Development of an integrated robotic polishing system

This thesis presents research carried out as part of a project undertaken in fulfilment of the requirements of Loughborough University for the award of Philosophical Doctorate. The main focus of this research is to investigate and develop an appropriate level of automation to the existing manual finishing operations of small metallic components to achieve required surface quality and to remove superficial defects. In the manufacturing industries, polishing processes play a vital role in the development of high precision products, to give a desired surface finish, remove defects, break sharp edges, extend the working life cycle, and meet mechanical specification. The polishing operation is generally done at the final stage of the manufacturing process and can represent up to a third of the production time. Despite the growth automated technology in industry, polishing processes are still mainly carried out manually, due to the complexity and constraints of the process. Manual polishing involves a highly qualified worker polishing the workpiece by hand. These processes are very labour intensive, highly skill dependent, costly, error-prone, environmentally hazardous due to abrasive dust, and - in some cases - inefficient with long process times. In addition, the quality of the finishing is dependent on the training, experience, fatigue, physical ability, and expertise of the operator. Therefore, industries are seeking alternative solutions to be implemented within their current processes. These solutions are mainly aimed at replacing the human operator to improve the health and safety of their workforce and improve their competitiveness. Some automated solutions have already been proposed to assist or replace manual polishing processes. These solutions provide limited capabilities for specific processes or components, and a lack of flexibility and dexterity. One of the reasons for their lack of success is identified as neglecting the study and implementing the manual operations. This research initially hypothesised that for an effective development, an automated polishing system should be designed based on the manual polishing operations. Therefore, a successful implementation of an automated polishing system requires a thorough understanding of the polishing process and their operational parameters. This study began by collaborating with an industrial polishing company. The research was focused on polishing complex small components, similar to the parts typically used in the aerospace industry. The high level business processes of the polishing company were capture through several visits to the site. The low level operational parameters and the understanding of the manual operations were also captured through development of a devices that was used by the expert operators. A number of sensors were embedded to the device to facilitate recording the manual operations. For instance, the device captured the force applied by the operator (avg. 10 N) and the cycle time (e.g. 1 pass every 5 sec.). The capture data was then interpreted to manual techniques and polishing approaches that were used in developing a proof-of-concept Integrated Robotic Polishing System (IRPS). The IRPS was tested successfully through several laboratory based experiments by expert operators. The experiment results proved the capability of the proposed system in polishing a variety of part profiles, without pre-existing geometrical information about the parts. One of the main contributions made by this research is to propose a novel approach for automated polishing operations. The development of an integrated robotic polishing system, based on the research findings, uses a set of smart sensors and a force-position-by-increment control algorithm, and transpose the way that skilled workers carry out polishing processes

Prediction of tool forces in manual grinding using consumer-grade sensors and machine learning

Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development

A New Dynamical Test Bench for Multi-Axial Loading of Angle Grinders

Automated testing with test benches plays a major role in the development of power tools such as angle grinders. Previous test benches for testing the drive train of an angle grinder replace the load from grinding a workpiece by dynamometer or servo motor in rotary direction and by linear motors in radial and axial direction. These can only apply forces up to 10 Hz and thus no speed-dependent force components. The aim of this paper is to develop a test bench for dynamic mechanical loading of the drive train of an angle grinder in the rotational, radial and axial axes up to 200 Hz, which corresponds to the maximum speed of an angle grinder. For this purpose, the modelling of the force application on the test bench and the resulting mechanical design is presented. In addition, the generation of test cases from measurement data of manual tests and the verification of the test bench are presented. Subsequently, a case study is presented to investigate the load pattern on the test bench with multi-axial load compared to pure torque loading on the same test bench and manual tests. It can be seen that the load pattern of the multi-axial load is qualitatively similar that of the load pattern from the manual test. Through using the developed test bench, it will be possible to investigate load patterns as well as wear or vibration of the drive train of an angle grinder

The machinability of rapidly solidified aluminium alloy for optical mould inserts

