Micro-end-milling-I. Wear and breakage

Unpredictable tool life and premature tool failure are major problems in micro-machining. In this study, the failure mechanisms of micro-end-mills were studied during the machining of aluminum, graphite electrodes and mild steel workpieces. Hundreds of machining operations were performed, and the pictures of cutting edges were taken with a scanning electron microscope to identify fatigue and extensive stress-related failure mechanisms. Also, the cutting force variation was monitored, i.e. the relationship between the utilization-related changes at the tool structure (wear), and the outcomes (increasing cutting force which means raising stress on the tiny shaft). Inspection of the cutting force variation patterns of large numbers of micro-end-mills indicated that tool failure occurs with chip clogging, fatigue and wear-related excessive stress depending on the characteristics of the workpiece. Two tool breakage prediction methods were developed by considering the variation of the static part of the feed direction cutting force. These methods used segmental averages and wavelet transformation coefficients. The accuracy of the proposed approaches were tested with experimental data and the agreement between the predictions and actual observations are reported.     

Adding time dimension to relational model and extending relational algebra

A methodology for adding the time dimension to the relational model is proposed and relational algebra is extended for this purpose. We propose time-stamping attributes instead of adding time to tuples. Each attribute value is stored along with a time interval over which it is valid. Non-first normal form realations are used. A relation can have atomic, set-valued, triplet-valued, or set triplet-valued attributes. The last two types of attributes preserve the time (history). Furthermore, new algebraic operations are defined to extract information from historical relations. These operations convert one attribute type to another and do selection over the time dimension. Algebraic rules and identities for the new operations are also included.     

Recognition of chatter with neural networks

Chatter deteriorates surface finish, reduces tool life, and damages machine tools. A chatter development prediction procedure is proposed for the cylindrical turning of long slender bars. The procedure uses two synthetically trained neural networks to recognize the harmonic acceleration signals and their frequency, and based on these observations, the future vibration characteristics of the system are estimated. The developed neural networks are capable of identifying 98% of the harmonic signals with over 90% certainty and estimate their frequencies with less than ±5% error from very short data sequences (only 11 sampled points). The accuracy of the neural networks is equivalent to time domain time series method based approaches; however, the proposed procedure can be implemented very quickly by using commercially available neural network hardware and software, and can use the new neural network chips to make the estimations very quickly by using parallel processors. The validity of the chatter prediction procedure is also demonstrated on the experimental data.     

Robust parallel hybrid artificial bee colony algorithms for the multi-dimensional numerical optimization

The Journal of Supercomputing - This study proposes a set of new robust parallel hybrid metaheuristic algorithms based on artificial bee colony (ABC) and teaching learning-based optimization (TLBO)...     

Probabilistic estimation of the reachable set of model reference adaptive controllers using the scenario approach

A fundamental and critical problem for Model Reference Adaptive Control (MRAC) systems is the characterisation of the system response during transients. This problem is strictly related to the estimation of the reachable set (RS) from a fixed set of initial conditions and it is typically tackled using the Lyapunov's direct method. One well-known drawback of this approach is the excessive conservatism in the estimation of the RS. To overcome this limitation the authors propose a novel probabilistic framework where uncertain parameters and control signals are considered as random variables. In this framework the RS design is translated into a stochastic convex optimisation problem. This brings the benefit that (probabilistic) LMIs with reduced conservatism can be worked out. The so-called scenario optimisation approach is then used to solve the stochastic optimisation problem with a-priori specified level of reliability. The novel approach is compared with an existing worst-case approach in determining the RS of MRAC systems in the presence of matched and input uncertainty via simulation studies. The proposed methodology can potentially be a useful tool for the probabilistic analysis and design of a broad category of existing adaptive control systems.     

Development of a new multi-criteria optimization method for engineering design problems

In this study, we developed a new multiple criteria optimization method in the context of engineering design. The design of experiment (DoE) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods are combined to identify an objective function of the selected problem by fitting a polynomial to the experimental data in a multiple linear regression analysis. Then, the regression function is incorporated into a mathematical model with the criteria constraints to determine an optimal criteria set. The proposed method can be used to compare the functionality and results provided by different scenario analyses of a building design optimization problem, which represent design solutions. We applied the DoE–TOPSIS model to solve different multi-criteria design optimization problems using two examples from the literature and obtained satisfactory results. In the examples, the results obtained using the combined DoE–TOPSIS model are almost in agreement with those derived from conventional multi-criteria design optimization methods, which demonstrates the simplicity, usability, and flexibility of the proposed method in solving engineering design problems.     

