was applied and tested using form tolerance specifications. This research enhances the existing inspection planning systems for complex mechanical parts with a hybrid inspection planning model. The main benefits of the developed segmentation and tolerance-based localization algorithms are the improvement of inspection decisions in order not to reject good parts that would have otherwise been rejected due to misleading results from currently available localization techniques. The better and more accurate inspection decisions achieved will lead to less scrap, which, in turn, will reduce the product cost and improve the company potential in the market

Integrated tactile-optical coordinate measurement for the reverse engineering of complex geometry

Complex design specifications and tighter tolerances are increasingly required in modern engineering applications, either for functional or aesthetic demands. Multiple sensors are therefore exploited to achieve both holistic measurement information and improved reliability or reduced uncertainty of measurement data. Multi-sensor integration systems can combine data from several information sources (sensors) into a common representational format in order that the measurement evaluation can benefit from all available sensor information and data. This means a multi-sensor system is able to provide more efficient solutions and better performances than a single sensor based system. This thesis develops a compensation approach for reverse engineering applications based on the hybrid tactile-optical multi-sensor system. In the multi-sensor integration system, each individual sensor should be configured to its optimum for satisfactory measurement results. All the data measured from different equipment have to be precisely integrated into a common coordinate system. To solve this problem, this thesis proposes an accurate and flexible method to unify the coordinates of optical and tactile sensors for reverse engineering. A sphere-plate artefact with nine spheres is created and a set of routines are developed for data integration of a multi-sensor system. Experimental results prove that this novel centroid approach is more accurate than the traditional method. Thus, data sampled by different measuring devices, irrespective of their location can be accurately unified. This thesis describes a competitive integration for reverse engineering applications where the point cloud data scanned by the fast optical sensor is compensated and corrected by the slower, but more accurate tactile probe measurement to improve its overall accuracy. A new competitive approach for rapid and accurate reverse engineering of geometric features from multi-sensor systems based on a geometric algebra approach is proposed and a set of programs based on the MATLAB platform has been generated for the verification of the proposed method. After data fusion, the measurement efficiency is improved 90% in comparison to the tactile method and the accuracy of the reconstructed geometric model is improved from 45 micrometres to 7 micrometres in comparison to the optical method, which are validated by case study

Automatic tolerance inspection through Reverse Engineering: a segmentation technique for plastic injection moulded parts

This work studies segmentations procedures to recognise features in a Reverse Engineering (RE) application that is oriented to computer-aided tolerance inspection of injection moulding die set-up, necessary to manufacture electromechanical components. It will discuss all steps of the procedures, from the initial acquisition to the final measure data management, but specific original developments will be focused on the RE post-processing method, that should solve the problem related to the automation of the surface recognition and then of the inspection process. As it will be explained in the first two Chapters, automation of the inspection process pertains, eminently, to feature recognition after the segmentation process. This work presents a voxel-based approach with the aim of reducing the computation efforts related to tessellation and curvature analysis, with or without filtering. In fact, a voxel structure approximates the shape through parallelepipeds that include small sub-set of points. In this sense, it represents a filter, since the number of voxels is less than the total number of points, but also a local approximation of the surface, if proper fitting models are applied. Through sensitivity analysis and industrial applications, limits and perspectives of the proposed algorithms are discussed and validated in terms of accuracy and save of time. Validation case-studies are taken from real applications made in ABB Sace S.p.A., that promoted this research. Plastic injection moulding of electromechanical components has a time-consuming die set-up. It is due to the necessity of providing dies with many cavities, which during the cooling phase may present different stamping conditions, thus defects that include lengths outside their dimensional tolerance, and geometrical errors. To increase the industrial efficiency, the automation of the inspection is not only due to the automatic recognition of features but also to a computer-aided inspection protocol (path planning and inspection data management). For this reason, also these steps will be faced, as the natural framework of the thesis research activity. The work structure concerns with six chapters. In Chapter 1, an introduction to the whole procedure is presented, focusing on reasons and utilities of the application of RE techniques in industrial engineering. Chapter 2 analyses acquisition issues and methods that are related to our application, describing: (a) selected hardware; (b) adopted strategy related to the cloud of point acquisition. In Chapter 3, the proposed RE post-processing is described together with a state of art about data segmentation and surface reconstruction. Chapter 4 discusses the proposed algorithms through sensitivity studies concerning thresholds and parameters utilised in segmentation phase and surface reconstruction. Chapter 5 explains briefly the inspection workflow, PDM requirements and solution, together with a preliminary assessing of measures and their reliability. These three chapters (3, 4 and 5) report final sections, called “Discussion”, in which specific considerations are given. Finally, Chapter 6 gives examples of the proposed segmentation technique in the framework of the industrial applications, through specific case studies

Intelligent laser scanning for computer aided manufacture.

Reverse engineering requires the acquisition of large amounts of data describing the surface of an object, sufficient to replicate that object accurately using appropriate fabrication techniques. This is important within a wide range of commercial and scientific fields where CAD models may be unavailable for parts that must be duplicated or modified, or where a physical model is used as a prototype. The three-dimensional digitisation of objects is an essential first step in reverse engineering. Optical triangulation laser sensors are one of the most popular and common non-contact methods used in the data acquisition process today. They provide the means for high resolution scanning of complex objects. Multiple scans of the object are usually required to capture the full 3D profile of the object. A number of factors, including scan resolution, system optics and the precision of the mechanical parts comprising the system may affect the accuracy of the process. A single perspective optical triangulation sensor provides an inexpensive method for the acquisition of 3D range image data