Computer aided inspection procedures to support smart manufacturing of injection moulded components

This work presents Reverse Engineering and Computer Aided technologies to improve the inspection of injection moulded electro-mechanical parts. Through a strong integration and automation of these methods, tolerance analysis, acquisition tool-path optimization and data management are performed. The core of the procedure concerns the automation of the data measure originally developed through voxel-based segmentation. This paper discusses the overall framework and its integration made according to Smart Manufacturing requirements. The experimental set-up, now in operative conditions at ABB SACE, is composed of a laser scanner installed on a CMM machine able to measure components with lengths in the range of 5÷250 mm, (b) a tool path optimization procedure and (c) a data management both developed as CAD-based applications

Defining scanning trajectory for on-machine inspection using a laser-plane scanner

International audienceScan path planning for on-machine inspection in a 5-axis machine tool is still a challenge to measure part geometry in a minimum amount of time with a given scanning quality. Indeed, as the laser-plane scanner takes the place of the cutting tool, the time allocated to measurement must be reduced, but not at detrimental of the quality. In this direction, this paper proposes a method for scan path planning in a 5-axis machine tool with the control of scanning overlap. This method is an adaptation of a method dedicated to a robot that has proved its efficiency for part inspection

Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints

Safe navigation of Micro Aerial Vehicles (MAVs) requires not only obstacle-free flight paths according to a static environment map, but also the perception of and reaction to previously unknown and dynamic objects. This implies that the onboard sensors cover the current flight direction. Due to the limited payload of MAVs, full sensor coverage of the environment has to be traded off with flight time. Thus, often only a part of the environment is covered. We present a combined allocentric complete planning and trajectory optimization approach taking these sensor visibility constraints into account. The optimized trajectories yield flight paths within the apex angle of a Velodyne Puck Lite 3D laser scanner enabling low-level collision avoidance to perceive obstacles in the flight direction. Furthermore, the optimized trajectories take the flight dynamics into account and contain the velocities and accelerations along the path. We evaluate our approach with a DJI Matrice 600 MAV and in simulation employing hardware-in-the-loop.Comment: In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 201

Framework for simulation-based Trajectory Planning and Execution of Robots equipped with a Laser Scanner for Measurement and Inspection

Shorter product life cycles require ever faster planning processes for the manufacturing of products. This also applies for measuring processes to ensure compliance with geometric workpiece specifications. In addition, these processes must be designed to be increasingly flexible since mass customization steadily increases product variety. Laser scanning systems mounted on robots offer the possibility of measuring a wide variety of geometries with low measurement uncertainty. In this paper, a method is presented with which measurement trajectories can be planned and virtually validated. We thereby combine and extend existing trajectory planning approaches and explicitly integrate robot kinematics into the planning approach to account for feasibility of the planned trajectories. These can then be directly transferred to the available measurement system. This is enabled by a real time interface directly connecting a virtual environment for measurement simulation and the real measurement system

Kinematic Modeling Of An Automated Laser Line Scanning System

This research work describes the geometric coordinate transformation in an automated laser line scanning system caused by movements required for scanning a component surface. The elements of an automated laser scanning system (robot, laser line scanner, and the component coordinate system) function as a mechanical linkage to obtain a trajectory on a component surface. This methodology solves the forward kinematics, derives the component surface, and uses inverse kinematic equations to characterize the movement of the entire automated scanning system on point trajectory. To reach a point on the component, joint angles of robot have been calculated. As a result, trajectory path is obtained. This obtained robot poses on point trajectory of the component surface can be used as an input for future work that aims to develop optimal scan paths to collect “best” point cloud data sets. This work contributes in laser scanning inspection of component surfaces in manufacturing, remanufacturing, and reverse engineering applications

Automatic volume inspection for glass blow moulds

In the glass bottle mould making industry, volume control is done by measuring the amount of water needed to fill the mould. This process has several issues. Firstly, it requires a trained operator to properly seal the mould. Secondly, different operators will lead to different volume values. Another issue is related to the time and work necessary for the procedure, which can take up to 20 minutes for a single mould, making it unsuitable to inspect several moulds of the same series. These issues can be solved by automating the procedure. By using reverse engineering systems to obtain the internal cavity surfaces, comparative studies can be done, such as wear study, enabling the optimization of the moulds. The goal of this project is to establish a system to automate the inspection of the moulds which will result in the acquisition of the moulding surfaces. Then, the volume of the moulds and surface deviations in specific areas can be measured. The development of this project focused in two main areas: the development of a script, where the volume is calculated and the surface is inspected, from cloud points, to determine if the mould is in an acceptable state; and the study of technologies capable of acquiring the mould’s surface while simultaneously being automatable. As for this study, several case studies using laser and structured light are performed to understand the abilities and limitations of these technologies. The first study was done using polished cast iron moulds to determine the ability to acquire the surface and obtain the volume. Then, the ability to present proper comparative results is explored by using a set of unpolished cast iron moulds and then these same moulds once polished to verify if the used systems can obtain the deviations between the two situations. Finally, the validation of the technologies was done using a demo bronze mould, where surface deviations were inspected as well as a ring gauge where the inner cylinder was used for inspection. From these cases, the used laser scanner was able to obtain the volumes of the moulds as well as proper comparative results without spray. As for the used structured light system, it proved unable to acquire the surfaces of the moulds and of the ring gauge, requiring spray. Despite this performance, the system is quite automatable and a state-of-the-art structured light system, using blue light, could be used for this purpose. The laser is also a viable solution, but the cost and complexity to automate can be higher than the structured light system

Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR

This paper addressed the challenge of exploring large, unknown, and unstructured industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system is that all the algorithms relied on the multi-resolution of the octomap for the world representation. We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements of the capability of the open-source system to run online and on-board the UAV in real-time. Our approach is compared to different reference heuristics under this simulation environment showing better performance in regards to the amount of explored space. With the proposed approach, the UAV is able to explore 93% of the search space under 30 min, generating a path without repetition that adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411

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

Human-Centered Automation for Resilience in Acquiring Construction Field Information

abstract: Resilient acquisition of timely, detailed job site information plays a pivotal role in maintaining the productivity and safety of construction projects that have busy schedules, dynamic workspaces, and unexpected events. In the field, construction information acquisition often involves three types of activities including sensor-based inspection, manual inspection, and communication. Human interventions play critical roles in these three types of field information acquisition activities. A resilient information acquisition system is needed for safer and more productive construction. The use of various automation technologies could help improve human performance by proactively providing the needed knowledge of using equipment, improve the situation awareness in multi-person collaborations, and reduce the mental workload of operators and inspectors. Unfortunately, limited studies consider human factors in automation techniques for construction field information acquisition. Fully utilization of the automation techniques requires a systematical synthesis of the interactions between human, tasks, and construction workspace to reduce the complexity of information acquisition tasks so that human can finish these tasks with reliability. Overall, such a synthesis of human factors in field data collection and analysis is paving the path towards “Human-Centered Automation” (HCA) in construction management. HCA could form a computational framework that supports resilient field data collection considering human factors and unexpected events on dynamic job sites. This dissertation presented an HCA framework for resilient construction field information acquisition and results of examining three HCA approaches that support three use cases of construction field data collection and analysis. The first HCA approach is an automated data collection planning method that can assist 3D laser scan planning of construction inspectors to achieve comprehensive and efficient data collection. The second HCA approach is a Bayesian model-based approach that automatically aggregates the common sense of people from the internet to identify job site risks from a large number of job site pictures. The third HCA approach is an automatic communication protocol optimization approach that maximizes the team situation awareness of construction workers and leads to the early detection of workflow delays and critical path changes. Data collection and simulation experiments extensively validate these three HCA approaches.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

3d Scanning And The Impact Of The Digital Thread On Manufacturing And Re-Manufacturing Applications

3D laser line scanners are becoming a powerful technology for capturing point cloud datasets and collecting dimensional information for many objects. However, the use of point cloud is limited due to many factors. These include the lack of on deep understanding of the effect of point cloud parameters on scan quality. This knowledge is critical to gaining an understanding of the measurement in point cloud. Currently, there are no adequate measurement procedures for 3D scanners. There is a need for standardized measurement procedures to evaluate 3D scanner accuracy due to uncertainties in 3D scanning, such as surface quality, surface orientation and scan depth [6]. The lack of standardized procedures does not allow the technology to be fully automated and used in manufacturing facilities that would allow 100% in-line inspection. In this dissertation I worked on accomplishing four tasks that will achieve the objective of having a standardized measurement procedure that is critical to develop an automated laser scanning system to avoid variations and have consistent data capable of identifying defects. The four tasks are: (1) linking the robot workspace with the scanner workspace; (2) studying the effect of the scanning speed and the resolution on point cloud quality by conducting an experiment with systematically varied scan parameters on scan quality; (3) studying the overall error of that is associated with the transformation of the point cloud in a remanufacturing facility using additive manufacturing. The parameters that were tested are the effect of view angle, standoff distance, speed, and resolution. Knowing the effect of these parameters is important in order to generate the scan path that provides the best coverage and quality of points collected. There is also a need to know the impact of all the scanning parameters especially the speed and the resolution; (4) modeling a machine learning tool to optimize the parameters of different scanning techniques after collecting the scanning results to select the optimal ones that provide the best scan quality. With the success of this work, the advancement and practice of automated quality monitoring in manufacturing will increase