The chaotic characteristics of three dimensional cutting

Chatter is an important problem in turning operations. However, there are very few studies on severe chatter because of the difficulties related to data collection and the complexities associated with non-linear cutting dynamics. In this study, cylindrical turning of long slender bars was simulated at three different cutting speeds by using experimentally identified cutting and structural dynamics transfer functions. The simulations were prepared at various depths of cuts, and variations of the cutting forces and displacements were investigated. Visual inspection and the Lyapunov spectrum of the cutting force signals suggested the chaotic nature of the cutting force signals. Although the frequency of the displacement signal remained constant, the amplitude of the signal appeared to have a chaotic nature. The same characteristics were also observed on the experimental data. During the design of a chatter detection or suppression system, the chaotic nature of the force and displacement signals must be considered. The unpredictable nature of the signals may cause prediction errors.     

Estimation of the chatter zones of drilled holes by using s-transformation

S-transformation based on diagnostic of machining operations from displacement, velocity, torque, and force data is proposed. Damping ratio was calculated from the time-frequency-amplitude plots of the s-transformation. The values were called damping index by considering the influence of the window functions on the calculations. Variance of the damping index in a small band around the first natural frequency of the system was found an effective chatter detection tool. The s-transformation of all the signals considered had similar characteristics and the sharp drop of the variance of the damping index indicated chatter development.     

Identification of the prefailure phase in microdrilling operations using multiple sensors

The life of microdrills with a diameter of less than 1 mm is short and unpredictable. Estimation of tool wear and the prefailure phase are more difficult than conventional drilling operations since most special machine tools for microdrilling have stepping motors that create fluctuating forces which consequently produce vibration. In this paper, a new method is proposed for detection of the prefailure phase of microdrilling, just 0.2 to 1 s before breakage occurs. The system measures the thrust force and microdrill velocity by using a dynamometer and a laser vibrometer, respectively. Seven characteristic features of the signals are obtained by describing their shape and spike characteristics. Adaptive Resonance Theory (ART2)-based neural networks are used for interpretation of the signal features. The proposed system accurately classified all the cases studied when a vigilance parameter of 0.995 of the ART2-type neural network was selected.     

A Three-Dimensional Cellular Automata Model for Dendrite Growth with Various Crystallographic Orientations During Solidification

A three-dimensional (3-D) cellular automata (CA) model coupled with the finite-element (FE) method has been proposed to simulate dendrite growth with various crystallographic orientations during solidification. The model introduces a new tracking neighborhood method to resolve the mesh dependency caused by the cubic lattice in the CA model for simulating 3-D dendrite growth. The migration of the solid–liquid (SL) interface is associated with the dendritic preferential orientation and the driving force for the phase transition. The latter is obtained from a thermodynamics database. The local curvature and anisotropy of the surface energy are also incorporated to describe the growth kinetics of the SL interface. The solute transfer is calculated using the FE method. A numerical simulation has been performed on a Fe-1.5 wt pct C alloy. The grain morphologies with various crystallographic orientations and the solute distribution during isothermal solidification are studied and discussed. The simulation results are compared with analytical solutions and experimental results, which are in good agreement.     

Selection of optimal material and operating conditions in composite manufacturing. Part I: computational tool

The Genetically Optimized Neural Network System (GONNS) is proposed as a human-like decision-making tool for the selection of optimum composite material and operating conditions. Multiple neural networks represent the characteristics of the system after a training process and genetic algorithms find the optimum operating conditions. The error of the GONNS was found to be less than 1% when the neural networks-represented analytical functions and genetic algorithms were used to select the optimal conditions. The GONNS is very promising for many complex optimization problems when analytical equations are not available to represent the characteristics of the system.     

