Robot path planning for dimensional measurement in automotive manufacturing

This paper addresses the robot path planning problem in our effort to develop a fully automated dimensional measurement system using an eye-in-hand robotic manipulator. First, the CAD-based vision sensor planning system developed in our lab is briefly introduced; it uses both the CAD model and the camera model to plan camera viewpoints. The planning system employs a decomposition-based approach to generate camera viewpoints that satisfy given task constraints. Second, to improve the efficiency of the eye-in-hand robot inspection system, robot path planning is studied, which is the focus of this paper. This problem is rendered as a Traveling Salesman Problem (TSP). A new hierarchical approach is developed to solve the TSP into its suboptimality. Instead of solving a large size TSP, this approach utilizes the clustering nature of the viewpoints and converts the TSP into a clustered Traveling Salesman Problem (CTSP). A new algorithm, which favors the intergroup paths, is proposed to solve the CTSP quickly. Performance of the new algorithm is analyzed. It is shown that instead of a fixed performance ratio as reported in some existing work, a constant bound can be achieved which is related to the diameter of the clusters. Experimental results demonstrate the effectiveness of the robot motion planning system. The proposed path planning approach can obtain sub-optimal solutions quickly for many large scale TSPs, which are common problems in many robotic applications. Copyright © 2005 by ASME.Link_to_subscribed_fulltex

Smart optical coordinate and surface metrology

Manufacturing has recently experienced increased adoption of optimised and fast solutions for checking product quality during fabrication, allowing for manufacturing times and costs to be significantly reduced. Due to the integration of machine learning algorithms, advanced sensors and faster processing systems, smart instruments can autonomously plan measurement pipelines, perform decisional tasks and trigger correctional actions as required. In this paper, we summarise the state of the art in smart optical metrology, covering the latest advances in integrated intelligent solutions in optical coordinate and surface metrology, respectively for the measurement of part geometry and surface texture. Within this field, we include the use of a priori knowledge and implementation of machine learning algorithms for measurement planning optimisation. We also cover the development of multi-sensor and multi-view instrument configurations to speed up the measurement process, as well as the design of novel feedback tools for measurement quality evaluation

Six-Sigma Quality Management of Additive Manufacturing

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps—define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to post-build inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM

Quality management approach of product data models for shipbuilding

A quality management approach to manage the quality of ship product model data is discussed. It aims to improve and to automate product data model control to make the design and production processes more reliable. This approach is supporting an efficient correction of decient structural designs under visual guidance towards the identied problems. Two international standards ISO STEP-59 and ISO/PAS 26183:2006 are utilized in this thesis

Intelligent viewpoint selection for efficient CT to video registration in laparoscopic liver surgery

PURPOSE: Minimally invasive surgery offers advantages over open surgery due to a shorter recovery time, less pain and trauma for the patient. However, inherent challenges such as lack of tactile feedback and difficulty in controlling bleeding lower the percentage of suitable cases. Augmented reality can show a better visualisation of sub-surface structures and tumour locations by fusing pre-operative CT data with real-time laparoscopic video. Such augmented reality visualisation requires a fast and robust video to CT registration that minimises interruption to the surgical procedure. METHODS: We propose to use view planning for efficient rigid registration. Given the trocar position, a set of camera positions are sampled and scored based on the corresponding liver surface properties. We implement a simulation framework to validate the proof of concept using a segmented CT model from a human patient. Furthermore, we apply the proposed method on clinical data acquired during a human liver resection. RESULTS: The first experiment motivates the viewpoint scoring strategy and investigates reliable liver regions for accurate registrations in an intuitive visualisation. The second experiment shows wider basins of convergence for higher scoring viewpoints. The third experiment shows that a comparable registration performance can be achieved by at least two merged high scoring views and four low scoring views. Hence, the focus could change from the acquisition of a large liver surface to a small number of distinctive patches, thereby giving a more explicit protocol for surface reconstruction. We discuss the application of the proposed method on clinical data and show initial results. CONCLUSION: The proposed simulation framework shows promising results to motivate more research into a comprehensive view planning method for efficient registration in laparoscopic liver surgery

Solid Modeling

To appear in the Encyclopedia of Electrical and Electronics Engineering, Ed. J. Webster, John Wiley & Sons, 1999.A solid model is a digital representation of the geometry of an existing or envisioned physical object. Solid models are used in many industries, from entertainment to health care. They play a major role in the discrete-part manufacturing industries, where precise models of parts and assemblies are created using solid modeling software or more general computer-aided design (CAD) systems. Solid modeling is an interdisciplinary field that involves a growing number of areas. Its objectives evolved from a deep understanding of the practices and requirements of the targeted application domains. Its formulation and rigor are based on mathematical foundations derived from general and algebraic topology, and from Euclidean, differential, and algebraic geometry. The computational aspects of solid modeling deal with efficient data structures and algorithms, and benefit from recent developments in the field of computational geometry. Efficient processing is essential, because the complexity of industrial models is growing faster than the performance of commercial workstations. Techniques for modeling and analyzing surfaces and for computing their intersections are important in solid modeling. This area of research, sometimes called computer aided geometric design, has strong ties with numerical analysis and differential geometry. Graphic user-interface (GUI) techniques also play a crucial role in solid modeling, since they determine the overall usability of the modeler and impace the user's productivity. There have always been strong symbiotic links and overlaps between the solid modeling community and the computer graphics community. Solid modeling interfaces are based on efficient three-dimensional (3D) graphics techniques, whereas research in 3D graphics focuses on fast or photo-realistic rendering of complex scenes, often composed of solid models, and on realistic or artistic animations of non-rigid objects. A similar symbiotic relation with computer vision is regaining popularity, as many research efforts in vision are model-based and attempt to extract 3D models from images or video sequences of existing parts or scenes. These efforts are particularly important for solid modeling, because the cost of manually designing solid models of existing objects or scenes far excees the other costs (hardware, software, maintenance, and training) associated with solid modeling. Finally, the growing complexity of solid models and the growing need for collaboration, reusability of design, and interoperability of software require expertise in distributed databases, constraint management systems, optimization techniques, object linking standards, and internet protocols. This report provides a brief overview of the solid modeling field, its fundamental technologies, and some important applications

Study of medical image data transformation techniques and compatibility analysis for 3D printing

Various applications exist for additive manufacturing (AM) and reverse engineering (RE) within the medical sector. One of the significant challenges identified in the literature is the accuracy of 3D printed medical models compared to their original CAD models. Some studies have reported that 3D printed models are accurate, while others claim the opposite. This thesis aims to highlight the medical applications of AM and RE, study medical image reconstruction techniques into a 3D printable file format, and the deviations of a 3D printed model using RE. A case study on a human femur bone was conducted through medical imaging, 3D printing, and RE for comparative deviation analysis. In addition, another medical application of RE has been presented, which is for solid modelling. Segmentation was done using opensource software for trial and training purposes, while the experiment was done using commercial software. The femur model was 3D printed using an industrial FDM printer. Three different non-contact 3D scanners were investigated for the RE process. Post-processing of the point cloud was done in the VX Elements software environment, while mesh analysis was conducted in MeshLab. The scanning performance was measured using the VX Inspect environment and MeshLab. Both relative and absolute metrics were used to determine the deviation of the scanned models from the reference mesh. The scanners' range of deviations was approximately from -0.375 mm to 0.388 mm (range of about 0.763mm) with an average RMS of about 0.22 mm. The results showed that the mean deviation of the 3D printed model (based on 3D scanning) has an average range of about 0.46mm, with an average mean value of about 0.16 mm

Real-time spatial modeling to detect and track resources on construction sites

For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin