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Fused filament fabrication technology provides the opportunity to produce complex objects with sophisticated interior features. Here, extruding variable bead widths for desktop 3D printers is presented in order to improve resolution and to decrease the build time. In the proposed method, the cross-section is continuously varied according to the geometric features associated with the layers. That is, thinner beads can be utilized to print features with higher accuracy such as the outer surfaces, edges/corners, and features with finer details. Larger beads could be employed to cover interior regions where the accuracy is not an important concern. In this paper, a comprehensive review on nozzles with variable bead widths is given. Then, new orientation-controlled extruder designs with rectangular orifice, which focuses on controlling the filament flow inside the nozzle to determine variable bead widths at the output of the nozzle, will be introduced. Numerical- and experimental analysis will be conducted to show comparatively the performance of the presented design. The repeatability of this process is also evaluated for nominal bead widths. The studies show that there is a good agreement between numerical and experimental results. The paper will be concluded with key points on the advantages of these proposed nozzle design.

Additive Manufacturing (AM) facilitates the production of intricate objects despite its weakness in attainable part quality and fabrication speed compared to the conventional manufacturing methods. To alleviate the problems arising as a natural outcome of AM methods, hybrid technologies become viable options by employing concurrent manufacturing procedures, e.g. synergetic additive and subtractive manufacturing (SM) actions. Hybrid workstations have recently opened up new dimensions to 3D-printing industry, but related research topics remain underexplored in the literature. In this study, a hybrid manufacturing simulator that imitates AM and SM operations is presented. The hybrid simulator reported aims to provide users with a comprehensive interpretation of the G-codes, thereby previews the AM/SM tool paths and the eventual shape of the fabricated object. Consequently, the simulator lets users realize the final form of the artifacts besides enabling them to notice possible problems on the manufacturing trajectories. In addition, a post-processor customized for multi-axis hybrid platforms is built for the generation of G-code files. As a significant feature of the proposed manufacturing simulator, the capability to handle variable bead-width on the additive infill path has also been addressed in the study.

This paper presents a novel method for generating image projections required for Digital Light Processing type 3D printer systems where the entire cross-section of the printed object is directly formed via projecting the image onto a vat of photopolymers. The cross-sections (i.e. slices), which are initially represented as bitmap images, are compressed via a novel lossless compression technique which makes good use of relative data encoding. The coherence between the consecutive slices (or images) are taken into account in this proposed paradigm. The method yields satisfactory performance in terms of data compaction achieved in the test cases considered. (c) 2017 The Authors. Published by Elsevier B.V.

The latest industrial revolution, Industry 4.0, is encouraging the integration of intelligent production systems and advanced information technologies. Additive manufacturing (AM) is considered to be an essential ingredient in this new movement. In this paper, a comprehensive review on AM technologies is presented together with both its contributions to Industry 4.0. The review focusses on three important aspects of AM: recent advances on material science, process development, and enhancements on design consideration. The main objective of the paper is to classify the current knowledge (and technological trends) on AM and to highlight its potential uses. (c) 2017 The Authors. Published by Elsevier B.V.

SUMMARYRecently, satellite images of most urban settings has become available on the internet. In this study, a novel mapping and global localization approach, which uses these images, is proposed for outdoor mobile robots operating in urban environment. The mapping of large-scale outdoor environments is done by employing the satellite images acquired by remote sensing technology, and then a map-based approach, that is, Monte Carlo localization is used for localization. The novelty of proposed method is that it uses standard equipment present on almost all autonomous robots and satellite images thus it acts as an alternative to GPS data in urban environments. Extensive field tests are presented to demonstrate the effectiveness of proposed approach.

This paper presents three recurrent neural networks to estimate the spectral content of (noisy) periodic waveforms that are common in many engineering processes. The presented (structured) networks, which are based on the recursive discrete Fourier transform, are especially useful in computing high-order derivaties of such waveforms. Unlike conventional differentiating techniques, the proposed networks perform differentiation in the frequency domain and thus are immune to uncorrelated measurement noise. Furthermore, due to moving-average based correlation scheme, which is inherent to the recursive transform, the presented networks can handle composite waveforms without a detailed signal model in the frequency domain. The performance of the proposed network architectures in a number of simulation and experimental cases has also been evaluated in this paper.

This paper presents a general methodology for designing neural networks by using a priori information on the process under study. The approach is especially useful in developing efficient neural networks for the applications where full-scale models are available but too complicated to implement on conventional computer systems. Traditional neural network development techniques are known to have considerable disadvantages, including a tedious (trial-and-error-based) design process and a long training phase for most real world problems. The presented network design paradigm overcomes these well-known shortcomings. The paper also illustrates the method's application to a real-world problem on the estimation of milling forces in an ideal machining process.

This work presents a methodology for designing neural networks to predict the behavior of nonlinear dynamical systems with the guidance of a priori knowledge on the physical systems. The traditional neural network development techniques are known to have considerable disadvantages including tedious design process, long training periods, and most notably convergence/stability problems for most real world applications. The presented approach, which circumvents such bottlenecks, is especially useful in developing efficient neural network models when full-scale models are not available. This study illustrates the application of the method on a highly nonlinear hydraulic servo-system so to estimate accurately the chamber pressures of its hydraulic piston in extended time periods.

Localization is one of the major research fields in mobile robotics. With the utilization of satellite images and Monte Carlo localization technique, the global localization of an outdoor mobile robot is studied in this paper. The proposed method employs satellite images downloaded from the Internet to localize the robot iteratively. To accomplish this, the proposed method matches the local laser scanner data with the segmented satellite images. Initial test results conducted on the METU campus are found to be quite promising. Further improvement of this approach has the potential of cutting down not only the operational costs but also the preparation period of the mobile robot enabling researchers to operate their robots in diverse outdoor settings.

The localization of mobile robots has been studied rigorously in the past. However, only a few studies have focused on developing specific Genetic Algorithms (GAs) to address the localization problem effectively. In this study; the global urban localization of an outdoor mobile platform is considered with the utilization of the odometer, the laser-rangeq finder measurements and the digital maps created from the relevant satellite images on the Internet. The localization issue is formulated as a constrained optimization problem. The study proposes a GA-based technique to solve the problem at hand efficiently.

Localization of mobile robots has been studied rigorously in the last decade. A number of successful approaches such as Extended Kalman Filter, Markov Localization, and Monte Carlo Localization assume that the map of the environment is originally presented to the robot. However, an important information package like the map of the environment could not be taken for granted in most real- world problems. In this study, a novel technique composed of a combination of Fuzzy C-Means and Fuzzy Neural Network methods is proposed to segment and convert a satellite image into a digital map for outdoor mobile robot applications.