Automated freeform assembly of threaded fasteners

Over the past two decades, a major part of the manufacturing and assembly market has been driven by its customer requirements. Increasing customer demand for personalised products create the demand for smaller batch sizes, shorter production times, lower costs, and the flexibility to produce families of products - or different parts - with the same sets of equipment. Consequently, manufacturing companies have deployed various automation systems and production strategies to improve their resource efficiency and move towards right-first-time production. However, many of these automated systems, which are involved with robot-based, repeatable assembly automation, require component- specific fixtures for accurate positioning and extensive robot programming, to achieve flexibility in their production. Threaded fastening operations are widely used in assembly. In high-volume production, the fastening processes are commonly automated using jigs, fixtures, and semi-automated tools. This form of automation delivers reliable assembly results at the expense of flexibility and requires component variability to be adequately controlled. On the other hand, in low- volume, high- value manufacturing, fastening processes are typically carried out manually by skilled workers. This research is aimed at addressing the aforementioned issues by developing a freeform automated threaded fastener assembly system that uses 3D visual guidance. The proof-of-concept system developed focuses on picking up fasteners from clutter, identifying a hole feature in an imprecisely positioned target component and carry out torque-controlled fastening. This approach has achieved flexibility and adaptability without the use of dedicated fixtures and robot programming. This research also investigates and evaluates different 3D imaging technology to identify the suitable technology required for fastener assembly in a non-structured industrial environment. The proposed solution utilises the commercially available technologies to enhance the precision and speed of identification of components for assembly processes, thereby improving and validating the possibility of reliably implementing this solution for industrial applications. As a part of this research, a number of novel algorithms are developed to robustly identify assembly components located in a random environment by enhancing the existing methods and technologies within the domain of the fastening processes. A bolt identification algorithm was developed to identify bolts located in a random clutter by enhancing the existing surface-based matching algorithm. A novel hole feature identification algorithm was developed to detect threaded holes and identify its size and location in 3D. The developed bolt and feature identification algorithms are robust and has sub-millimetre accuracy required to perform successful fastener assembly in industrial conditions. In addition, the processing time required for these identification algorithms - to identify and localise bolts and hole features - is less than a second, thereby increasing the speed of fastener assembly

Novel control approaches for the next generation computer numerical control (CNC) system for hybrid micro-machines

