Composite Drilling Monitoring System

This research presents a monitoring system for composite drilling that inspect the condition of the drill bit using a digital microscope camera with a computer system that implies image processing method for flank wear detection. This project aims to provide a new system by introducing a new camera view angle that can detect wear or changes in the drill bit geometry specifically, flank region in order to determine the tool life span. Images of wear drill bit are captured and later on analyse using MATLAB software to measure wear percentage. The camera view angle is determined based on the principle of illumination and decided to be at 45 degree view angle. Then, the images taken undergo image pre-processing and further analyse to detect wear by image fusion method. During the image pre-processing, the true colour image is converted into binary images and canny edge detector is applied to detect the flank boundary. Morphological operators such as dilation, flood fill, closing, and erosion is then applied to reconstruct the flank region to make sure it is approximately the same shape as the actual flank region. Finally, the reference image and the wear drill bit image are fuse together using Principle Component Analysis (PCA) method and the wear percentage is calculated. This project is divided into two parts which are hardware and software part. For the hardware part, a setup is design and innovated meanwhile for the software part, a MATLAB code is developed to process the images taken and calculate the wear percentage. The algorithm used is referred by Math Work and being adapted into this project to satisfy the objective of this project. At the end of the project, we could observe the changes in the flank wear area as compared to a reference image and the wear percentage can be calculated

Indirect monitoring method of tool wear using the analysis of cutting force during dry machining of Ti alloys

In recent decades, optimize tool life is in constant evolution so many researchers have focused to analysis the tool wear by indirect monitoring (e.g. acoustic emission, cutting forces, vibrations) that plays a significant role in control and improvement of product during of machining operations - in real time to prevent instabilities of process. On another hand, it is recalled that titanium alloys are used in many industries as aerospace sector that have been utilized in strength to weight ration reduction in some parts of aircraft. On the negative side, Ti alloys tends to be hard machining due to their mechanical properties such as low thermal conductivity and modulus of elasticity causing increase cutting temperature, tool breakage or possibility interruption of process.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

On the Machinability of an Al-63%SiC Metal Matrix Composite

This paper presents a preliminary study of aluminium matrix composite materials during machining, with a special focus on their behavior under conventional processes. This work will expand the knowledge of these materials, which is considered to be strategic for some industrial sectors, such as the aeronautics, electronics, and automotive sectors. Finding a machining model will allow us to define the necessary parameters when applying the materials to industry. As a previous step of the material and its machining, an experimental state-of-the-art review has been carried out, revealing a lack of studies about the composition and material properties, processes, tools, and recommended parameters. The results obtained and reflected in this paper are as follows; SiC is present in metallic matrix composite (MMC) materials in a very wide variety of sizes. A metallographic study of the material confirms the high percentage of reinforcement and very high microhardness values registered. During the machining process, tools present a very high level of wear in a very short amount of time, where chips are generated and arcs are segmented, revealing the high microhardness of the material, which is given by its high concentration of SiC. The chip shape is the same among other materials with a similar microhardness, such as Ti or its alloys. The forces registered in the machining process are quite di erent from conventional alloys and are more similar to the values of harder alloys, which is also the case for chip generation. The results coincide, in part, with previous studies and also give new insight into the behavior of this material, which does not conform to the assumptions for standard metallic materials, where the hypothesis of Sha er is not directly applicable. On the other hand, here, cutting forces do not behave in accordance with the traditional model. This paper will contribute to improve the knowledge of the Al-63%SiC MMC itself and the machining behavior

Modelling flan wear of carbide tool insert in metal cutting

In this paper theoretical and experimental studies are carried out to investigate the intrinsic relationship between tool flank wear and operational conditions in metal cutting processes using carbide cutting inserts.Anewflank wear rate model, which combines cutting mechanics simulation and an empirical model, is developed to predict tool flank wear land width. A set of tool wear cutting tests using hard metal coated carbide cutting inserts are performed under different operational conditions. The wear of the cutting inset is evaluated and recorded using Zygo New View 5000 microscope. The results of the experimental studies indicate that cutting speed has a more dramatic effect on tool life than feed rate. The wear constants in the proposed wear rate model are determined based on the machining data and simulation results. A good agreements between the predicted and measured tool flank wear land width show that the developed tool wear model can accurately predict tool flank wear to some extent

Laser surface texturing of a WC-CoNi cemented carbide grade: surface topography design for honing application

