Knowledge Capture in CMM Inspection Planning: Barriers and Challenges

Coordinate Measuring Machines (CMM) have been widely used as a means of evaluating product quality and controlling quality manufacturing processes. Many techniques have been developed to facilitate the generation of CMM measurement plans. However, there are major gaps in the understanding of planning such strategies. This significant lack of explicitly available knowledge on how experts prepare plans and carry out measurements slows down the planning process, leading to the repetitive reinvention of new plans while preventing the automation or even semi-automation of the process. The objectives of this paper are twofold: (i) to provide a review of the existing inspection planning systems and discuss the barriers and challenges, especially from the aspect of knowledge capture and formalization; and (ii) to propose and demonstrate a novel digital engineering mixed reality paradigm which has the potential to facilitate the rapid capture of implicit inspection knowledge and explicitly represent this in a formalized way. An outline and the results of the development of an early stage prototype - which will form the foundation of a more complex system to address the aforementioned technological challenges identified in the literature survey - will be given

Investigation and development of an advanced virtual coordinate measuring machine

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityDimensional measurement plays a critical role in product development and quality control. With the continuously increasing demand for tighter tolerances and more complex workpiece shapes in the industry, dimensional metrology often becomes the bottleneck of taking the quality and performance of manufacturing to the next level. As one kind of the most useful and powerful measuring instruments, coordinate measuring machines (CMMs) are widely employed in manufacturing industries. Since the accuracy and efficiency of a CMM have a vital impact on the product quality, productivity and manufacturing cost, the evaluation and improvement of CMM performance have always been important research topics since the invention of CMM. A novel Advanced Virtual Coordinate Measuring Machine (AVCMM) is proposed against such a background. The proposed AVCMM is a software package that provides an integrated virtual environment, in which user can plan inspection strategy for a given task, carry out virtual measurement, and evaluate the uncertainty associated with the measurement result, all without the need of using a physical machine. The obtained estimate of uncertainty can serve as a rapid feedback for user to optimize the inspection plan in the AVCMM before actual measurement, or as an evaluation of the result of a performed measurement. Without involving a physical CMM in the inspection planning or evaluation of uncertainty, the AVCMM can greatly reduce the time and cost needed for such processes. Furthermore, as the package offers vivid 3D visual representation of the virtual environment and supports operations similar to a physical CMM, it does not only allow the user to easily plan and optimise the inspection strategy, but also provide a cost-effective, risk-free solution for training CMM operators. A modular, multitier architecture has been adopted to develop the AVCMM system, which incorporates a number of functional components covering CMM and workpiece modelling, error simulation, inspection simulation, feature calculation, uncertainty evaluation and 3D representation. A new engine for detecting collision/contact has been developed and utilized, which is suitable for the virtual environment of simulated CMM inspections. A novel approach has been established to calculate errors required for the error simulation, where the data are obtained from FEA simulations in addition to conventional experimental method. Monte Carlo method has been adopted for uncertainty evaluation and has been implemented with multiple options available to meet different requirements. A prototype of the proposed AVCMM system has been developed in this research. Its validity, usability and performance have been verified and evaluated through a set of experiments. The principles for utilising the AVCMM in practical use have also been established and demonstrated. The results have indicated that the proposed AVCMM system has great potentials to improve the functionalities and overall performance of CMMs.ORSAS and the School of Engineering and Design of Brunel University

Error compensation and uncertainty evaluation of CMMs based on kinematic error models and gaussian processes

