Muhammad Rizal

Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Tool wear is a detrimental factor that affects the quality and tolerance of machined parts. Having an accurate prediction of tool wear is important for machining industries to maintain the machined surface quality and can consequently reduce inspection costs and increase productivity. Online and real-time tool wear prediction is possible due to developments in sensor technology. Recently, various sensors and methods have been proposed for the development of tool wear monitoring systems. In this study, an online tool wear monitoring system was proposed using a strain gauge-type sensor due to its simplicity and low cost. A model, based on the adaptive network-based fuzzy inference system (ANFIS), and a new statistical signal analysis method, the I-kaz method, were used to predict tool wear during a turning process. In order to develop the ANFIS model, the cutting speed, depth of cut, feed rate and I-kaz coefficient from the signals of each turning process were taken as inputs, and the flank wear value for the cutting edge was an output of the model. It was found that the prediction usually accurate if the correlation of coefficients and the average errors were in the range of 0.989-0.995 and 2.30-5.08% respectively for the developed model. The proposed model is efficient and low-cost which can be used in the machining industry for online prediction of the cutting tool wear progression, but the accuracy of the model depends upon the training and testing data.

Online Cutting Tool Wear Monitoring Using I-Kaz Method and New Regression Model

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals using the regression model and I-kaz method. The detection of tool wear was done automatically using the in-house developed regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out on a CNC turning machine Colchester Master Tornado T4 in dry cutting condition, and Kistler 9255B dynamometer was used to measure the cutting force signals, which then stored and displayed in the DasyLab software. The progression of the cutting tool flank wear land (VB) was indicated by the amount of the cutting force generated. Later, the I-kaz was used to analyze all the cutting force signals from beginning of the cut until the rejection stage of the cutting tool. Results of the I-Kaz analysis were represented by various characteristic of I-kaz 3D coefficient and 3D graphic presentation. The I-kaz 3D coefficient number decreases as the tool wear increases. This method can be used for real time tool wear monitoring.

Comparison of oil press for jatropha oil - A review

Siregar A.N., Ghani J.A., Haron C.H.C., Rizal M., Yaakob Z., Kamarudin S.K. (2015): Comparison of oil press for jatropha oil – a review. Res. Agr. Eng., 61: 1–13. As petrol will soon be exhausted in the near future, Jatropha is going to be one of the substitute candidates for future biodiesel production. Countries of South-East Asia, such as Malaysia, they are going to start the establishment of Jatropha plantations assuming that Jatropha will be the main resource for biodiesel production. A press is commonly used to extract oils from Jatropha. An oil press can be manually driven or engine-powered. In this paper, we will review some available advances focused on mechanical extraction techniques, covering three types of press for Jatropha oil extraction. We have found that major points like operating principles, oil extraction levels, advantages and disadvantages of each press and important factors to increase oil recovery. Based on the study, three types of press are: ram press, which is ineffective; strainer press, which is able to produce more oil than others and cylinder-hole press, which is the best due to its capacity in extracting oil from Jatropha seeds for about 89.4% of oil yields.

Determination of sensor location for cutting tool deflection using finite element method simulation

The aim of this study was to determine the the optimum locations to mount the sensor for measuring the cutting tool deflection during turning process using finite element method simulation. In this study, stress analysis had been conducted using Autodesk Inventor Professional 2010 integrated with ANSYS software. The simulation results were validated using a strain gauge as the sensor for the detection of cutting force signal during the turning of hardened plain carbon steel JIS S45C using carbide tool. Two strain gauges were mounted on the tool holder at two defined locations I and II, at distances of 37 and 47 mm, respectively, from the cutting point. Only one set of cutting parameters was conducted at spindle speed, N = 1000 rpm, feed, f = 0.25 mm/rev and depth of cut, d = 0.80 mm. The turning process was stopped and the insert discarded when the average flank wear, VBm, reached 0.30 mm. The main cutting force, Fy, and the feed force, Fx, for each machining run were measured, collected and analysed at locations I and II. It was found that when strain gauges were placed at a distance of approximately 43 mm from the cutting point, it was the optimum location for sensing the cutting force signals.

Performance of uncoated and coated carbide tools in turning FCD700 using fem simulation

This paper presents investigations on the performance of uncoated and multi-layer coated carbide tools while turning ductile cast iron FCD700. Three different squares-edged carbide tools were used, namely TiN+TiCN+Al2O3+TiN-coated carbide tools, multi-layered hard coating of 5 and 10 μm thickness and an uncoated WC/Co tool. Deform-3D FEM software is utilised to predict the main cutting force, the sliding velocity, interface temperature and interface pressure and tool wear rate on these inserts. The results show that the main cutting forces obtained with multi-layer carbide tools are smaller than the uncoated carbide tool. The simulated results for cutting forces were validated experimentally, and the maximum error between the simulation and experimental results is 8.14 %. It was found that the coated carbide tool with the highest thickness is the most suitable for turning ductile cast iron at higher cutting speeds. (Received in February 2014, accepted in March 2015. This paper was with the authors 1 month for 2 revisions.)

I-kaz TM -Based Analysis of Cutting Force Signals for Tool Condition Monitoring in Turning Process

Cutting force is an important signal in machining process and has been widely used for tool condition monitoring. Monitoring the condition of the cutting tool in the machining process is very important to maintain the machined surface quality and consequently reduce inspection costs and increase productivity. This paper utilizes I-kaz-based analysis of cutting force signal to monitor the status of tool wear. The cutting force signals are measured by two channels of strain gauge that were mounted on the surface of tool holder. Experiments were carried out by turning hardened carbon steel and cutting force signals were analyzed using I-kazTM technique by integrating two component of signals (I-kaz 2D, Z2), I-kaz of cutting force (Z of Fy), and I-kaz of feed force (Z of Fx). The results show that I-kaz of feed force can be effectively used to monitor tool wear progression during turning operation.

Detection of cutting tool wear using statistical analysis and regression model

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical‐based method called Integrated Kurtosis‐based Algorithm for Z‐Filter technique, called I‐kaz was used for developing a regression model and 3D graphic presentation of I‐kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I‐kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I‐kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time to...