Process control in CNC manufacturing for discrete components: A STEP-NC compliant framework

With today's highly competitive global manufacturing marketplace, the pressure for right-first-time manufacture has never been so high. New emerging data standards combined with machine data collection methods, such as in-process verification lead the way to a complete paradigm shift from the traditional manufacturing and inspection to intelligent networked process control. Low-level G and M codes offer very limited information on machine capabilities or work piece characteristics which consequently, results in no information being available on manufacturing processes, inspection plans and work piece attributes in terms of tolerances, etc. and design features to computer numerically controlled (CNC) machines. One solution to the aforementioned problems is using STEP-NC (ISO 14649) suite of standards, which aim to provide higher-level information for process control. In this paper, the authors provide a definition for process control in CNC manufacturing and identify the challenges in achieving process control in current CNC manufacturing scenario. The paper then introduces a STEP-compliant framework that makes use of self-learning algorithms that enable the manufacturing system to learn from previous data and results in eliminating the errors and consistently producing quality products. The framework relies on knowledge discovery methods such as data mining encapsulated in a process analyser to derive rules for corrective measures to control the manufacturing process. The design for the knowledge-based process analyser and the various process control mechanisms conclude the paper.     

A comparative analysis of efficiency and reliability of capacitive micro-switches with initially curved electrodes

Enhancement of both efficiency and reliability of MEMS structures has always been an interesting and even essential issue for research community. This paper provides a comparative investigation in this field focusing on the role of initially curved electrodes of capacitive micro-switches. Four models have been introduced by appliance of curved microbeams as either upper or lower electrodes of a capacitive MEMS switch, as well as the conventional base model with straight both electrodes. By introducing a mathematical model and appropriate numerical procedure, the contact area between two electrodes, which has direct effect on the reliability has been estimated using Hertz relation for all models. The electromechanical coupling factor which is related to the efficiency of the switch has been calculated considering the stored mechanical and electrical energy of the system. The results have shown that by appliance of an initial curvature to the both electrodes, the estimated contact area can increase up to 279% compared to the conventional switches. Also, a switch with straight moveable electrode and curved substrate exhibits an increase in coupling factor up to 24% compared to the base model, while increasing the pull-in voltage of the switch.

     

On the implementation of adaptive sliding mode robust controller in the stabilization of electrically actuated micro-tunable capacitor

Microsystem Technologies - Parallel-plates based micro-tunable capacitors are known to have low travel ranges, which deteriorate as going even lower in terms of their initials gap sizes. Such...     

Multi-Modal Dialog Scene Detection Using Hidden Markov Models for Content-Based Multimedia Indexing

A class of audio-visual data (fiction entertainment: movies, TV series) is segmented into scenes, which contain dialogs, using a novel hidden Markov model-based (HMM) method. Each shot is classified using both audio track (via classification of speech, silence and music) and visual content (face and location information). The result of this shot-based classification is an audio-visual token to be used by the HMM state diagram to achieve scene analysis. After simulations with circular and left-to-right HMM topologies, it is observed that both are performing very good with multi-modal inputs. Moreover, for circular topology, the comparisons between different training and observation sets show that audio and face information together gives the most consistent results among different observation sets.     

Smartphone based sperm counting - an alternative way to the visual assessment technique in sperm concentration analysis

Sperm Counting is the first phase of the infertility diagnosis. Computer Aided Sperm Analysis (CASA) and Visual Assessment (VA) are two evaluation techniques employed in analyses. VA is carried out by observing sperm on counting chambers. Therefore, diagnosis strongly depends on the skills and experiences. CASA isolates the human factor by utilizing the computer based techniques. However, it is more costly than VA and requires exhausted parameter settings. In this study, we present a novel approach that uses smartphone and computer for sperm counting analysis. Smartphone is used to obtain images similar to VA technique. Then, sample videos are transferred to the computer side where we developed the Computerized Sperm Counting Software (CSCS) to count the sperm using counting chambers and eliminate human effects. CSCS consists of four modules: (1) Data Acquisition and Organization, (2) Regions of Interest (ROI) detection, (3) Motile/Immotile Sperm Detection, (4) Counting. Smartphone based data acquisition provided less costly design contrary to CASA systems. ROI extraction was realized by a combinational approach of line detection and segmentation methods. Background and Foreground extractions were employed in immotile and motile sperm detection process, respectively. Additionally, active contour was implemented to enhance the segmentation of immotile sperm. As the final step, detected sperms were counted by pixel based blob analysis. According to experimental results, the proposed smartphone based sperm concentration analysis can be adapted in laboratories due to its modularity, functionality, accuracy and cost when compare to CASA and VA based sperm counting analysis.

