Tool Condition Monitoring (TCM) methods have shown significant potential to automatically detect worn tools without intervention in the machining process, thus decreasing machine downtime and improving reliability and part quality. Previous research on TCM systems have used a wide variety of time-domain and frequency-domain features extracted from cutting force related parameters as well as mechanical and acoustical vibrations to infer the wear state of tools. This project concerns the process of drilling thousands of tight-tolerance holes on tubesheets and baffles of heat exchangers using large diameter indexable insert drills on a horizontal boring machine. To address the issues involved in the process, the aim of this research is to develop a non-intrusive, indirect, online TCM system on the horizontal boring machine to monitor the drill wear and hole quality while drilling. The specific objectives are to establish an indirect TCM system for the drilling process, to develop models to predict tool wear and the machining accuracy of the drilled holes, and to develop an optimum tool replacement strategy. The TCM system developed used two cutting-force related signals on the horizontal boring machine, namely the spindle motor current and the axial feed motor current. Features extracted from these data streams, as well as the machining parameters, the cutting speed and the feed rate, and the number of holes drilled with the current inserts, are the inputs to a series of models to predict the tool wear state and the hole diameter. The first model is an autoregressive model that allows the prediction of the extracted features for the next hole before it is drilled. As each hole is drilled, this model is updated with the most recent data to improve the accuracy of the prediction. The predicted values for the features are then used as inputs to the second and third models which are surface response models, one to estimate the tool wear state and one to estimate the hole diameter. A tool replacement strategy based on applying limits to the predicted hole diameter was also developed. Adjusting these limits allows the strategy to be tuned for either hole accuracy or tool life depending on the requirements of a specific application. Tuning the replacement strategy for tool life resulted in a significant 44% increase in tool life and a non-trivial reduction in machine down time due to fewer tool changes while holding a hole diameter tolerance of ±0.1mm. The TCM system ensured that not a single over tolerance hole would have been drilled which is critically important since over tolerance holes can result in a scrapped workpiece. The proposed 3-model TCM system shows promise in being able to significantly reduce the risk of drilling out of tolerance holes while at the same time increasing tool life and correspondingly decreasing tool change time. The models are able to accurately predict the insert flank wear and as well as the actual hole diameter within acceptable error. The TCM system could be implemented in an industrial settingwith minimal revision and since it is an indirect system there would be no intrusion into the manufacturing operation. One limitation of the TCM system as proposed is that it is only capable of detecting gradual tool wear and not catastrophic tool failure, a limitation that was known from the outset but was not investigated as it was beyond the scope of this project. The proposed TCM system would allow the integration of additional functionality to instantaneously detect catastrophic tool failure. Finally, for use in a production environment, the developed models need to be implemented on a standalone device that requires essentially no operator input to monitor continuous drilling operations for tubesheet and baffle applications. This implementation could include automatic detection of the machining parameters using frequency analysis of the motor signals.

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In a fully automated manufacturing environment, instant detection of condition state of the cutting tool is essential to the improvement of productivity and cost effectiveness. In this paper, a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach was developed to investigate the effectiveness of multisensor fusion when machining 4340 steel with multi-layer coated and multi-flute carbide end mill cutter. Feature- and decision-level information fusion models utilizing assorted combinations of sensors were studied against selected ML algorithms and their majority vote ensemble to classify gradual and transient tool abnormalities. The criterion for selecting the best model does not only depend on classification accuracy but also on the simplicity of the implemented system where the number of features and sensors is kept to a minimum to enhance the efficiency of the online acquisition system. In this study, 135 different features were extracted from sensory signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing modules. Then, these features along with machining parameters were evaluated for significance by using different feature reduction techniques. Specifically, two feature extraction methods were investigated: independent component analysis (ICA), and principal component analysis (PCA) and two feature selection methods were studied, chi square and correlation-based feature selection (CFS). For various multi-sensor fusion models, an optimal feature subset is computed. Finally, ML algorithms using support vector machine (SVM), multilayer perceptron neural networks (MLP), radial basis function neural network (RBF) and their majority voting ensemble were studied for selected features to classify not only flank wear but also breakage and chipping. In this research, it has been found that utilizing the multisensor feature fusion technique under majority vote ensemble gives the highest classification performance. In addition, SVM outperformed other ML algorithms while CFS feature selection method surpassed other reduction techniques in improving classification performance and producing optimal feature sets for different models.