A case study on trace metals in surface sediments and dissolved inorganic nutrients in surface water of Olüdeniz Lagoon-Mediterranean, Turkey

Hydro-chemical parameters (salinity, temperature, pH, dissolved oxygen, nitrate, silicate and phosphate) in seawater and major trace metals (Al, Fe, Mn, Cr, V, Zn, Pb, Cu) in sediments were evaluated for the assessment of quality of seawater and sediments in very small lagoon in Mediterranean, Olüdeniz. Enrichment factors for metals in sediment were in the range of 1.62-8.09, comparable to crustal rock composition. For metals, comparison with literature data revealed relatively low metal concentrations for Olüdeniz sediments. Correlation analyses on the sediment metal data showed strong correlation in between Cr, Fe and Zn. Surface water salinity slightly decreases within the lagoon, indicating that limited fresh waters inflow to the lagoon. In October, the lagoon waters contained very low phosphate concentrations but measurable values of nitrate and silicate, yielding high NO(3)(-)/PO(4)(3-) ratios (90). Very low Chlorophyll-a (biomass indicator) concentrations measured in the lagoon suggest the phosphorus limitation of primary productivity.     

Selection of optimal cutting conditions by using GONNS

Machining conditions are optimized to minimize the production cost in conventional manufacturing. In specialized manufacturing applications, such as micro machining and mold making, achievement of specific goals may be the primary objective. The Genetically Optimized Neural Network System (GONNS) is proposed for the selection of optimal cutting conditions from the experimental data when analytical or empirical mathematical models are not available. GONNS uses Backpropagation (BP) type neural networks (NN) to represent the input and output relations of the considered system. Genetic Algorithm (GA) obtains the optimal operational condition by using the NNs. In this study, multiple NNs represented the relationship between the cutting conditions and machining-related variables. Performance of the GONNS was tested in two case studies. Optimal operating conditions were found in the first case study to keep the cutting forces in the desired range, while a merit criterion (metal removal rate) was maximized in micro-end-milling. Optimal operating conditions were calculated in the second case study to obtain the best possible compromise between the roughness of machined mold surfaces and the duration of finishing cut. To train the NNs, 81 mold parts were machined at different cutting conditions and inspected.     

Basic computational tools and mechanical hardware for torque-based diagnostic of machining operations

In the industry, only rotary dynamometers can be used for monitoring when multiple spindles are used in machining operations. The current commercial rotary dynamometers are bulky and expensive for most machining centers. The basic hardware and computational tools proposed are for a smaller, more cost effective Torque-based Machining Monitor (TbMM). The objective of the TbMM concept is to estimate the remaining tool life, detect chatter from the torque signal inside the proposed device, and communicate with the central computer only when problems arise. The remaining tool life estimation and chatter detection algorithms of the TbMM were developed by analyzing the experimental data collected by a commercial rotary dynamometer. The mechanical hardware of the TbMM was designed to generate voltage proportional to the cutting torque using a piezoelectric composite element. The remaining tool life was estimated from the standard deviation (or variance) of the torque signal. Teager-Kaiser algorithm (TKA) based procedure detected the chatter based on the frequency estimations only from four samples at a time. The accuracy and characteristics of the signal of the mechanical component of the TbMM were found satisfactory in the estimation of machining problems such as wear and chatter. The TbMM is a good choice particularly when multiple spindles work simultaneously on the same workpiece.     

Output feedback adaptive stabilization and command following for minimum phase dynamical systems with unmatched uncertainties and disturbances

In this article, we develop an output feedback adaptive control framework for continuous-time minimum phase multivariable dynamical systems for output stabilisation and command following. The approach is based on a nonminimal state-space realisation that generates an expanded set of states using the filtered inputs and filtered outputs and their derivatives of the original system. Specifically, a direct adaptive controller for the nonminimal state-space model is constructed using the expanded states of the nonminimal realisation and is shown to be effective for multi-input, multi-output linear dynamical systems with unmatched disturbances, unmatched uncertainties and unstable dynamics. The proposed adaptive control architecture requires only knowledge of the open-loop system's relative degree as well as a bound on the system's order. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach.     

Fault diagnosis on bottle filling plant using genetic-based neural network

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA–ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA–NN environment. It may also be used for training for selection of ANN configurations at various applications.