Ultra-high precision machining is a material removing process under the nanotechnology regime whereby the highest dimensional accuracies are attained. Critical components for optical devices and optical measuring systems are mainly produced through ultra-high precision machining. Their mass production is usually implemented by utilising optical moulds. Aluminium alloys have proven to be advantageous and very commonly used in the photonics industry for moulds. This ever-increasing use and demand within optics have led to the development of newly modified grades of aluminium alloys produced by rapid solidification in the foundry process. The newer grades are characterised by finer microstructures and improved mechanical and physical properties. The main inconvenience in their usage currently lies in their very limited machining database. This research investigates the machinability of rapidly solidified aluminium, RSA 905, under varying cutting conditions in single point diamond turning. The machining parameters varied were cutting speed, feed rate and depth of cut. The resulting surface roughness of the workpiece and wear of the diamond tool were measured at various intervals. Acoustic emissions and cutting force were also monitored during machining. The results were statistically analysed and accurate predictive models were developed. Generally, very low tool wear, within 3 to 5 μm, and very low surface roughness, within 3 to 8 nm, was obtained. Acoustic emissions recorded were in the range of 0.06 to 0.13 V and cutting forces were in the range of 0.08 to 0.94 N. The trends of the monitored acoustic emissions and cutting force showed to have a linked representation of the tool wear and surface roughness results. Contour maps were generated to identify zones where the cutting parameters produced the best results. In addition, a range of machining parameters were presented for optimum quality where surface roughness and tool wear can be minimised. As the machining is of a nanometric scale, a molecular dynamics approach was applied to investigate the underlying mechanisms at atom level. The nanomachining simulations were found to have a correlation to the actual machining results and microstructural nature of the alloy. This research proves that rapidly solidified aluminium is a superior alternative to traditional aluminium alloys and provides a good reference with room for flexibility that machinists can apply when using rapidly solidified aluminium alloys. Efficiency could be improved by reducing the required machining interruption through effective monitoring and performance could be improved by maintaining quality and extending tool life through parameter selection

Abrasive machining with MQSL

Grinding and polishing of engineered components are critical aspects of the precision manufacturing of high performance, quality assured products. Elevated process temperatures, however, are a common and for the most part undesirable feature of the grinding process. High process temperatures increase the likelihood of microstructural change within the immediate subsurface layer and are detrimental to the strength and performance of the manufactured products. Increasing processing costs and tighter environmental legislation are encouraging industry to seek innovative fluid application techniques as significant savings in production can be achieved. In this context, and with sponsorship from three industrial partners, namely; Fives Cinetic, Fuchs Lubricants plc and Southside Thermal Sciences Ltd, and also from the Engineering and Physical Science Research Council (EPSRC), this research aimed to develop an understanding of Minimum Quantity Solid Lubrication (MQSL) as a method for abrasive machining, with particular reference to the control of surface temperatures. Improving the lubricity of Minimum Quantity Lubrication (MQL) fluids reduces the frictional source of process heat and controls the finish surface temperature. The application of effective solid lubricants is known as Minimum Quantity Solid Lubrication (MQSL). Molybdenum Disulphide (MoS2), Calcium Fluoride (CaF2), and hexagonal Boron Nitride (hBN) were compared against a semi-synthetic water soluble machining fluid (Fuchs EcoCool). A series of Taguchi factorial experimental trials assessed their performances through ANOVA (ANalysis Of VAriance) statistical method. The hBN produced the lowest grinding temperatures of the solid lubricants tested, although they still remained higher than those achieved using the EcoCool control. The reduction of the machining fluid enabled a Charged Coupled Device (CCD) sensor to be fitted into the grinding machine. The recorded movement in the emitted spectrum from the grinding chips was compared to experimental and modelled process temperatures. This showed that the wavelengths of the chip light correlated to the temperature of the finish grinding surface. This greatly contributed to determining the feasibility of constructing a non-destructive, non-invasive, thermally-adaptive control system for controlling grinding surface temperatures.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Geometrical Error Analysis and Correction in Robotic Grinding

