Huseyin Metin Ertunc

Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)

Monitoring of tool wear condition for drilling is a very important economical consideration in automated manufacturing. Two techniques are proposed in this paper for the on-line identification of tool wear based on the measurement of cutting forces and power signals. These techniques use hidden Markov models (HMMs), commonly used in speech recognition. In the first method, bargraph monitoring of the HMM probabilities is used to track the progress of tool wear during the drilling operation. In the second method, sensor signals that correspond to various types of wear status, e.g., sharp, workable and dull, are classified using a multiple modeling method. Experimental results demonstrate the effectiveness of the proposed methods. Although this work focuses on on-line tool wear condition monitoring for drilling operations, the HMM monitoring techniques introduced in this paper can be applied to other cutting processes.

A decision fusion algorithm for tool wear condition monitoring in drilling

Tool wear monitoring of cutting tools is important for the automation of modern manufacturing systems. In this paper, several innovative monitoring methods for on-line tool wear condition monitoring in drilling operations are presented. Drilling is one of the most widely used manufacturing operations and monitoring techniques using measurements of force signals (thrust and torque) and power signals (spindle and servo) are developed in this paper. Two methods using Hidden Markov models, as well as several other methods that directly use force and power data are used to establish the health of a drilling tool in order to avoid catastrophic failure of the drill. In order to increase the reliability of these methods, a decision fusion center algorithm (DFCA) is proposed which combines the outputs of the individual methods to make a global decision about the wear status of the drill. Experimental results demonstrate the effectiveness of the proposed monitoring methods and the DFCA.

Real time monitoring of tool wear using multiple modeling method

Real time monitoring of tool wear in machining operations is very crucial in order to prevent tool failures, increase machine utilization and decrease production cost in an automated manufacturing environment. In general the price of the tool is relatively low, but the failure can cause incomparably higher production cost. Over the years, a wide variety of tool condition monitoring (TCM) techniques has been developed based on sensor signals such as cutting forces, acoustic emission and vibration. In this paper, a real time monitoring technique based on multiple modeling method is presented for drilling operations which is one of the most widely used manufacturing operations. The multiple modeling method utilizes cutting forces (thrust and torque) collected during the drilling operation and classifies these forces using hidden Markov models (HMM) to determine wear status of the drill bit. Experimental results have been presented in order to demonstrate the effectiveness of the proposed method.

A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm

In machining systems, the quality of the manufactured part is directly related to the condition of the tool used. Sharp tools are mostly used on the final machining pass to obtain enhanced dimensional accuracy and surface smoothness. Worn tools on the other hand are typically used for coarse machining. The operator usually makes tool assignments based on his experience, the wear levels of the tools and the type of machining task. However, this kind of operator judgment is bound to errors and may not be reliable in processes requiring high precision. Therefore, a tool condition monitoring system is highly desirable to achieve the best results in machining quality. In this study, three-axis cutting forces, torque, three-axis accelerometer and acoustic emission signals were analyzed and used for the development of an online tool condition monitoring system. Various time domain and statistical features extracted from these signals were used to train support vector machine models in a binary decision tree, which was used to predict the condition of the cutting tool. The genetic algorithm was employed for reducing the dimensionality of the feature set by selecting the features that correlates best with the tool condition. Nine experiments were carried out at different cutting conditions. Experimental results demonstrated the efficacy of the proposed scheme. The classification rates for the tool condition monitoring system before and after inclusion of the genetic algorithm step were determined as 89% and 100%, respectively.

Online Monitoring Of Tool Wear In Drilling and Milling By Multi-Sensor Neural Network Fusion

In manufacturing systems the detection of tool wear during cutting process is one of the most important considerations. In order to perform online tool condition monitoring (TCM) for different cutting conditions, a sensor-integration strategy with machining parameters is proposed. TCM systems are most frequently based on the research which attempts to correlate the condition of drilling and milling tools to the signals obtained from multiple sensors (namely, cutting forces, vibration, current and sound connected to a CNC machine). The aim of the proposed study is to create a TCM system that will lead to a more efficient and economical machining tool usage. The used system is capable of accurate tool wear monitoring in around 97% accuracy. Experimental results under different conditions have demonstrated that TCM can be implemented by using neural network.

CNC lastik kalıbı işleme makine tasarımı, imalatı ve özgün NC takım yolu oluşturulması

Bu calismada, basta lastik sektoru olmak uzere bir cok sektorde (ayakkabi taban kaliplarinin islenmesi, medikal sektorundeki protez imalati, havacilik, otomotiv, kuyumculuk sektoru vb.) kullanilan 5 eksen CNC prototip uretimi icin mekanik tasarim, matematiksel modelleme ve yazilim algoritmasi gerceklestirilmistir. Bu amacla ilk olarak 5 eksen CNC lastik kalibi desen makinesinin prototip tasarimi ve imalati yapilmistir. Calismadaki amac CNC tezgâhlari ile yazi ve desenlerin 3 boyutlu karmasik yuzeylere aktarilmasi isleminin gerceklestirilmesidir. Lastik yuzeyine karakterler ve desenlerin uygun formda aktarilmasi icin matematiksel donusum algoritmasi gelistirilmistir. Yazilim algoritmasinda elde edilen ciktilar kullanilarak C# derleyicisi ile kullanicilar icin bir arayuz tasarlanmistir. Algoritma ciktisi NC kodlari tasarlanan prototip makinede test edilmistir. Test sonuclarinda istenilen hassasiyette desen ve karakter isleme basari ile gerceklestirilmistir.

Fuzzy Logic Controller and PID Controller Design for Aircraft Pitch Control

The goal of this paper is to compare a PID controller and a Fuzzy Logic controller in terms of pitch control of an aircraft. Firstly, derivation of the mathematical model is introduced to define the dynamics of an aircraft. To inspect the performance of the controllers, the PID (Proportional-Integral-Derivative) and FLC (Fuzzy Logic Controller) is proposed for controlling the pitch angle of an aircraft. Simulation results are illustrated in the time domain. In the end, the performances of pitch control are looked into and examined with respect to evaluation criteria of step response so as to determine which control method exhibits better performance depending on pitch angle and pitch rate. It is determined thanks to simulation that FLC shows better performance than PID controller.