Abrasive effectiveness of composite-like honing stones is related to the intrinsic surface topography resulting from the cubic boron nitride (CBN) grains protruding out of the metallic matrix. Within this framework, Laser Surface Texturing (LST) is implemented for replicating topographic features of a honing stone in a WC-base cemented carbide grade, commonly employed for making tools. In doing so, regular arrays of hexagonal pyramids (similar to CBN grains) are sculpted by a laser micromachining system. Micrometric precision is attained and surface integrity does not get affected by such surface modification. Finally, potential of laser-patterned cemented carbide tools, as alternative to conventional honing stones, is supported by successful material removal and enhanced surface smoothness of a steel workpiece in the abrasive testing.Peer ReviewedPostprint (author's final draft

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Live Tool Condition Monitoring of SiAlON Inserted Tools whilst Milling Nickel-Based Super Alloys

Cutting tools with ceramic inserts are often used in the process of machining many types of super alloys, mainly due to their high strength and thermal resistance. Nevertheless, during the cutting process, the plastic flow wear generated in these inserts enhances and propagates cracks due to high temperature and high mechanical stress. This leads to a very variable failure of the cutting tool. Furthermore, in high-speed rough machining of nickel-based super alloys, such as Inconel 718 and Waspalloy, it is recommended to avoid the use of any type of coolant. This in turn, enables the clear visualization of cutting sparks, which in these machining tasks are quite distinctive. The present doctoral thesis attempts to set the basis of a potential Tool Condition Monitoring (TCM) system that could use vison-based sensing to calculate the amount of tool wear. This TCM system would work around the research hypothesis that states that a relationship exists between the continuous wear that ceramic SiAlON (solid solutions based on the Si3N4 structure) inserts experience during a high-speed machining process, and the evolution of sparks created during the same process. A successful TCM system such as this could be implemented at an industrial level to aid in providing a live status of the cutting tool’s condition, potentially improving the effectiveness of these machining tasks, whilst preventing tool failure and workpiece damage. During this research, sparks were analyzed through various visual methods in three main experiments. Four studies were developed using the mentioned experiments to support and create a final predictive approach to the TCM system. These studies are described in each thesis chapter and they include a wear assessment of SiAlON ceramics, an analysis of the optimal image acquisition systems and parameters appropriate for this research, a study of the research hypothesis, and finally, an approach to tool wear prediction using Neural Networks (NN). To carry out some of these studies, an overall methodology was structured to perform experiments and to process spark evolution data, as image processing algorithms were built to extract spark area and intensity. Towards the end of this thesis, these spark features were used, along with measured values of tool wear, namely notch, flank and crater wear, to build a Neural Network for tool wear prediction

Detection Of Chipping In Ceramic Cutting Inserts From Workpiece Profile Signature During Turning Process Using Machine Vision

Ceramic tools are prone to chipping due to their low impact toughness. Tool chipping significantly decreases the surface finish quality and dimensional accuracy of the workpiece. Thus, in-process detection of chipping in ceramic tools is important especially in unattended machining. Existing in-process tool failure detection methods using sensor signals have limitations in detecting tool chipping. The monitoring of tool wear from the workpiece profile using machine vision has great potential to be applied in-process, however no attempt has been made to detect tool chipping. In this work, a vision-based approach has been developed to detect tool chipping in ceramic insert from 2-D workpiece profile signature. The profile of the workpiece surface was captured using a DSLR camera. The surface profile was extracted to sub-pixel accuracy using invariant moment method. The effect of chipping in the ceramic cutting tools on the workpiece profile was investigated using autocorrelation function (ACF) and fast Fourier transform (FFT). Detection of onset tool chipping was conducted by using the sub-window FFT and continuous wavelet transform (CWT). Chipping in the ceramic tool was found to cause the peaks of ACF of the workpiece profile to decrease rapidly as the lag distance increased and deviated significantly from one another at different workpiece rotation angles. From FFT analysis the amplitude of the fundamental feed frequency increases steadily with cutting duration during gradual wear, however, fluctuates significantly after tool has chipped. The stochastic behaviour of the cutting process after tool chipping leads to a sharp increase in the amplitude of spatial frequencies below the fundamental feed frequency. CWT method was found more effective to detect the onset of tool chipping at 16.5 s instead of 17.13 s by sub-window FFT. Root mean square of CWT coefficients for the workpiece profile at higher scale band was found to be more sensitive to chipping and thus can be used as an indicator to detect the occurrence of the tool chipping in ceramic inserts