The use of robots in industrial applications has been widespread in the manufacturing tasks such as welding, finishing, polishing and grinding. Most robotic grinding focus on the surface finish rather than accuracy and precision. Therefore, it is important to advance the technology of robotic machining so that more practical and competitive systems can be developed for components that have accuracy and precision requirement. This thesis focuses on improving the level of accuracy in robotic grinding which is a significant challenge in robotic applications because of the kinematic accuracy of the robot movement which is much more complex than normal CNC machine tools. Therefore, aiming to improve the robot accuracy, this work provides a novel method to define the geometrical error by using the cutting tool as a probe whilst using Acoustic Emission monitoring to modify robot commands and to detect surfaces of the workpiece. The work also includes an applicable mathematical model for compensating machining errors in relation to its geometrical position as well as applying an optimum grinding method to motivate the need of eliminating the residual error when performing abrasive grinding using the robot. The work has demonstrated an improved machining precision level from 50µm to 30µm which is controlled by considering the process influential variables, such as depth of cut, wheel speed, feed speed, dressing condition and system time constant. The recorded data and associated error reduction provide a significant evidence to support the viability of implementing a robotic system for various grinding applications, combining more quality and critical surface finishing practices, and an increased focus on the size and form of generated components. This method could provide more flexibility to help designers and manufacturers to control the final accuracy for machining a product using a robot system

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services

Energy efficient machine tools

The growing global energy demand from industry results in significant ecological and economical costs. Aiming to decrease the impact of machining operations, an increasing number of research activities and publications regarding energy efficient machine tools and machining processes can be found in the literature. This keynote paper provides an overview of current machine- and process-related measures to improve the energy efficiency of metal cutting machine tools. Based on an analysis of the energy requirements of machine tool components, design measures to reduce the energy demand of main and support units are introduced. Next, methods for an energy efficient operation of machine tools are reviewed. Furthermore, latest developments and already available energy efficiency options in the machine tool industry are discussed. The paper concludes with recommendations and future research questions for more energy efficient machine tools

IN-SITU CHARACTERIZATION OF SURFACE QUALITY IN γ-TiAl AEROSPACE ALLOY MACHINING

The functional performance of critical aerospace components such as low-pressure turbine blades is highly dependent on both the material property and machining induced surface integrity. Many resources have been invested in developing novel metallic, ceramic, and composite materials, such as gamma-titanium aluminide (γ-TiAl), capable of improved product and process performance. However, while γ-TiAl is known for its excellent performance in high-temperature operating environments, it lacks the manufacturing science necessary to process them efficiently under manufacturing-specific thermomechanical regimes. Current finish machining efforts have resulted in poor surface integrity of the machined component with defects such as surface cracks, deformed lamellae, and strain hardening. This study adopted a novel in-situ high-speed characterization testbed to investigate the finish machining of titanium aluminide alloys under a dry cutting condition to address these challenges. The research findings provided insight into material response, good cutting parameter boundaries, process physics, crack initiation, and crack propagation mechanism. The workpiece sub-surface deformations were observed using a high-speed camera and optical microscope setup, providing insights into chip formation and surface morphology. Post-mortem analysis of the surface cracking modes and fracture depths estimation were recorded with the use of an upright microscope and scanning white light interferometry, In addition, a non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN) was developed to achieve a real-time total volume crack monitoring capability. This approach showed good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, thereby establishing the significant potential for real-time quality monitoring in manufacturing processes. The findings from this present study set the tone for creating a digital process twin (DPT) framework capable of obtaining more aggressive yet reliable manufacturing parameters and monitoring techniques for processing turbine alloys and improving industry manufacturing performance and energy efficiency

Manufacturing at double the speed

The speed of manufacturing processes today depends on a trade-off between the physical processes of production, the wider system that allows these processes to operate and the co-ordination of a supply chain in the pursuit of meeting customer needs. Could the speed of this activity be doubled? This paper explores this hypothetical question, starting with examination of a diverse set of case studies spanning the activities of manufacturing. This reveals that the constraints on increasing manufacturing speed have some common themes, and several of these are examined in more detail, to identify absolute limits to performance. The physical processes of production are constrained by factors such as machine stiffness, actuator acceleration, heat transfer and the delivery of fluids, and for each of these, a simplified model is used to analyse the gap between current and limiting performance. The wider systems of production require the co-ordination of resources and push at the limits of human biophysical and cognitive limits. Evidence about these is explored and related to current practice. Out of this discussion, five promising innovations are explored to show examples of how manufacturing speed is increasing ? with line arrays of point actuators, parallel tools, tailored application of precision, hybridisation and task taxonomies. The paper addresses a broad question which could be pursued by a wider community and in greater depth, but even this first examination suggests the possibility of unanticipated innovations in current manufacturing practices