Integrated inpection of sculptured surface products using machine vision and a coordinate measuring machine

In modem manufacturing technology with increasing automation of manufacturing processes and operations, the need for automated measurement has become much more apparent. Computer measuring machines are one of the essential instruments for quality control and measurement of complex products, performing measurements that were previously laborious and time consuming. Inspection of sculptured surfaces can be time consuming since, for exact specification, an almost infinite number of points would be required. Automated measurement with a significant reduction of inspected points can be attempted if prior knowledge of the part shape is available. The use of a vision system can help to identify product shape and features but, unfortunately, the accuracy required is often insufficient. In this work a vision system used with a Coordinate Measuring Machine (CMM), incorporating probing, has enabled fast and accurate measurements to be obtained. The part features have been enhanced by surface marking and a simple 2-D vision system has been utilised to identify part features. In order to accurately identify all parts of the product using the 2-D vision system, a multiple image superposition method has been developed which enables 100 per cent identification of surface features. A method has been developed to generate approximate 3-D surface position from prior knowledge of the product shape. A probing strategy has been developed which selects correct probe angle for optimum accuracy and access, together with methods and software for automated CMM code generation. This has enabled accurate measurement of product features with considerable reductions in inspection time. Several strategies for the determination and assessment of feature position errors have been investigated and a method using a 3-D least squares assessment has been found to be satisfactory. A graphical representation of the product model and errors has been developed using a 3-D solid modelling CAD system. The work has used golf balls and tooling as the product example

Cyber-Physical Manufacturing Metrology Model (CPM3) - Big Data Analytics Issue

Internet of Things (IoT) is changing the world, and therefore the application of ICT (Information and Communication Technology) in manufacturing. As a paradigm based on the Internet, IoT utilizes the benefits of interrelated technologies/smart devices such as RFID (Radio Frequency Identification) and WSAN (Wireless Sensor and Actuator Networks) for the retrieval and exchange of information thus opening up new possibilities for integration of manufacturing system and its cyber representation through Cyber-Physical Manufacturing (CPM) model. On the other hand, CPM and digital manufacturing represent the key elements for implementation of Industry 4.0 and backbone for "smart factory" generation. Interconnected smart devices generate huge databases (big data), so that Cloud computing becomes indispensable tool to support the CPM. In addition, CPM has an extremely expressed requirement for better control, monitoring and data management. Limitations still exist in storages, networks and computers, as well as in the tools for complex data analysis, detection of its structure and retrieval of useful information. Products, resources, and processes within smart factory are realized and controlled through CPM model. In this context, our recent research efforts in the field of quality control and manufacturing metrology are directed to the development of framework for Cyber-Physical Manufacturing Metrology Model (CPM3). CPM3 framework will be based on: 1) integration of digital product metrology information obtained from big data using BDA (big data analytics) through metrology features recognition, and 2) generation of global/local inspection plan for CMM (Coordinate Measuring Machine) from extracted information. This paper will present recent results of our research on CPM3 - big data analytics issue

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

digital factory technologies for robotic automation and enhanced manufacturing cell design

The fourth industrial revolution is characterised by the increased use of digital tools, allowing for the virtual representation of a real production environment at different levels, from the entire production plant to a single machine or a specific process or operation. In this framework, Digital Factory technologies, based on the employment of digital modelling and simulation tools, can be used for short-term analysis and validation of production control strategies or for medium term production planning or production system design/redesign. In this research work, a Digital Factory methodology is proposed to support the enhancement of an existing manufacturing cell for the fabrication of aircraft engine turbine vanes via robotic automation of its deburring station. To configure and verify the correct layout of the upgraded manufacturing cell with the aim to increase its performance in terms of resource utilization and throughput time, 3D Motion Simulation and Discrete Event Simulation are jointly employed for the modeling and simulation of different cell settings for proper layout configuration, safe motion planning and resource utilization improvement. Validation of the simulation model is carried out by collecting actual data from the physical reconfigured manufacturing cell and comparing these data to the model forecast with the aim to adapt the digital model accordingly to closely represent the physical manufacturing system

Automated calibration of multi-sensor optical shape measurement system

A multi-sensor optical shape measurement system (SMS) based on the fringe projection method and temporal phase unwrapping has recently been commercialised as a result of its easy implementation, computer control using a spatial light modulator, and fast full-field measurement. The main advantage of a multi-sensor SMS is the ability to make measurements for 360° coverage without the requirement for mounting the measured component on translation and/or rotation stages. However, for greater acceptance in industry, issues relating to a user-friendly calibration of the multi-sensor SMS in an industrial environment for presentation of the measured data in a single coordinate system need to be addressed. The calibration of multi-sensor SMSs typically requires a calibration artefact, which consequently leads to significant user input for the processing of calibration data, in order to obtain the respective sensor's optimal imaging geometry parameters. The imaging geometry parameters provide a mapping from the acquired shape data to real world Cartesian coordinates. However, the process of obtaining optimal sensor imaging geometry parameters (which involves a nonlinear numerical optimization process known as bundle adjustment), requires labelling regions within each point cloud as belonging to known features of the calibration artefact. This thesis describes an automated calibration procedure which ensures that calibration data is processed through automated feature detection of the calibration artefact, artefact pose estimation, automated control point selection, and finally bundle adjustment itself. [Continues.

The evaluation of a novel haptic machining VR-based process planning system using an original process planning usability method

This thesis provides an original piece of work and contribution to knowledge by creating a new process planning system; Haptic Aided Process Planning (HAPP). This system is based on the combination of haptics and virtual reality (VR). HAPP creates a simulative machining environment where Process plans are automatically generated from the real time logging of a user’s interaction. Further, through the application of a novel usability test methodology, a deeper study of how this approach compares to conventional process planning was undertaken. An abductive research approach was selected and an iterative and incremental development methodology chosen. Three development cycles were undertaken with evaluation studies carried out at the end of each. Each study, the pre-pilot, pilot and industrial, identified progressive refinements to both the usability of HAPP and the usability evaluation method itself. HAPP provided process planners with an environment similar to which they are already familiar. Visual images were used to represent tools and material whilst a haptic interface enabled their movement and positioning by an operator in a manner comparable to their native setting. In this way an intuitive interface was developed that allowed users to plan the machining of parts consisting of features that can be machined on a pillar drill, 21/2D axis milling machine or centre lathe. The planning activities included single or multiple set ups, fixturing and sequencing of cutting operations. The logged information was parsed and output to a process plan including route sheets, operation sheets, tool lists and costing information, in a human readable format. The system evaluation revealed that HAPP, from an expert planners perspective is perceived to be 70% more satisfying to use, 66% more efficient in completing process plans, primarily due to the reduced cognitive load, is more effective producing a higher quality output of information and is 20% more learnable than a traditional process planning approach