Analysis of the Machining Process of Inconel 718 Parts Manufactured by Laser Metal Deposition

Laser metal deposition (LMD) is an additive manufacturing process that allows the manufacturing of near-net-shape products. This could mean significant savings in terms of materials and costs in the manufacturing of high-performance components for the aeronautical industry. In this work, an analysis of how the LMD processing of alloy 718 affects the final machining has been carried out. For this purpose, a comparative study has been done by means of the monitoring of the end milling process of a part manufactured by LMD and a rough-milled part from forged material. Differences between process outputs such as chip morphology and cutting forces were studied. Material characteristics such as microstructure, hardness and mechanical properties were also analyzed.This research was funded by European Commission grant number 723440 (PARADDISE project), which is an initiative of the Photonics and Factories of the Future Public Private Partnership, and by the Vice-Counselor of Technology, Innovation and Competitiveness of the Basque Government grant number KK-2018/00115 (ADDISEND project) and grant number KK-2019/00004 (PROCODA project)

Investigation into the effect of fixturing systems on the design of condition monitoring for machining operations

The global market competition has drawn the manufacturer’s attention on automated manufacturing processes using condition monitoring systems. These systems have been used for improving product quality, eliminating inspection, and enhancing manufacturing productivity. Fixtures are essential devices in machining processes to hold the tool or workpiece, hence they are influenced directly by the stability of the cutting tool. Therefore, tool and fixturing faults play an important part in the inaccuracy of the machining processes causing deterioration of surface roughness. For the above mentioned reasons, and the limited work in this domain, this thesis develops an experimental investigation to evaluate the effect of fixturing quality on the design of condition monitoring systems. The proposed monitoring system implements multisensors and signal processing methods able to analyse the sensory information and make an appropriate decision. Therefore, several sensors namely force, vibration, acoustic emission, eddy current, power, strain and sound, are combined with a newly suggested approach, named Taylor’s Equation Induced Pattern (TIP), and neural networks to detect tool wear and tool breakage. It also evaluates the monitoring system to provide valuable data to show the effect of fixturing quality. Surface roughness of the workpiece has been measured and compared with the sensitivity of the monitoring system, which reflects the state of tool and fixturing conditions

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

Alternative experimental methods for machine tool dynamics identification: A review

An accurate machine dynamic characterization is essential to properly describe the dynamic response of the machine or predict its cutting stability. However, it has been demonstrated that current conventional dynamic characterization methods are often not reliable enough to be used as valuable input data. For this reason, alternative experimental methods to conventional dynamic characterization methods have been developed to increase the quality of the obtained data. These methods consider additional effects which influence the dynamic behavior of the machine and cannot be captured by standard methods. In this work, a review of the different machine tool dynamic identification methods is done, remarking the advantages and drawbacks of each method.The present work has been partially supported by the EU Horizon 2020 InterQ project (958357/H2020-EU.2.1.5.1.) and the CDTI CERVERA programme MIRAGED project (EXP-00,137,312/CER-20191001)

Online mjerenje glatkoće površine drva

The latest progress in the field of optics and microelectronics resulted in the development of new generation vision systems capable of scanning surface topography with very high sampling frequencies. The blue color of illuminating light as well as novel systems for controlling ultra-thin laser line thickness allows the measurement of the porous surface of wood with a triangulation method. Three alternative sensors were tested here in order to verify their suitability for the determination of surface topography in the industrial environment. The scanning head was installed at the exit zone of the four-side profiling moulder and was set to scrutinize the wood surface shape line-by-line, immediately after profiling. The sensor was also tested for automatic detection of surface defects appearing on the elements after sanding, wetting and painting with various finishing products. The set of pilot test results is presented, together with an original algorithm for real-time surface defects detection.Najnoviji napredak u području optike i mikroelektronike rezultirao je novom generacijom skenera koji mogu skenirati topografiju površine vrlo visokom frekvencijom uzorkovanja. Svjetlost plave boje, kao i novi sustav za kontrolu vrlo tanke laserske zrake omogućuju mjerenje porozne površine drva metodom triangulacije. Testirana su tri alternativna senzora kako bi se potvrdila njihova prikladnost za određivanje topografije površine u industrijskim uvjetima. Glava za skeniranje postavljena je na izlazu četverostranoga profilnoga glodala kako bi se odmah nakon profiliranja pomno linijski skenirala površina drva. Senzor je također testiran za automatsko otkrivanje površinskih grešaka na elementima nakon brušenja, vlaženja i premazivanja različitim premaznim materijalima. Predstavljen je set rezultata pilot-ispitivanja, zajedno s originalnim algoritmom za otkrivanje površinskih grešaka u realnom vremenu

