In manufacturing, monitoring machine health is an important step when implementing Industry 4.0 and ensures effective machining operations and minimal downtime. Monitoring the health of cutting tools during a machining process helps contain the faults associated with gradual tool wear, because they can be tracked and responded to as wear worsens. Left unchecked, tool failures can lead to more severe problems, such as dimensional and surface issues with machined workpieces and lower overall productivity during the machining process. This research explores the use of a machine vision setup used internally by the McMaster Manufacturing Research Institute (MMRI) in their three lathe machines. This machine vision setup provides a direct indication of the tool's maximum flank wear (VBmax), which, according to ISO 3685:1993(E), is set to be 300 µm. Also investigated was the use of image processing and analysis methods to determine the flank wear without removing the tool from the machine. This new, in-machine vision setup is intended to replace the use of an external optical microscope, which requires extended downtime between cutting passes. As a result of this replacement, the experimentation downtime was decreased by around 98.6%, leading to the experiment time to decrease from 5 weeks or more to just a couple of days. In addition, the difference in measurement between a commonly used optical microscope and in-machine vision setup was found to be ±3µm. / Thesis / Master of Science (MSc)

A new methodology was developed to observe and measure tool wear during the die compaction process. The newly developed method is a non-destructive test using silicon rubber to transcript die surface profiles. Tool wear was observed and measured by recording surface roughness and diameter of the cylindrical die replicas on a surface profiler including weight loss in the die. To validate this procedure, an aluminum alloy powder without lubricant was compacted to examine the effect on die wear. The die materials were machined from several wrought and composite materials. A further dimension to the program was the variance of compaction pressures and lubricants.

With the possession of extremely broad unique properties, particulate reinforced aluminum composites are very attractive in diverse applications. Aluminum matrix particulate composites are challenging to work with. A single pressing and sintering process was used to fabricate the reinforced aluminum composites in this study. The key advantage of this method is its comparative low expense. However, abrasive reinforcement powders can lead to shorter tool life. To study the fundamental wear mechanisms during the die compaction process, a new method was developed and combined with experiments to quantify tool wear. Automatic die compaction experiments and tribological experiments are employed in this study. The tribologcial experiments consist of a modified pin-onlat test and a modified loop test. Mass loss of tools was recorded during all the experiments. A new tool wear model was used in this study to investigate effect of different hard phase and different lubricant level on die compaction process.

The present work was performed at AB Sandvik Coromant as a part in improving the knowledge and understanding about wear of uncoated WC/Co cutting tools during turning of titanium alloy Ti-6Al-4V. When machining titanium alloys, or any other material, wear of the cutting tools has a huge impact on the ability to shape the material as well as the manufacturing cost of the finished product. Due to the low thermal conductivity of titanium, high cutting temperatures will occur in narrow regions near the cutting edge during machining. This will result in high reaction and diffusion rates, resulting in high cutting tool wear rates. To be able to improve titanium machining, better knowledge and understanding about wear during these tough conditions are needed. Wear tests were performed during orthogonal turning of titanium alloy and the cutting tool inserts were analysed by SEM, EDS and optical imaging in Alicona InfiniteFocus. Simulations in AdvantEdge provided calculated values for cutting temperatures, cutting forces and contact stresses for the same conditions as used during wear tests. It was found that turning titanium alloy with WC/Co cutting tools at cutting speeds 30-60 m/min causes chamfering of the cutting tool edge and adhesion of a build-up layer (BUL) of workpiece material on top of the rake face wear land. The wear rate for these low cutting speeds was found to be almost unchanging during cutting times up to 3 minutes. During cutting speeds of 90-115 m/min, crater wear was found to be the dominating wear mechanism and the wear rate was found to have a linear dependence of cutting speed. An Arrhenius-type temperature dependent wear mechanism was found for high cutting speeds, between 90 and 115 m/min.

abstract: The demand for miniaturized components with feature sizes as small as tens of microns and tolerances as small as 0.1 microns is on the rise in the fields of aerospace, electronics, optics and biomedical engineering. Micromilling has proven to be a process capable of generating the required accuracy for these features and is an alternative to various non-mechanical micro-manufacturing processes which are limited in terms of cost and productivity, especially at the micro-meso scale. The micromilling process is on the surface, a miniaturized version of conventional milling, hence inheriting its benefits. However, the reduction in scale by a few magnitudes makes the process peculiar and unique; and the macro-scale theories have failed to successfully explain the micromilling process and its machining parameters. One such characteristic is the unpredictable nature of tool wear and breakage. There is a large cost benefit that can be realized by improving tool life. Workpiece rejection can also be reduced by successfully monitoring the condition of the tool to avoid issues. Many researchers have developed Tool Condition Monitoring and Tool Wear Modeling systems to address the issue of tool wear, and to obtain new knowledge. In this research, a tool wear modeling effort is undertaken with a new approach. A new tool wear signature is used for real-time data collection and modeling of tool wear. A theoretical correlation between the number of metal chips produced during machining and the condition of the tool is introduced. Experimentally, it is found that the number of chips produced drops with respect to the feedrate of the cutting process i.e. when the uncut chip thickness is below the theoretical minimum chip thickness. / Dissertation/Thesis / Masters Thesis Engineering 2015

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.

This licentiate thesis has the main focus on evaluation of the wear of coated and uncoated polycrystalline cubic boron nitride cutting tool used in cutting operations against hardened steel. And to exam the surface finish and integrity of the work material used. Harder work material, higher cutting speed and cost reductions result in the development of harder and more wear resistance cutting tools. Although PCBN cutting tools have been used in over 30 years, little work have been done on PVD coated PCBN cutting tools. Therefore hard turning and hard milling experiments with PVD coated and uncoated cutting tools have been performed and evaluated. The coatings used in the present study are TiSiN and TiAlN. The wear scar and surface integrity have been examined with help of several different characterization techniques, for example scanning electron microscopy and Auger electron spectroscopy.   The results showed that the PCBN cutting tools used displayed crater wear, flank wear and edge micro chipping. While the influence of the coating on the crater and flank wear was very small and the coating showed a high tendency to spalling. Scratch testing of coated PCBN showed that, the TiAlN coating resulted in major adhesive fractures. This displays the importance of understanding the effect of different types of lapping/grinding processes in the pre-treatment of hard and super hard substrate materials and the amount and type of damage that they can create. For the cutting tools used in turning, patches of a adhered layer, mainly consisting of FexOy were shown at both the crater and flank. And for the cutting tools used in milling a tribofilm consisting of SixOy covered the crater. A combination of tribochemical reactions, adhesive wear and mild abrasive wear is believed to control the flank and crater wear of the PCBN cutting tools. On a microscopic scale the difference phases of the PCBN cutting tool used in turning showed different wear characteristics. The machined surface of the work material showed a smooth surface with a Ra-value in the range of 100-200 nm for the turned surface and 100-150 nm for the milled surface. With increasing crater and flank wear in combination with edge chipping the machined surface becomes rougher and showed a higher Ra-value. For the cutting tools used in milling the tendency to micro edge chipping was significant higher when milling the tools steels showing a higher hard phase content and a lower heat conductivity resulting in higher mechanical and thermal stresses at the cutting edge.

This diploma thesis deals with the choice of suitable VBD for longitudinal turning barrel steel OCHN3MFA. In the theoretical part, serving as a basis for the experimental part, the technology of turning, tooling and coating methods was analyzed. In the experimental part, ten VBDs were tested, eight of which were of cemented carbide and two of ceramic. The monitored parameters were force load and tool wear VB. From the evaluated load data and tool wear, a suitable VBD for turning barrel steel was selected. The most suitable VBD for turning barrel steel is VBD – G. Tools VBD – I and VBD – J from ceramics are inappropriate tools for turning barrel steel.

The production of faulty parts poses significant challenges for production facilities, as it leads to increased inventory levels, operating costs, and impedes overall productivity. Despite its fundamental nature, this issue remains prevalent in manufacturing operations. To effectively reduce the rate of faulty parts, it is crucial to have a thorough understanding of the manufacturing process and exercise control by monitoring various parameters.  The aim of this study is to investigate the right prerequisites which enable quality assurance through in-line failure detection by vibration analysis. The research questions formulated for this thesis are as follows:  RQ1: What are the essential prerequisites for quality assurance through in-line failure detection by vibration analysis in the machining of splines? RQ2: How suitable is the use of vibration measurements in identifying and sorting out poor quality in the specific machining process of splines? The study was conducted through a literature review and a single case study of a gear hobbing process in an industrial manufacturing company.  The collection of data was acquired via interviews, observations, and vibration measurements during the spline manufacturing process. To analyse the collected data several tools got used. Python was used as the tool for performing several operations on the dataset, such as FFT of the vibration signals. To later visualize the results which facilitated the analysis of the entire dataset.  The results of the study indicate several similarities between the documented fault progression in gear systems and the manufacturing of splines. However, further research is needed to identify the core differences between these two fault progressions.  Furthermore, the study identified the essential prerequisites for implementing vibration analysis as an in-line failure detection method in spline manufacturing operations. Additionally, it concluded on the suitability of vibration analysis for identifying faults in this context.

This thesis investigates the current state of tool wear prediction models in machining, focusing on their limitations in accurately incorporating the complex dynamics of cutting fluids and their industrial applicability. It proposes a comprehensive evaluation framework to classify and evaluate a wide range of models, including empirical, physical, computational, and data-driven models. The study identifies the key limitations and strengths of each model category. It proposes enhancements by integrating advanced fluid dynamics and predictive modeling techniques to improve tool wear predictions' accuracy and industrial applicability. A structured literature review was conducted to investigate and evaluate existing tool wear models and their integration with cutting fluid dynamics. This review included defining search criteria, selecting relevant studies, and assessing their quality and relevance. The study uses thematic analysis and model evaluation frameworks to classify and evaluate the models, leading to the identification of critical limitations and strengths. The literature review and model evaluation findings revealed that empirical models, while simple and quick to implement, showed moderate accuracy and limited fluid dynamics integration. Physical models provided high accuracy in specific conditions but were computationally intensive. Computational models, particularly those using techniques like Finite Element Analysis (FEA) and Computational Fluid Dynamics(CFD), offered detailed insights and high accuracy but required significant computational resources. Data-driven models demonstrated exceptional predictive capabilities and comprehensive fluid dynamics integration but relied heavily on data availability and quality.  The proposed enhancements include introducing non-linear elements into empirical models, incorporating simplified fluid models or empirical correlations into physical models, exploring reduced-order models (ROMs) or surrogate models for computational models, and developing robust data preprocessing and augmentation techniques for data-driven models. These enhancements aim to improve the accuracy and applicability of tool wear models in industrial machining processes, ultimately contributing to more efficient and cost-effective machining operations. The study emphasizes the importance of a systematic and holistic approach to model evaluation and enhancement. Future research should focus on validating these proposed enhancements through empirical studies and real-world applications, ensuring their relevance and robustness in diverse industrial settings. This research offers significant potential to advance tool wear modeling, providing valuable insights for both academia and industry.