A new model reference control architecture: Stability,performance, and robustness

In this paper, we develop a new model reference control architecture to effectively suppress system uncertainties and achieve a guaranteed transient and steady‐state system performance. Unlike traditional robust control frameworks, only a parameterization of the system uncertainty given by unknown weights with known conservative bounds is needed to stabilize uncertain dynamical systems with predictable system performance. In addition, the proposed architecture's performance is not dependent on the level of conservatism of the bounds of system uncertainty. Following the same train of thought as adaptive controllers that modify a given reference system to improve system performance, the proposed method is inspired by a recently developed command governor theory that minimizes the effect of system uncertainty by augmenting the input signal of the uncertain dynamical and reference systems. Specifically, a dynamical system, called a command governor, is designed such that its output is used to modify the input of both the controlled uncertain dynamical and reference systems. It is theoretically shown that if the command governor design parameter is judiciously selected, then the controlled system approximates the given original, unmodified reference system. The proposed approach is advantageous over model reference adaptive control approaches because linearity of the uncertain dynamical system is preserved through linear control laws, and hence, the closed‐loop performance is predictable for different command spectrums. Additionally, it is shown that the architecture can be modified for robustness improvements with respect to high frequency content due to, for example, measurement noise. Modifications can also be made in order to accommodate actuator dynamics and retain closed‐loop stability and predictable performance. The main contribution of this paper is the rigorous analysis of the stability and performance of a system utilizing the command governor framework. A numerical example is provided to illustrate the effectiveness of the proposed architecture. Copyright © 2015 John Wiley & Sons, Ltd.     

A Novel Nonlinear Acoustic Health Monitoring Approach for Detecting Loose Bolts

To date, sensors have been the inevitable component of structural health monitoring (SHM) systems. Typically, sensory signals are digitized, processed by computers, and then the information is presented to the operator with plots or warnings depending on the sophistication of the system. This study proposes a novel nonlinear acoustic health monitoring (NAHM) approach for detection of loose bolts, which can work with and without any sensors. The structure is excited with bitonal excitations, which their difference is in the audible range. When the bolts are well tightened, the structure remains silent. But, the structure creates audible sound or verbal warnings in the presence of one or more loose bolts. There is no need for sensor(s), A/D converters or computers between the operator and the structure. However, it is also possible to attach a piezoelectric sensor or to use a microphone/sound level meter for further analysis of the structure’s response. The feasibility of the concept was demonstrated by detecting the loose bolt in a bolted plate system. For demonstrating the industrial potential of the proposed NAHM system, the concept was implemented for two simple washers held with nuts and bolts. Additionally, the intensities of the audible alarms were studied at different torque levels. The proposed NAHM may be used as a low-cost sensor-free SHM or as a backup for conventional nonlinear SHM systems.     

An Efficient Algorithm for the Single Machine Total Tardiness Problem

This paper presents an exact algorithm for the single machine total tardiness problem (1// Σ T_i). We present a new synthesis of various results from the literature which leads to a compact and concise representation of job precedences, a simple optimality check, new decomposition theory, a new lower bound, and a check for presolved subproblems. These are integrated through the use of an equivalence concept that permits a continuous reformation of the data to permit early detection of optimality at the nodes of an enumeration tree. The overall effect is a significant reduction in the size of the search tree, CPU times, and storage requirements. The algorithm is capable of handling much larger problems (e.g., 500 jobs) than its predecessors in the literature (≤ 150). In addition, a simple modification of the algorithm gives a new heuristic which significantly outperforms the best known heuristics in the literature.     

Micro-end-milling-II. Extending tool life with a Smart Workpiece Holder (SWH)

A new procedure is proposed to extend tool life. The new procedure uses a Smart Workpiece Holder (SWH), which monitors the cutting force and reduces the metal removal rate when it predicts tool breakage possibility. The response includes a quick move in the opposite of the feed direction by using a piezo-electric actuator and by reducing the feed rate. The proposed system was tested with mild steel and aluminum workpieces. Based on the experimental results, better than 50% of the tool breakage cases could be predicted and tool life can be increased more than 30%.