The integrity of machining tools is important to maintain a high level of surface quality. The wear of the tool can lead to poor surface quality of the workpiece and even to damage of the machine. Furthermore, in some applications such as aeronautics and precision engineering, it is preferable to change the tool earlier rather than to loose the workpiece because of its high price compared to the tool's one. Thus, to maintain a high quality of the manufactured pieces, it is necessary to assess and predict the level of wear of the cutting tool. This can be done by using condition monitoring and prognostics. The aim is then to estimate and predict the amount of wear and calculate the remaining useful life (RUL) of the cutting tool. This paper presents a method for tool condition assessment and life prediction. The method is based on nonlinear feature reduction and support vector regression. The number of original features extracted from the monitoring signals is first reduced. These features are then used to learn nonlinear regression models to estimate and predict the level of wear. The method is applied on experimental data taken from a set of cuttings and simulation results are given. These results show that the proposed method is suitable for assessing the wear evolution of the cutting tools and predicting their RUL. This information can then be used by the operators to take appropriate maintenance actions.

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Health assessment and life prediction of cutting tools based on support vector regression

Background Bone fracture treatment usually involves restoring of the fractured parts to their initial position and immobilizing them until the healing takes place. Drilling of bone is common to produce hole for screw insertion to fix the fractured parts for immobilization. Orthopaedic drilling during surgical process causes increase in the bone temperature and forces which can cause osteonecrosis reducing the stability and strength of the fixation. Methods A comprehensive review of all the relevant investigations carried on bone drilling is conducted. The experimental method used, results obtained and the conclusions made by the various researchers are described and compared. Result Review suggests that the further improvement in the area of bone drilling is possible. The systematic review identified several consequential factors (drilling parameters and drill specifications) affecting bone drilling on which there no general agreement among investigators or are not adequately evaluated. These factors are highlighted and use of more advanced methods of drilling is accentuated. The use of more precise experimental set up which resembles the actual situation and the development of automated bone drilling system to minimize human error is addressed. Conclusion In this review, an attempt has been made to systematically organize the research investigations conducted on bone drilling. Methods of treatment of bone fracture, studies on the determination of the threshold for thermal osteonecrosis, studies on the parameters influencing bone drilling and methods of the temperature measurement used are reviewed and the future work for the further improvement of bone drilling process is highlighted.

Drilling of bone: A comprehensive review.

Reliability of prognostics and health management systems relies upon accurate understanding of critical components' degradation process to predict the remaining useful life (RUL). Traditionally, degradation process is represented in the form of physical or expert models. Such models require extensive experimentation and verification that are not always feasible. Another approach that builds up knowledge about the system degradation over the time from component sensor data is known as data driven. Data driven models, however, require that sufficient historical data have been collected. In this paper, a two phases data driven method for RUL prediction is presented. In the offline phase, the proposed method builds on finding variables that contain information about the degradation behavior using unsupervised variable selection method. Different health indicators (HIs) are constructed from the selected variables, which represent the degradation as a function of time, and saved in the offline database as reference models. In the online phase, the method finds the most similar offline HI, to the online HI, using k-nearest neighbors classifier to use it as a RUL predictor. The method finally estimates the degradation state using discrete Bayesian filter. The method is verified using battery and turbofan engine degradation simulation data acquired from NASA data repository. The results show the effectiveness of the method in predicting the RUL for both applications.

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Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction

Intelligent spindles are core components of the next-generation of intelligent/smart machine tools in the Industry 4.0 Era. The purpose of this paper is to clarify the concept of intelligent spindles and provide an in-depth review of the state-of-the-art of related technologies. A new integrated concept for intelligent spindles is proposed, followed by descriptions of required characteristics, key enabling technologies and expected intelligent functions. Relevant research that may be beneficial to the development of intelligent spindles is reviewed from six thrust areas, which include monitoring and control of tool condition, chatter, spindle collision, temperature/thermal error, spindle balance, and spindle health. Finally, current limitations and challenges are discussed, and future trends of intelligent spindles are prospected from various perspectives.

The concept and progress of intelligent spindles: A review

Recently, there has been a growing interest in using modular and reconfigurable robots (MRRs) in flexible automation to reduce labor and increase throughput. Moreover, the significant facilitation of repair and maintenance of MRRs has attracted manufacturers to respond to the increasing desire for new products and methods of production in today's competitive market. As a result, design and control of such systems have become major topics for investigation in recent years. This paper presents a novel design methodology of interval type-2 Takagi-Sugeno-Kang fuzzy logic controllers (IT2 TSK FLCs) for MRR manipulators with uncertain dynamic parameters. We develop a mathematical framework for the design of IT2 TSK FLCs for tracking purposes that can be effectively used in real-time applications. To verify the effectiveness of the proposed controller, experiments are performed on an MRR with two degrees of freedom, which exhibits dynamic coupling behavior. Results show that the developed controller can outperform some well-known linear and nonlinear controllers for different configurations. Therefore, the proposed structure can be adopted for the position control of MRRs with unknown dynamic parameters in trajectory-tracking applications.

Design of Novel Interval Type-2 Fuzzy Controllers for Modular and Reconfigurable Robots: Theory and Experiments

Cortical bone drilling and thermal osteonecrosis

Bone drilling causes an increase in bone temperature, and a temperature above 47°C is critical because it causes thermal bone necrosis. Thermal osteonecrosis is common with the drill diameter of ≥4.5 mm without cooling. The aim of this study was to determine the increase of bone temperature during drilling using newly contructed two-step and internally cooled drills. An experiment was set up according to a central composite design. An internally cooled drill (3.4 mm and 4.5 mm) and a two-step drill (2.5/3.4 and 3.4/4.5 mm) were used in combination with feed rates of (0.02, 0.04, 0.10, 0.16 and 0.18 mm/rev) and cutting speeds (1.18, 10.68, 33.61, 56.55 and 66.05 m/min) with and without cooling with water of 24°C. Bone temperatures were measured with thermocouples. Drilling was performed on pig diaphyses with a three-axis mini milling machine. Bone temperatures in all combinations of parameters with internal cooling were below the critical 47°C (p = 0.05). The highest temperatures were detected using a 4.5-mm drill (40.5°C). A statistically significant effect other than cooling was found with the drill diameter and feed. A drill diameter of 3.4 mm with internal cooling developed a maximum temperature of 38.5°C and without cooling 46.3°C. For the same conditions a drill with diameter of 4.5 mm reached temperatures of 40.5°C and 55.7°C, respectively. The effect of feed rate is inversely proportional to the increase in bone temperature. With the feed rate 0.16 mm/rev, temperature was below critical even using the 4.5-mm drill (46.4°C, p = 0.05). Using the 3.4-mm drill all temperatures were below critical (46.2°C, p = 0.05). The two-step drill compared to a standard drill with the same diameter did not show statistical differences in maximum bone temperatures for all combinations of parameters (p = 0.05). A two-step drill does not have any advantages over a standard twist drill of the same diameter. An internally cooled drill causes a significantly smaller increase of bone temperature during drilling with water of 24°C. An internally cooled drill is currently the 'ideal' drill for traumatology/orthopaedics because it produces the smallest increase in bone drilling temperature. If internal cooling is used the regulation of other drilling parameters is of no importance.

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Temperature changes during cortical bone drilling with a newly designed step drill and an internally cooled drill.

A new type of continuous hybrid tool wear estimator is proposed in this paper. It is structured in the form of two modules for classification and estimation. The classification module is designed by using an analytic fuzzy logic concept without a rule base. Thereby, it is possible to utilize fuzzy logic decision-making without any constraints in the number of tool wear features in order to enhance the module robustness and accuracy. The final estimated tool wear parameter value is obtained from the estimation module. It is structured by using a support vector machine nonlinear regression algorithm. The proposed estimator implies the usage of a larger number and various types of features, which is in line with the concept of a closer integration between machine tools and different types of sensors for tool condition monitoring.

Tool wear estimation using an analytic fuzzy classifier and support vector machines

Drill wear monitoring in cortical bone drilling.

Forecasting performances of feed-forward and recurrent neural networks (NN) trained with different learning algorithms are analyzed and compared using the Mackey-Glass nonlinear chaotic time series. This system is a known benchmark test whose elements are hard to predict. Multi-layer Perceptron NN was chosen as a feed-forward neural network because it is still the most commonly used network in financial forecasting models. It is compared with the modified version of the so-called Dynamic Multi-layer Perceptron NN characterized with a dynamic neuron model, i.e., Auto Regressive Moving Average filter built into the hidden layer neurons. Thus, every hidden layer neuron has the ability to process previous values of its own activity together with new input signals. The obtained results indicate satisfactory forecasting characteristics of both networks. However, recurrent NN was more accurate in practically all tests using less number of hidden layer neurons than the feed-forward NN. This study once again confirmed a great effectiveness and potential of dynamic neural networks in modeling and predicting highly nonlinear processes. Their application in the design of financial forecasting models is therefore most recommended.

/pdf/a-comparison-of-feed-forward-and-recurrent-neural-networks-1zqevooee1.pdf

Danko Brezak

Papers

Cortical bone drilling and thermal osteonecrosis

Temperature changes during cortical bone drilling with a newly designed step drill and an internally cooled drill.

Tool wear estimation using an analytic fuzzy classifier and support vector machines

Drill wear monitoring in cortical bone drilling.

A comparison of feed-forward and recurrent neural networks in time series forecasting