In an effort to increase machining efficiency and minimize costs, research into tool condition monitoring (TCM) systems has focused on developing methods to allow for unmanned machining. For drilling processes, such systems typically use indirect approaches to monitoring the tool condition by measuring spindle torque and feed force as well as vibrations including acoustic emission (AE – mechanical vibrations faster than 100 kHz). This project aimed to advance the state-of-the-art in the area of TCM by developing a method to detect sudden tool failures in large diameter (> 25 mm) indexable insert drills. This project was a continuation of the research conducted by Mr. R. Griffin (a former MSc student), who developed a model capable of predicting long term wear trends in indexable insert drills [1]. Notably, his model was unable to react to sudden tool breakage due to tool chipping, which was addressed by this project as presented in this thesis. In order to develop and train models able to detect sudden tool failure, an experiment was developed and installed in the field of the industry partner of this project. The experiment’s main feature was a pair of AE sensors added to the existing torque and force sensors. On this setup, experiments were conducted by drilling 2251 holes in workpieces using indexable insert drills with or without the insert breaking. When drilling holes without the insert breaking, the holes were named as good ones; and when drilling holes with the insert breaking they were named as bad holes. During the drilling process, data was collected from current sensors attached to the spindle motor and feed motor as well as from an AE sensor on the spindle and on the workpiece. From the signals from the spindle motor current and feed motor current sensors, algorithms were developed to identify and divide the signals of drilling a hole into different sections of the drilling cycle (i.e. entrance, steady-state, exit, etc.). Steady-state time-domain features were extracted from the sensor signals measured for all holes drilled in the experiments and the extracted features were used to train and test the classifier models. These models were cross validated to determine which type of model was the best fit for the drilling data collected. The results from the classifier models show that most of the classifiers tested have the ability to identify sudden tool breakage based on the data recorded in the present study, with varying degrees of success. The naïve Bayes classifier was able to detect the most failures but suffered from a large number of falsely detected failures. Both the classification tree and linear discriminant analysis classifiers had lower failure detection rates than the naïve Bayes classifier, but did not suffer from the same amount of false positives; as such, these two classifiers had higher overall classification rates than the naïve Bayes. These results suggest that classification tree and linear discriminant analysis methods are better suited for the drilling application and that the time-domain features should be complemented by others, such as the features extracted from the frequency domain, to accurately diagnose the tool condition. Future research should focus on extracting frequency and time-frequency domain features as these features might contain more information on tool condition. In addition, methods of examining features at the entrance and exit of the holes should be investigated as these two points in the drilling cycle are the most prone to sudden tool failure.

This thesis presents a multi-physics-based approach to the design and analysis of smart cutting tools for emerging industrial requirements, within an innovative design process. The design process is in stages according to design specifications and requires analysis, conceptual design, detailed design, prototype production and service testing. The research presented in the thesis follows the design process but focuses on the detailed design of the smart turning tool, including mechanical design, electrical wiring and sensor circuitry, embedded algorithms development, and multi-physics-based simulation for the tool system integration, design analysis and optimisation. The thesis includes the introduction of the research background, a critical literature review of the research topic, a multi-physics-based design and analysis of the smart cutting tool, a mechanical structural detail design of the prototype smart turning tool, the electrical system design focusing on cutting force measurement and embedded wireless communication features, and the final experimental testing and calibration of the smart cutting tool. The contributions to knowledge are highlighted in the conclusions chapter towards the end of the thesis. The research proposes multi-physics-based design and analysis concepts for a smart turning tool, which can measure the cutting forces on a 0.1 N scale and can also be used to monitor the tool condition, particularly for ultraprecision and micro-machining purposes. The smart turning tool is a sensored tool, constructed with wireless and plug-and-produce features. The tool design modelling and simulation was undertaken within a multi-physics modelling and analysis environment-based on COMSOL. This integrates the piezoelectric physics with mechanical structural design and radio frequency electronic communications of cutting force signals. The multi-physics simulation method takes account of all design-mechanics-physics-electronics analysis and transformations simultaneously within one computational environment, including FEA analysis, modal analysis, structural deformation, lead piezoelectric effect and wireless data/signal simulation. With the multi-physics simulation developed, the integrated design of the smart turning tool and its performance can be physically analysed and optimised in a virtual environment. The tool design process follows the total design methodology, which can be strictly executed in several design stages. Both mechanical and electrical design of the smart cutting tool are embodied into the tool detail design. The tool mechanical structure is systematically built from the selection of the tool material, through the structure analysis and further progressed with static force – strain/stress transformation, equivalent force measurement and calibration. The electrical circuitry was systematically developed from developing the customised charge amplifier, detail design of the main circuitry and coding development procedure, preliminary PCB fabrication and multi-sensor port PCB development, as well as the real-time cutting force monitoring programming and interface coding. The experiment calibrations and cutting trials with the tool system are also designed in light of the total design methodology. The experiment procedure for using the smart turning tool is further presented in two different sections. The thesis concludes with a further discussion on the main research findings, which are further supported by the highlighted contributions to knowledge and recommendations for future work.

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Milling is a widely used cutting process, most commonly applied to machining external surfaces of workpieces. When machining operations are required within hard to reach areas of components, or deep within the bore of components, alternative methods of metal removal are generally employed. Typically when milling at extended reaches, difficulties may increase exponentially when trying to achieve distances several meters into a component. Essentially every topic of the milling process becomes difficult and more convoluted. Firstly to generate a stable cutting condition, and ultimately for an operator to be able to understand the cutting conditions, when all normal senses to interpret the machining stability are removed. The aim for the research is, to enable the operation of high slenderness ratio internal milling operations to become a viable technology, by detailing the measures required, to obtain a stable cutting condition. The process needs to be monitored for degradation of the tooling due to wear, and to prevent catastrophic machine damage from tool breakage or machine component failure. This research addresses the lack of knowledge available for milling with extended reaches, and the knowledge gained to overcome the real difficulties that exist for this process. Initial experiments are conducted on a prototype machine to gain experience of the internal machining operation and the many issues that it faced. Establishing requirements of the process via investigation of the tooling and necessary auxiliary equipment, it becomes possible to consider countermeasures to address the errors generated by torsional twisting of the milling arm. A system for applying a counter torque to reduce torsional deflection errors has been employed to successfully reduce the unavoidable issue over such long distances. For the process to become manageable for an industrial operator without a high level of specialist knowledge, the application of tool condition monitoring (TCM) and process condition monitoring (PCM) had to be applied. This addresses a void in available literature and research with respect to internal machining, and enables the process to become practical for an industrial environment. For this reason the research project will concentrate on the application of TCM and PCM onto the machining system. The completion of the research resulted in the process becoming satisfyingly stable, and with a resulting accuracy that satisfies the requirements of the component. Performance of the final system rivalled or achieved better results than had been experienced by the project sponsor.