This thesis was submitted for the degree of Masters of Philosophy and awarded by Brunel University London.Given the increasing demand for precision engineering applications, the evaluation of measurement error and uncertainty has been the focus of intensive research to meet the requirements of precision manufacturing processes. Systematic errors of mechanical components affect the accuracy of the production parts. It is therefore best to analyse the geometric accuracy of machine tools before production processes begin. This proposed method is based on simulation in the MATLAB programme, which investigates the influence of the geometric errors of the Coordinate Measuring Machine (CMM) on the calibration. The advantages of this measurement procedure are reduced physical measuring times, reduced measurement uncertainties as well as volumetric measurement, and compensation for CMM geometric errors. In this research, theoretical modelling of the local, kinematic error model and the Gaussian Process (GP) model are presented and explored in depth. These proposed methods are simulations providing an integrated virtual environment in which user can generate the inspection path planning for specific tasks and evaluate the errors and uncertainty associated with the measurement results, all without the need to perform a number of physical CMM measurements. The estimated errors and uncertainty can serve as rapid feedback for users before performing actual measurements or as a prior evaluation of the results of the CMM calibrations. The estimation of CMM geometric errors are usually described using 21 kinematic errors which consist of three positional and three rotational error functions for each of the three axes, along with three squareness errors. This assumes that the method to estimate of these kinematic errors can be generated by performing an artefact measurement such as for a hole or a ball plate in the numbers of the positions of the CMM working region and then matching the kinematic errors to the measured changes in artefact geometry. The process validation of a local, kinematic error model and a GP model has been determined with the design and analysis of CMM measurement using a ball plate as an artefact, calculating the percentage error to compare their effective results. This research project has led to the following contribution to knowledge: Mathematical model development for making effective choices regarding the local, kinematic error model and GP model is performed and formulated; this is verified by particular kinematic errors of the CMM measurements, presenting high accuracy and reliability of the error and uncertainty evaluation performance. The improvement achieved by the proposed method over the traditional approaches between the simulated datasets and actual CMM data measurements has been demonstrated. The numerical simulations with a well-designed strategy providing accurate estimates of the CMM kinematic errors using only a nominal CMM calibration with a ball plate have been validated and evaluated in both approaches. The influences of kinematic errors affected through the measurement process of the CMM on the calibration have been investigated.Royal Thai Government, Ministry of Sciences and Technology and National Institute of Metrology Thailand (NIMT)

Coordinate Measuring Machine (CMM) inspection planning and knowledge capture – formalising a black art

In manufacturing, the automated elicitation of engineering knowledge is a major challenge due to the increasing knowledge-intensive processes and systems used in industry. Capturing and formalizing engineering knowledge is a highly costly and time-consuming task. The existing literature covers little in this field, leaving unanswered the technical difficulties of capturing and representing knowledge in Coordinate Measuring Machine (CMM) inspection planning applications. This work presents the Inspection Planning and Capturing Knowledge (IPaCK) system, a novel paradigm for the automated capturing and formalising of human centred expertise in the field of CMM planning. The proposed solution is an innovative physical setup using a simple tracked hand-held probe that facilitates intuitive planning of a CMM measurement strategy as a user interacts with a real component. As the sequence is generated, in real time a motion tracking-based digital tool logs user activity throughout the task. A post processor then converts log file data into multiple formalised outputs representing the knowledge created and utilised during the CMM inspection planning task. Experienced CMM inspection planners validated IPaCK’s potential to produce knowledge representations of CMM planning strategies that were useful, relevant and accurate. A comparison of planning strategies resulted in the detection of measurement patterns; embedding both inspection planning knowledge and experience, constituting the first known implementation of automatically capturing best practice and defining benchmarks to evaluate future planning strategies. A task completion time (TCT) comparison against a conventional CMM showed that IPaCK facilitates faster measurement planning and part programming. On using the system, novice planners rated IPaCK and its knowledge representations to provide significant metacognition support to CMM planning and training. Experienced planners confirmed IPaCK’s knowledge capture capability and that the formats were industry acceptable, relevant and beneficial in inspection planning tasks. IPaCK could be at the heart of the next generation of CMM inspection planning systems; one that automatically captures and formalises inspection planning knowledge and experience in multiple outputs. This thesis presents the underpinning science and technology to realise the implementation