It is well-recognised that micro-machining is a key enabling technology for manufacturing high value-added 3D micro-products, such as optics, moulds/dies and biomedical implants etc. These products are usually made of a wide range of engineering materials and possess complex freeform surfaces with tight tolerance on form accuracy and surface finish.In recent years, hybrid micro-machining technology has been developed to integrate several machining processes on one platform to tackle the manufacturing challenges for the aforementioned micro-products. However, the complexity of system integration and ever increasing demand for further enhanced productivity impose great challenges on current CNC systems. This thesis develops, implements and evaluates three novel control approaches to overcome the identified three major challenges, i.e. system integration, parametric interpolation and toolpath smoothing. These new control approaches provide solid foundation for the development of next generation CNC system for hybrid micro-machines.There is a growing trend for hybrid micro-machines to integrate more functional modules. Machine developers tend to choose modules from different vendors to satisfy the performance and cost requirements. However, those modules often possess proprietary hardware and software interfaces and the lack of plug-and-play solutions lead to tremendous difficulty in system integration. This thesis proposes a novel three-layer control architecture with component-based approach for system integration. The interaction of hardware is encapsulated into software components, while the data flow among different components is standardised. This approach therefore can significantly enhance the system flexibility. It has been successfully verified through the integration of a six-axis hybrid micro-machine. Parametric curves have been proven to be the optimal toolpath representation method for machining 3D micro-products with freeform surfaces, as they can eliminate the high-frequency fluctuation of feedrate and acceleration caused by the discontinuity in the first derivatives along linear or circular segmented toolpath. The interpolation for parametric curves is essentially an optimization problem, which is extremely difficult to get the time-optimal solution. This thesis develops a novel real-time interpolator for parametric curves (RTIPC), which provides a near time-optimal solution. It limits the machine dynamics (axial velocities, axial accelerations and jerk) and contour error through feedrate lookahead and acceleration lookahead operations. Experiments show that the RTIPC can simplify the coding significantly, and achieve up to ten times productivity than the industry standard linear interpolator. Furthermore, it is as efficient as the state-of-the-art Position-Velocity-Time (PVT) interpolator, while achieving much smoother motion profiles.Despite the fact that parametric curves have huge advantage in toolpath continuity, linear segmented toolpath is still dominantly used on the factory floor due to its straightforward coding and excellent compatibility with various CNC systems. This thesis presents a new real-time global toolpath smoothing algorithm, which bridges the gap in toolpath representation for CNC systems. This approach uses a cubic B-spline to approximate a sequence of linear segments. The approximation deviation is controlled by inserting and moving new control points on the control polygon. Experiments show that the proposed approach can increase the productivity by more than three times than the standard toolpath traversing algorithm, and 40% than the state-of-the-art corner blending algorithm, while achieving excellent surface finish.Finally, some further improvements for CNC systems, such as adaptive cutting force control and on-line machining parameters adjustment with metrology, are discussed in the future work section.It is well-recognised that micro-machining is a key enabling technology for manufacturing high value-added 3D micro-products, such as optics, moulds/dies and biomedical implants etc. These products are usually made of a wide range of engineering materials and possess complex freeform surfaces with tight tolerance on form accuracy and surface finish.In recent years, hybrid micro-machining technology has been developed to integrate several machining processes on one platform to tackle the manufacturing challenges for the aforementioned micro-products. However, the complexity of system integration and ever increasing demand for further enhanced productivity impose great challenges on current CNC systems. This thesis develops, implements and evaluates three novel control approaches to overcome the identified three major challenges, i.e. system integration, parametric interpolation and toolpath smoothing. These new control approaches provide solid foundation for the development of next generation CNC system for hybrid micro-machines.There is a growing trend for hybrid micro-machines to integrate more functional modules. Machine developers tend to choose modules from different vendors to satisfy the performance and cost requirements. However, those modules often possess proprietary hardware and software interfaces and the lack of plug-and-play solutions lead to tremendous difficulty in system integration. This thesis proposes a novel three-layer control architecture with component-based approach for system integration. The interaction of hardware is encapsulated into software components, while the data flow among different components is standardised. This approach therefore can significantly enhance the system flexibility. It has been successfully verified through the integration of a six-axis hybrid micro-machine. Parametric curves have been proven to be the optimal toolpath representation method for machining 3D micro-products with freeform surfaces, as they can eliminate the high-frequency fluctuation of feedrate and acceleration caused by the discontinuity in the first derivatives along linear or circular segmented toolpath. The interpolation for parametric curves is essentially an optimization problem, which is extremely difficult to get the time-optimal solution. This thesis develops a novel real-time interpolator for parametric curves (RTIPC), which provides a near time-optimal solution. It limits the machine dynamics (axial velocities, axial accelerations and jerk) and contour error through feedrate lookahead and acceleration lookahead operations. Experiments show that the RTIPC can simplify the coding significantly, and achieve up to ten times productivity than the industry standard linear interpolator. Furthermore, it is as efficient as the state-of-the-art Position-Velocity-Time (PVT) interpolator, while achieving much smoother motion profiles.Despite the fact that parametric curves have huge advantage in toolpath continuity, linear segmented toolpath is still dominantly used on the factory floor due to its straightforward coding and excellent compatibility with various CNC systems. This thesis presents a new real-time global toolpath smoothing algorithm, which bridges the gap in toolpath representation for CNC systems. This approach uses a cubic B-spline to approximate a sequence of linear segments. The approximation deviation is controlled by inserting and moving new control points on the control polygon. Experiments show that the proposed approach can increase the productivity by more than three times than the standard toolpath traversing algorithm, and 40% than the state-of-the-art corner blending algorithm, while achieving excellent surface finish.Finally, some further improvements for CNC systems, such as adaptive cutting force control and on-line machining parameters adjustment with metrology, are discussed in the future work section

Open architecture control system for a modular reconfigurable machine tool.