Design and analysis of the internally cooled smart cutting tools with the applications to adaptive machining

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Adaptive machining with internally cooled smart cutting tools is a smart solution for industrial applications, which have stringent manufacturing requirements such as contamination free machining (CFM), high material removal rate, low tool wear and better surface integrity. The absence of cutting fluid in CFM causes the cutting tool and the workpiece subject to great thermal loads owing to higher friction and adhesion, and as a result may increase the levels of tool wear drastically. The increase in cutting temperature may influence the chip morphology which in return producing metal chips in unfavourable ribbon or snarl forms. CFM is difficult to be realized as contaminants can be in various forms in the machining operation and to avoid them totally requires a very tight controlled condition. However, the ecological, economical and technological demands compel the manufacturing practitioners to implement environmentally clean machining process (ECMP). Machining with innovative cooling techniques such as heat pipe, single-phase microduct, cryogenic or minimum quantity lubrication (MQL) has been intensely researched in recent years in order to reduce the cutting temperature in ECMP, thus enabling the part quality, the tool life and the material removal rate achieved in ECMP at least equate or surpass those obtained in conventional machining. On the other hand, the reduction of cutting temperature by using these techniques is often superfluous and is adverse to the produced surface roughness as the work material tends to inherent brittle and hard property at low temperature. Open cooling system means the machining requires a constant cooling supply and it does not provide a solution for process condition feedback as well.This Ph.D. project aims to investigate the design and analysis of internally cooled cutting tools and their implementation and application perspectives for smart adaptive machining in particular. Circulating the water based cooling fluid in a closed loop circuit contributes to sustainable manufacturing. The advantage of reducing cutting temperature from localized heat at the tool tip of an internally cooled cutting tool is enhanced with the smart features of the tool, which is trained by real experimental data, to cognitively vary the coolant flow rate, cutting feed rate or/and cutting speed to control the critical machining temperature as well as optimum machining conditions. Environmental friendly internal micro-cooling can avoid contamination of generated swarf which can also reduce the cutting temperature and thus reduce tool wear, increase machining accuracy and optimize machining economics. Design of the smart cutting tool with internal micro-cooling not only takes into account of the environmental aspects but also justifies with its ability to reduce the machining cost. Reduction of production cost can be achieved with the lower consumption of cooling fluid and improved machining resources/ energy efficiency. The models of structural, heat transfer, computational fluid dynamics (CFD) and tool life provide useful insight of the performance of the internally cooled smart cutting tool. Experimental validation using the smart cutting tool to machine titanium, steel and aluminium, indicates that the application of internally cooled smart cutting tools in adaptive machining can improve machining performance such as cutting temperature, cutting forces and surface quality generated. The useful tool life span is also extended significantly with internally cooled smart cutting tools in comparison to the tool life in conventional machining. The internally cooled smart cutting tool has important implications in the application to ECMP particularly by overcoming the stigma of high uncontrollable cutting temperature with the absence of cooling fluid.Brunel Universit

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Air-Drill With Accelerometer And Microphone For Composite Drilling Application

Impaired dimensional accuracy and assembly tolerance due to tool wear could leads to serious problem such as part dismissals in substantial amount on the structure of aircraft. To make sure that drilled holes in composite materials are of good quality from various aspects, current practice in the industries was the disposal of the drill bits after it is being used for a predetermined number of cycles. However, this leads to wastage of some of the expensive drill bit which can still produce holes without delamination even though it has been used for predetermined number of cycles. To monitor the tools condition, researchers have conduct various experiment to correlate tool wear with process variables such as cutting force, thrust force, vibrations, spindle current, acoustic emission etc. In this research, the effect of sharpness of drill bits on vibrations of air drill, sound produced during drilling and quality of holes produced will be studied. In this experiment, 3 accelerometers have been attached on locations near the spindle, at the middle and near the handle of air drill to measure the vibrations of the air drill. Microphone was placed about 1 meter away from the jig and the sounds produced during drilling process were collected. The vibrations of air drill and sound produced during drilling with different conditions of drill bit are analysed and compared. The result shows that vibrations measured by accelerometer placed nearest to the spindle are most sensitive to change in condition of drill bit. For CFRP plate, maximum amplitude of air drill vibration increases with condition of air drill in order: sharp, no drill bit, blunt, and chipped off. For GFRP plate, maximum amplitude of air drill vibration increases with condition of air drill in order: no drill bit, blunt, chipped off and sharp. Other than that, the fast Fourier Transform of the vibration signals from this accelerometer shows that there was a unique set of frequencies with significant peaks with different conditions of drill bits

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation