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

Development of A Tool Condition Monitoring System for Flank Wear in Turning Process Using Machine Learning

Computer-numerical control (CNC) machining has become the norm in most manufacturing businesses. In order to maximize machining efficiency, prevent unintended damage, and maintain product quality all at once, tool wear measurement is essential. Wear on the tools is typically an inevitable side effect of machining. Because it endangers the machining process, flank wear, the most frequent type of tool wear, should be avoided. This study's objective is to fill this growing need by developing a system that can use machine learning to monitor tool condition during the turning process using MATLAB. The regression method with boosted decision trees and SVR with Gaussian kernels are applied to predict the flank wear based on the vibration signal and cutting parameters. This study found that the regression with boosted decision tree method has a lower mean average percentage error, 6.43%, while SVR is 10.11%. Plus, R2 for regression is slightly better than SVR. It shows that the system successfully produced an accurate prediction of flank wear

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 toolchipping 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 xxiv sensitive to chipping and thus can be used as an indicator to detect the occurrence of the tool chipping in ceramic inserts

Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain

Rotating machinery—a crucial component in modern industry, requires vigilant monitoring such that any potential malfunction of its electromechanical systems can be detected prior to a fatal breakdown. However, identifying faulty signals from a defective rotating machinery is challenging due to complex dynamical behaviour. Therefore, the search for features which best describe the characteristic of different fault conditions is often crucial for condition monitoring of rotating machinery. For this purpose, this study used the intensification and diversification properties of the recently proposed moth-flame optimisation (MFO) algorithm and utilised the algorithm in the proposed feature selection scheme. The proposed method consisted of three parts. First, the vibration signals of gear with different fault conditions were decomposed by a fourth-level discrete wavelet packet transform, and the statistical features at all constructed nodes were derived. Second, the MFO algorithm was utilised to select the optimal discriminative features. Lastly, the MFO-selected features were used as the input for a support vector machine (SVM) diagnostic model to identify fault patterns. To further demonstrate the superiority of the proposed method, other feature selection approaches were applied, including randomly selected features and complete features, and other diagnostic models, namely the multilayer perceptron neural network and k-nearest neighbour. Comparative experiments demonstrated that SVM with the MFO-selected features outperformed the others, with the classification accuracy of 99.60%, thus validating its effectiveness

Minimization of tool path length of drilling process using particle swarm optimization (PSO)

In the era of challenging economic, the industry in our country has been forced to produce a good quality product and increase the productivity of machining process simultaneously in order to compete with other countries. Drrilling process is one of a very important cutting process in industry. In a drilling for machining by Computer Numerical Control (CNC) such as drilling machines, the parameter of the tool routing path for the machining operation plays a very important role to minimize the machining time (Tiwari 2013, Rao and Kalyankar 2012) . This machine can be used with procedures for drilling, spreading, weaning and threading with a lot of the holes precisely. In order to increase the efficiency and productivity of drilling process, optimization on parameters of process can lead to better performance. Optimization of holes drilling operations will lead to reduction in time order and better productivity of manufacturing systems. Optimizing the tool path has played an important role, especially in mass production because reducing the time to produce one piece eventually lead to a significant reduction in the cost of the entire series (Pezer, 2016). In various publications and articles, scientists and researchers adapted several methods of artificial intelligence (AI) or hybrid optimization method for tool path artificial immune system (AIS), genetic algorithms (GA), Artificial Neural networks (ANN) Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) (Narooei and Ramli, 2014). These methods were been proven that can produce better performance and increase the productivity of drilling process. Therefore, in this study, the Particle Swarm Optimization (PSO) algorithm was develop in order to minimizing the tool path length in the drilling process which can produce the better results for the required machining time process. For this study, the main purpose is to apply the Particle Swarm Optimization (PSO) algorithm for use in searching for the optimal tool routing path for in simulation of drilling proces

Image-based oil palm leaf disease detection using convolutional neural network

Over the years, numerous studies have been conducted on the integration of computer vision and machine learning in plant disease detection. However, these conventional machine learning methods often require the contour segmentation of the infected region from the entire leaf region and the manual extraction of different discriminative features before the classification models can be developed. In this study, deep learning models, specifically, the AlexNet convolutional neural network (CNN) and the combination of AlexNet and support vector machine (AlexNet-SVM), which overcome the limitation of handcrafting of feature representation were implemented for oil palm leaf disease identification. The images of healthy and infected leaf samples were collected, resized, and renamed before the model training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in the conventional machine learning methods. The optimal architecture of AlexNet CNN and AlexNet-SVM models were then determined and subsequently applied for the oil palm leaf disease identification. Comparative studies showed that the overall performance of the AlexNet CNN model outperformed AlexNet-SVM-based classifier

Application of wavelet analysis in tool wear evaluation using image processing method

Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the work-piece during machining process, the machined surface provides valuable information about the cutting tool condition. Therefore, the objective of present study is to evaluate the tool wear based on the workpiece profile signature by using wavelet analysis. The effect of wavelet families, scale of wavelet and statistical features of the continuous wavelet coefficient on the tool wear is studied. The surface profile of workpiece was captured using a DSLR camera. Invariant moment method was applied to extract the surface profile up to sub-pixel accuracy. The extracted surface profile was analyzed by using continuous wavelet transform (CWT) written in MATLAB. The re-sults showed that average, RMS and peak to valley of CWT coefficients at all scale increased with tool wear. Peak to valley at higher scale is more sensitive to tool wear. Haar was found to be more effective and significant to correlate with tool wear with highest R2 which is 0.9301

Microstructure analysis of aluminium metal matrix alloy with silicon carbide and hexogonal boron nitride

Aluminum metal matrix composite (AMMCs) are considered a group of new advance material for its light, weight, high strength, modulus, low co-efficient of thermal expansion and good wear properties. In recent years, Metal Matrix Composite (MMCs) have attracted much attention due to excellent mechanical properties such as high specific strength and wear resistance (Poletti et al., 2008). AMMCs are widely used in aircraft, aerospace, automobile and various others field. The MMCs encompasses a wide range scale and microstructure. MMCs could be a material with a least two constituent elements. One necessary to be metal whereas another could also be special metal or alternative material like reinforcement ceramic. Metal matrix composite attract great deal of attentions nowadays due to their great mechanical properties and also their application in advance industry. The network is bulk and nonstop material though support is short and end material improved into matrix. The reinforcement should be stable in given working temperature and non-reactive too. The most commonly used reinforcement are silicon carbide (SiC) and aluminum oxide (Al2O3). The primary function of the reinforcement in MMCs is to carry most of the applied load, where the matrix binds the reinforcement together, and transmit and distributes the external load to the individual reinforcement. Good wetting is an essential condition for the generation for satisfactory bond between particles between particles reinforcement. The composite microstructure may be subdivided, as depicted in, according to whether the reinforcement is in the form of continuous fibers, short fibers or particles

Antecedents and outcomes of brand management from the perspective of resource based view (RBV) theory

Brand management requires greater emphasis on internal factors to increase brand performance. A model of antecedents and outcomes of brand management is developed in this study based on the Resource Based View (RBV) Theory. Top management emphasis on brand, corporate supportive resources and market orientation are identified as crucial internal factors or antecedents for success of brand management. Apart from that, the brand management measurement are expanded in this study with the introduction of three new marketing constructs namely marketing capabilities, innovation and brand orientation as new dimensions in brand management which currently comprised of management related constructs. This study also contributes in the brand management of small and medium enterprise (SMEs) literature as previous studies mainly focused on the brand management for multinational companies or large organizations. One important issue of SMEs is the “internal” brand management which is currently under-researched even though it is critical in brand building and management. Therefore, this research aims to highlight the antecedents and outcomes of brand management in Malaysians’ SMEs based on RBV theory. A comprehensive literature review was done and a conceptual model is proposed in this literature review

Electrode Wear Rate On Electrical Discharge Machining of Titanium Alloys (Ti-6Al-4V) At Different Peak Current and Pulse Duration by Using Modified RBD Palm Oil as Dielectric Fluids

Electrical Discharge Machining (EDM) is a machining process in terms of thermoelectric that removes metal by discharging a discrete sparks series of the metal and workpiece. The cutting tool in EDM has used an electric spark to cut the workpiece of sample and produce the finished part to the demanded shape. Vegetable oil as the dielectric fluid is one way to ensure EDM's long-term viability because it is environmentally friendly and biodegradable. The main objective of this preliminary study is to compare the uses of modified bio-degradable and conventional dielectric fluid performance for a titanium alloy (Ti-6Al-4V) with a copper (Cu) electrode using a sustainable EDM process in terms of electrode wear rate (EWR). To achieve a concentration of viscosity rate as kerosene fluids, RBD palm oil has been transesterified. The effect of EWR of kerosene and modified RBD palm oil as dielectric fluids was investigated in this paper for response variables of pulse duration (ton) of 50, 100, and 150µs, and peak current (Ip) of 6, 9, and 12A. The morphology of the copper electrode, as well as the migration of workpiece material elements to the tool electrode, were studied by using scanning electron microscopy (SEM). The lowest EWR was recorded at Ip=6A with ton=150µs, which is 0.0416mm3/min and 0.0432mm3/min, and the highest EWR was recorded at Ip=12A with ton=50µs, which is 0.1725mm3/min and 0.2324mm3/min, for modified RBD palm oil compared to kerosene, respectively. The EWR rises as the peak current rises, but it decreases as the pulse duration increases. The uses of modified RBD palm oil shows slightly different results compared to kerosene