M.Sc.Eng. University of KwaZulu-Natal, Durban 2013.The present day manufacturing environment has forced manufacturing systems to be flexible and adaptable to be able to match the product demands and frequent introduction of new products and technologies. This research forms part of a greater research initiative that looks at the development of the reconfigurable manufacturing paradigm. Previous research has shown that the lack of development of a Modular Reconfigurable Manufacturing Tools (MRMT) and Open Architecture Control System (OACS) is currently a key limiting factor to the establishment of Reconfigurable Manufacturing Systems (RMS), which has been the primary motivation for this research. Open Architecture (OA) systems aim to bring the ideas of RMS to control systems for machining systems. An OA system incorporates vendor neutrality, portability, extendibility, scalability and modularity. The research has proposed, designed and developed a novel solution that incorporates these core principles allowing the system to be flexible in mechanical and control architectures. In doing so, the system can be reconfigured at any time to match the specific manufacturing functionality required at that time thereby prolonging the lifecycle of the machine via multiple reconfigurations over time, in addition to decreasing the cost of system modifications due to a well-defined modular system. The reconfiguration and machining variance is achieved by the introduction of mechanical and control modules that extend the Degrees of Freedom (DOF’s) available to the system. The OACS has been developed as a modular solution that links closely to the existing mechanical modularity on the RMT to maximize the reconfigurability of the system. The aim was to create a one to one link between mechanical and electronic hardware and the software system. This has been achieved by the addition of microcontroller based distributed modules which acts as the interface between the electro-mechanical machine axes via hardwired signals and the host PC via the CAN bus communication interface. The distributed modules have been developed on different microcontrollers, which have successfully demonstrated the openness and customizability of the system. On the host PC, the user is presented with a GUI that allows the user to configure the control system based on the MRMT physical configuration. The underlying software algorithms such as, text Interpretation, linear interpolation, PID or PI controllers and determination of kinematic viability are part of the OACS and are used at run time for machine operation. The machining and control performance of the system is evaluated on the previously developed MRMT. The performance evaluation also covers the analysis of the reconfigurability and scalability of the system. The research is concluded with a presentation based on conclusions drawn from the research covering the challenges, limitations and problems that OA and RMS can face before MRMT become readily available for industry

Research and development of a reconfigurable robotic end-effector for machining and part handling.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF

dsPIC-based signal processing techniques and an IT-enabled distributed system for intelligent process monitoring and management

dsPIC Technology employs a powerful 16-bit architecture into single-chip devices that seamlessly integrate the diverse attributes of a microcontroller with the computation and throughput capabilities of a digital signal processor in a single core. The key element of this dissertation is to explore how dsPIC has influenced the applicability of research in e-Monitoring systems. At the same time dsPIC has offered the opportunity to develop methodologies which were previously not even considered. The dsPIC devices are used, in this research, for front end data acquisition, signal processing and communication tools within a proposed monitoring architecture. In this work, novel digital signal processing (DSP) techniques are developed for the monitoring of an example application, namely the challenging one of tool breakage in milling operations. The monitoring regime is implemented on the dsPIC and its capabilities for real-time frequency analysis using overlap FFT and Multiband IIR Filters with dynamic coefficient selection techniques is explored. The developed systems are tested for various cutting conditions using existing machine tool signals and tool breakage is detected reliably in real-time. In attempting to enhance the accuracy of tool monitoring it is evident that the depth of cut (DOC) is an important parameter and achieving its on-line monitoring provides valuable information for condition monitoring. A systematic approach is adopted for the analysis and selection of ultrasonic sensors for distance measurement. A DOC monitoring system is developed using the dsPIC as the data acquisition and processing core. To achieve reliable results, various DSP algorithms are developed, implemented and verified for their effectiveness. The system integration stage combines the above elements for robust and reliable decision making and provides communication of the generated information to support management function using the internet and GSM connectivity. This integration enables an enhanced process management system which is capable of identifying all significant events, for offline analysis and subsequent diagnosis in addition to the real time diagnostic mode

An autonomous self-reconfigurable modular robotic system with optimised docking connectors

Includes bibliographical references.Self-Reconfigurable Modular Robots are robotic systems consisting of a number of self-contained modules that can autonomously interconnect in different positions and orientations thereby varying the shape and size of the overall modular robot. This ground breaking capability is what in theory, makes self-reconfigurable modular robots more suitable for use in the navigation of unknown or unstructured environments. Here, they are required to reconfigure into different forms so as to optimise their navigation capabilities, a feat that is rendered impossible in conventional specialised robots that lack reconfiguration capabilities. However, the frequent development and use of self-reconfigurable modular robots in everyday robotic navigation applications is significantly hampered by the increased difficulty and overall cost of production of constituent robotic modules. One major contributor to this is the difficulty of designing suitably robust and reliable docking mechanisms between individual robotic modules. Such mechanisms are required to be mechanically stable involving a robust coupling mechanism, and to facilitate reliable inter-module power sharing and communication. This dissertation therefore proposes that the design and development of a functional low cost self-reconfigurable modular robot is indeed achievable by optimising and simplifying the design of a robust and reliable autonomous docking mechanism. In this study, we design and develop such a modular robot, whose constituent robotic modules are fitted with specialised docking connectors that utilise an optimised docking mechanism. This modular robot, its robotic modules and their connectors are then thoroughly tested for accuracy in mobility, electrical and structural stability, inter-module communication and power transfer, self-assembly, self-reconfiguration and self-healing, among others. The outcome of these testing procedures proved that it is indeed possible to optimise the docking mechanisms of self-reconfigurable modular robots, thereby enabling the modular robot to more easily exhibit efficient self-reconfiguration, self-assembly and self-healing behaviours. This study however showed that the type, shape, functionality and structure of electrical contacts used within the docking connectors for inter-module signal transfer and communication play a major role in enabling efficient self-assembly, self-reconfiguration and self-healing behaviours. Smooth spring loaded metallic electrical contacts incorporated into the docking connector design are recommended. This study also highlights the importance of closed loop control in the locomotion of constituent robotic modules, especially prior to docking. The open loop controlled locomotion optimisations used in this project were not as accurate as was initially expected, making self-assembly rather inaccurate and inconsistent. It is hoped that the outcomes of this research will serve to improve the docking mechanisms of self-reconfigurable modular robots thereby improving their functionality and pave the way for future large scale use of these robots in real world applications

