Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed.     

Modelling 3-D cutting dynamics with neural networks

Identification of 3-D cutting dynamics requires an expensive experimental set-up and complicated analysis. Recently, time series methods were used to model cutting dynamics. This approach allows a simpler experimental set-uup and estimates the discrete transfer functions used for simulation and/or calculation of frequency domain characteristics of the system. In this paper, the use of neural networks is proposed to model the 3-D cutting dynamics. Neural networks can be trained using the same experimental set-up used for the time series methods. However, several time series models (for different cutting speeds) can be represented with a single neural network, and cutting forces can be studied for varying cutting speed conditions. Also, four neural networks were used to store the frequency domain characteristics of the thrust direction cutting force. In this study, the estimation errors for the neural networks were less than 7% of the defined range (the difference between the maximum and minimum of the data).     

Joint forecasts of Dow Jones stocks under general multivariate loss function

When forecasts are assessed by a general loss (cost-of-error) function, the optimal point forecast is, in general, not the conditional mean, and depends on the conditional volatility—which, for stock returns, is time-varying. In order to provide forecasts of daily returns of 30 DJIA stocks under a general multivariate loss function, the following issues are addressed. We discuss what conditions define a multivariate loss function, and a simple class of such functions is proposed. Based on suitable combinations of univariate losses, the suggested multivariate functions are convenient for practical applications with many variables. To keep the computational aspect tractable, a flexible multivariate GARCH model is employed in estimating the conditional forecast distributions. The model easily copes with large number of series while allowing for skewness, fat tails, non-ellipticity, and tail dependence. Based on Engle’s DCC GARCH, it uses multivariate affine generalized hyperbolic distributions as conditional probability law, and the number of parameters to be estimated simultaneously does not depend on the number of series. The model is fitted using daily data from 2002 to 2007 (keeping data from 2008 for out-of-sample forecasts), and a bootstrap procedure is used to derive point forecasts under several multivariate loss functions of the proposed type.     

Min-degree constrained minimum spanning tree problem: New formulation via Miller–Tucker–Zemlin constraints

Given an undirected network with positive edge costs and a positive integer $d>2$, the minimum-degree constrained minimum spanning tree problem is the problem of finding a spanning tree with minimum total cost such that each non-leaf node in the tree has a degree of at least $d$. This problem is new to the literature while the related problem with upper bound constraints on degrees is well studied. Mixed-integer programs proposed for either type of problem is composed, in general, of a tree-defining part and a degree-enforcing part. In our formulation of the minimum-degree constrained minimum spanning tree problem, the tree-defining part is based on the Miller–Tucker–Zemlin constraints while the only earlier paper available in the literature on this problem uses single and multi-commodity flow-based formulations that are well studied for the case of upper degree constraints. We propose a new set of constraints for the degree-enforcing part that lead to significantly better solution times than earlier approaches when used in conjunction with Miller–Tucker–Zemlin constraints.     

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.     

Urban Sustainability Incentives for Residential Water Conservation: Adoption of Multiple High Efficiency Appliances

Effects of multiple types of water use efficiency appliances on long term water savings and water use trend shifts were analyzed. The study group included senior and low income families in the urban areas of Miami-Dade County, Florida, USA. The participants in the study group experienced continuous and significant water savings within 3 years of the implementation of the water conservation incentives. Water savings were observed at approximately 200 l per household per day, which is about 31 % reduction in household water demand in comparison to the average residential water demand within the County. The water use profile of participants showed noticeable shifts over time in water demand frequency curves toward lower water consumption rates. The cost-saving analysis showed that adoption of multiple water efficiency appliances contributed to the highest annual monetary savings (i.e., high water savings and moderate product costs). Future conservation program planning efforts should take both water savings and product cost into account in order to achieve the greatest benefits.     

A non-contact method for part-based process performance monitoring in end milling operations

Surface response to excitation (SuRE) method was originally developed for structural health monitoring (SHM) applications. SuRE was used to evaluate the performance of completed milling operations. The method generates surface waves on the plate and studies the spectrum changes at selected points to detect defects and change of compressive forces. In this study, the length, depth, and width of a slot were changed step by step. The surface of the aluminum plate was excited in the 20–400 kHz range with a piezoelectric element. A laser scanning vibrometer was used to monitor the vibrations at the predetermined grid points after the dimensions of the slot were changed methodically. The frequency spectrums of measured vibrations were calculated by using the Fast Fourier Transformation (FFT). The sums of the squares of the differences (SSD) of the spectrums were calculated to evaluate the change of the spectrums. The SuRE method was able to determine if the dimensions were changed in each case at all the selected points. The scanning laser vibrometer is not feasible to be used at the shop floor. However, the study demonstrated that a piezoelectric element attached to any of the grid points would be able to evaluate the completed machining process.     

Development of a decision support system for robot selection

With the availability of more different robot types and models along with their separate specifications, selecting the most appropriate robot is becoming more difficult and complicated for companies. Furthermore, a common set of robot selection criteria is not available for the decision makers. In this study, a two-phase robot selection decision support system, namely ROBSEL, is developed to help the decision makers in their robot selection decisions. In development of ROBSEL, an independent set of criteria is obtained first and arranged in the Fuzzy Analytical Hierarchy Process (FAHP) decision hierarchy. In the first elimination phase of the decision support system, the user obtains the feasible set of robots by providing limited values for the 15 requirements. ROBSEL, then, uses FAHP decision hierarchy to rank the feasible robots in the second phase. ROBSEL is illustrated and tested and several critical issues in its practical usage are explored in the paper. The applications of ROBSEL show that ROBSEL is a useful, practical and easy to use robot selection tool and improves robot selection decisions in the companies.     

Control of multivehicle systems in the presence of uncertain dynamics

In this paper, we present a cooperative control architecture for high-order multivehicle systems having non-identical nonlinear uncertain dynamics. The proposed methodology consists of a local cooperative controller and a vehicle-level controller for each vehicle. The former controller receives the relative output measurements of the neighbouring vehicles in order to solve a containment problem formulated on a leader–follower framework. Specifically, the leaders generate trajectories in which the vehicles (followers) converge to the convex hull formed by those of the leaders. For a special case with one leader, this controller synchronises the output of the vehicles with the output of the leader. The latter controller receives the internal-state measurements for suppressing the nonlinear uncertain dynamics of the vehicle by using a decentralised adaptive control approach. The interaction topology between vehicles is described by undirected graphs and extensions to directed graphs are further discussed. The stability and convergence properties of the proposed cooperative control architecture are analysed by using the results from linear algebra and the Lyapunov theory. Several numerical examples are provided to demonstrate the efficacy of the proposed cooperative control architecture.