     

Signal-adaptive discrete evolutionary transform as a sparse time–frequency representation

Discrete evolutionary transform (DET) has usually been applied to signals in a blind-way without using any parameters to characterize the signal. For this reason, it is not optimal and needs improvement by using some information about the signal. In this paper, we propose an improvement for the discrete evolutionary transform in order to obtain a sparse representation and redefine the generalized time-bandwidth product optimal short-time Fourier transform as a special case of it. In case of linear FM-type signals, the optimized kernel function of the transform is determined according to signal parameters including the instantaneous frequency. The performance of the adaptive-DET is illustrated on three distinct cases. In case of multi-component LFM signals, when the concentration of the proposed distribution is compared to the ordinary sinusoidal-DET, the improvement is computed as 28% in terms of the ratio of norms. Furthermore we define a new and a general class of distribution functions named as the short-time generalized discrete Fourier transform (ST-GDFT) which is a larger set of signal representations including the adaptive-DET.     

The Implementation of Optimization Methods for Contrast Enhancement

The performances of the multivariate techniques are directly related to the variable selection process, which is time consuming and requires resources for testing each possible parameter to achieve the best results. Therefore, optimization methods for variable selection process have been proposed in the literature to find the optimal solution in short time by using less system resources. Contrast enhancement is the one of the most important and the parameter dependent image enhancement technique. In this study, two optimization methods are employed for the variable selection for the contrast enhancement technique. Particle swarm optimization (PSO) and artificial bee colony (ABC) optimization methods are implemented to the histogram stretching technique in parameter selection process. The results of the optimized histogram stretching technique are compared with one of the parameter independent contrast enhancement technique; histogram equalization. The results show that the performance of the optimized histogram stretching is better not only in distorted images but also in original images. Histogram equalization degraded the original images while the optimized histogram stretching has no effect due to being an adaptive solution.     

CFD simulation of free-surface flow over triangular labyrinth side weir

Side weirs are extensively used in the hydraulic and environmental engineering applications. The modeling of free surface flow over a labyrinth side weir is a sophisticated problem in the hydraulic engineering. The water surface profiles over the triangular labyrinth side weirs were investigated by many of the researchers experimentally and theoretically. In this study, the free surface flow over the triangular labyrinth side weir was modeled by using Volume of Fluids (VOF) method to describe the flow characteristics in subcritical flow conditions. A valid method, Grid Convergence Index (GCI) was used to determine the numerical uncertainty of the simulation results. The simulation results were compared with experimental observations, and good agreements were obtained between the both results.     

Coordinating metaheuristic agents with swarm intelligence

Coordination of multi agent systems remains as a problem since there is no prominent method suggests any universal solution. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems using metaheuristic algorithms. An idea for coordinating metaheuristic agents borrowed from swarm intelligence is introduced in this paper. This swarm intelligence-based coordination framework has been implemented as swarms of simulated annealing agents collaborated with particle swarm optimization for multidimensional knapsack problem. A comparative performance analysis is also reported highlighting that the implementation has produced much better results than the previous works.     

Using an artificial bee colony algorithm for the optimal placement of viscous dampers in planar building frames

In this study, an Artificial Bee Colony Algorithm (ABCA) is used to obtain the optimal size and location of viscous dampers in planar buildings to reduce the damage to the frame systems during an earthquake. The transfer function amplitude of the top displacement and the elastic base shear force evaluated at the first natural circular frequency of structures are chosen as objective functions. The damper coefficients of the added viscous dampers are taken into consideration as design variables in a planar building frame. Transfer function amplitude of the top displacement and the amplitude of the elastic base shear force at the fundamental natural frequency are minimized under an active constraint on sum of the damper coefficients of the added dampers. According to two specified objective functions, an optimization algorithm based on the ABCA is proposed. The proposed method is verified by a gradient-based algorithm; steepest direction search algorithm (SDSA). The proposed ABCA and the SDSA are applied to find the optimal damper distribution for a nine-storey planar building then the optimal damper allocation obtained from the ABCA is investigated to rehabilitate models of irregular planar buildings. The validity of the proposed method was demonstrated through a time history analysis of the optimal damper designs, which were determined based on the frequency domain using the ABCA. The numerical results of the proposed optimal damper design method show that the use of the ABCA can be a practical and powerful tool to determine the optimal damper allocation in planar building structures.