Thermal Performance of a Multi-Axis Smoothing Cell

Multi Axis Robots have traditionally been used in industry for pick and place, de-burring, and welding operations. Increasing technological advances have broadened their application and today robots are increasingly being used for higher precision applications in the medical and nuclear sectors. In order to use robots in such roles it is important to understand their performance. Thermal effects in machine tools are acknowledged to account for up to 70% of all errors (Bryan J. , 1990) and therefore need to be considered. This research investigates thermal influences on the accuracy and repeatability of a six degree of freedom robotic arm, which forms an integral part of a smoothing cell. The cell forms part of a process chain currently being developed for the processing of high accuracy freeform surfaces, intended for use on the next generation of ground based telescopes. The robot studied was a FANUC 710i/50 with a lapping spindle the end effector. The robot geometric motions were characterised and the structure was thermally mapped at the latter velocity. The thermal mapping identified the key areas of the robot structure requiring more detailed analysis. Further investigation looked into thermal variations in conjunction with geometric measurements in order to characterise the robot thermal performance. Results showed thermal variations of up to 13ºC over a period of six hours, these produced errors of up to 100μm over the 1300mm working stroke slow. Thermal modelling carried out predicted geometric variation of 70μm to 122μm for thermal variations up to 13ºC over a period of six hours. The modelling was 50% to 75% efficient in predicting thermal error magnitudes in the X axis. With the geometric and modelling data a recommendation for offline compensation would enable significant improvement in the robots positioning capability to be achieved

Smart machining system platform for CNC milling with the integration of a power sensor and cutting model

Novel techniques and strategies are investigated for dynamically measuring the process capability of machine tools and using this information for Smart Machine System (SMS) research. Several aspects of the system are explored including system integration, data acquisition, force and power model calibration, feedrate scheduling and tool condition monitoring. A key aspect of a SMS is its ability to provide synchronization between process measurements and model estimates. It permits real time feedback regarding the current machine tool process. This information can be used to accurately determine and keep track of model coefficients for the actual tooling and materials in use, providing both a continued improvement in model accuracy as well as a way to monitor the health of the machine and the machining process. A cutting power model is applied based on a linear tangential force model with edge effect. The robustness of the model is verified through experiments with a wide variety of cutting conditions. Results show good agreement between measured and estimated power. A test platform has been implemented for performing research on Smart Machine Systems. It uses a commercially available OAC from MDSI, geometric modeling software from Predator along with a number of modules developed at UNH. Test cases illustrate how models and sensors can be combined to select machining conditions that will produce a good part on the first try. On-line calibration allows the SMS to fine tune model coefficients, which can then be used to improve production efficiency as the machine learns its own capabilities. With force measurements, the force model can be calibrated and resultant force predictions can be performed. A feedrate selection planner has been created to choose the fastest possible feedrates subject to constraints which are related to part quality, tool health and machine tool capabilities. Monitoring tangential model coefficients is shown to be more useful than monitoring power ratio for tool condition monitoring. As the model coefficients are independent of the cutting geometry, their changes are more promising, in that KTC will increase with edge chipping and breakage, while KTE will increase as the flank wearland expands

Wireless rotational process monitoring system

The manufacturing industry is constantly looking for ways to reduce production costs and at the same time to increase productivity. Automation of common manufacturing operations is one of these methods. By automating common manufacturing operations; various machines, robots, control systems and information technologies are used to reduce the overall human input requirement (mental and physical). Recent advances in technology have made it possible to now also automate (or facilitate) the maintenance requirement of these machines and tools. Modern tools and machines, which can estimate when it will fail or when failure is imminent have obvious advantages for predictive maintenance purposes. Another function of this technology is to determine how efficiently a tool or machine operates, or what the quality of the produced goods is. Predictive maintenance can decrease manufacturing plant or machine down times – which have a positive effect on cost-savings – has gained considerable importance over the last two decades