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Despite the rapid growth in the past decade, the industrial adoption of additive manufacturing has not still been achieved due to certain limitations. A recent trend to alleviate its inherent drawbacks is to integrate additive manufacturing with secondary production techniques. Indeed, hybrid additive manufacturing solutions may help overcome the current barriers to today's production systems by making use of the combined merits of merging technologies. This paper presents a comprehensive review of the current state-of-the-art in the field, covering the following three key aspects of the subject: (i) advances in the hybridization of additive manufacturing processes, (ii) developments in process planning for integrated technologies, and (iii) insights into the hybrid additive manufacturing industry. The main objective of the paper is to classify the latest knowledge for the researchers, along with highlighting challenges and future research directions in this field.

A novel design and manufacturing pipeline for Additive Manufacturing is presented. The architecture of the pipeline is motivated by the observation that the conventional pipeline is unnecessarily complex. Most of the time, only a small set of programming steps suffices for 3D design and manufacture. In particular, the proposed method requires no complex hardware or software, and it generates the geometric data on the fly. This is demonstrated using a simplified evaluation of general volumetric sweeps. The method side-steps many of the problems of the conventional Additive Manufacturing pipeline. Several scripts are provided that illustrate the capabilities and the advantages of the proposed approach. These scripts are processed on a single-board computer and the parts are manufactured on a Fused Filament Fabrication type of 3D printer.

Çok amaçlı ve kısıtlı eniyileme problemleri için Pareto esasına dayalı çok amaçlı olurluğu arttırılmış parçacık sürü optimizasyonuyla (MOFEPSO) elde edilen çözümler ile bu problemlerin sabit ağırlıklı doğrusal fonksiyonlarla tek amaca indirgenen halleri için tek amaçlı eniyileme yaklaşımıyla bulunan çözülmeler karşılaştırılmıştır. İkisi aşırı kısıtlı olmak üzere toplam üç farklı problem için gerçekleştirilen karşılaştırmalarda amaç fonksiyonlarına önceden bir ağırlık verilmeksizin çok amaçlı bir yaklaşımla gerçekleştirilen eniyilemenin incelenen tüm tek amaçlı kıstaslar için de uygun sonuçlar verebildiği görülmüştür. Çok amaçlı yaklaşımla amaç fonksiyonlarına sonradan (eniyileme sonrasında) ağırlık verilebilir ve eniyileme koşusunun tekrarlanmasına gerek olmadan ağırlıklar değiştirilebilir.

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 new command generation technique titled VEPRO for computer controlled production machinery. In this method, the tool trajectory is described by a high-level scripting language that enables parametric representations of complex work-piece geometries. The interpreted script is then employed to generate the interpolation data required to compute a tool trajectory that is subjected to a number of kinematic constraints. A real-time interpolator is employed to provide the position commands required by each motion controller in a synchronous fashion. As a proof of concept, the proposed method is emulated on a PC using Python scripting language. The performance of the paradigm is rigorously assessed via two test cases involving different manufacturing techniques (e.g. pocket milling and 3D printing). Through the experimental results, the paper illustrates that the technique, which lends itself for real-time hardware implementation, exhibits satisfactory performance for all intensive purposes and that the method is technically feasible for a wide spectrum of manufacturing applications and systems. (C) 2017 Elsevier Ltd. All rights reserved.

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.

Constrained optimization problems constitute an important fraction of optimization problems in the mechanical engineering domain. It is not uncommon for these problems to be highly-constrained where a specialized approach that aims to improve constraint satisfaction level of the whole population as well as finding the optimum is deemed useful especially when the objective functions are very costly. A new algorithm called Feasibility Enhanced Particle Swarm Optimization (FEPSO), which treats feasible and infeasible particles differently, is introduced. Infeasible particles in FEPSO do not need to evaluate objective functions and fly only based on social attraction depending on a single violated constraint, called the activated constraint, which is selected at each iteration based on constraint priorities and flight occurs only along dimensions of the search space to which the activated constraint is sensitive. To ensure progressive improvement of constraint satisfaction, particles are not allowed to violate a satisfied constraint in FEPSO. The highly-constrained four-stage gear train problem and its two variants introduced in this paper are used to assess the effectiveness of FEPSO. The results suggest that FEPSO is effective and consistent in obtaining feasible points, finding good solutions, and improving the constraint satisfaction level of the swarm as a whole.

Curve offsets are utilized in different fields of engineering and science. Additive manufacturing, which lately becomes an explicit requirement in manufacturing industry, utilizes curve offsets widely. One of the necessities of offsetting is for scaling which is required if there is shrinkage after the fabrication or if the surface quality of the resulting part is unacceptable. Therefore, some post-processing is indispensable. But the major application of curve offsets in additive manufacturing processes is for generating head trajectories. In a point-wise AM process, a correct tool-path in each layer can reduce lots of costs and increase the surface quality of the fabricated parts. In this study, different curve offset generation algorithms are analyzed to show their capabilities and disadvantages through some test cases and improvements on their drawbacks are suggested.

This article introduces a new method entitled multi-objective feasibility enhanced partical swarm optimization (MOFEPSO), to handle highly-constrained multi-objective optimization problems. MOFEPSO, which is based on the particle swarm optimization technique, employs repositories of non-dominated and feasible positions (or solutions) to guide feasible particle flight. Unlike its counterparts, MOFEPSO does not require any feasible solutions in the initialized swarm. Additionally, objective functions are not assessed for infeasible particles. Such particles can only fly along sensitive directions, and particles are not allowed to move to a position where any previously satisfied constraints become violated. These unique features help MOFEPSO gradually increase the overall feasibility of the swarm and to finally attain the optimal solution. In this study, multi-objective versions of a classical gear-train optimization problem are also described. For the given problems, the article comparatively evaluates the performance of MOFEPSO against several popular optimization algorithms found in the literature.

Two-dimensional curve offsets have a wide application area ranging from manufacturing to medical imaging. To that end, this paper concentrates on two novel techniques to produce planar curve offsets. Both methods, which are based on mathematical morphology, employ the concept that the boundaries formed by a circular structuring element whose center moves across the points on a base curve comprise the entire offsets of the progenitor. The first technique titled IMOBS was introduced in our former paper and was shown to have superior properties in terms of its high accuracy, low computational complexity, and its ability to handle complex curves if compared to the techniques available in the literature. Consequently, an all-purpose algorithm titled AMOBS is introduced to enhance further the performance of the former technique by making good use of gradient information to find globally the most suitable candidate points in the boundary data set via grid search techniques. Thus, the new paradigm is demonstrated to overcome some of the problems (like orphan curve offsets) encountered in extreme cases. Both algorithms, which have similar attributes in terms of run-time complexity and memory cost, are comparatively tested via two experimental cases where most CAD/CAM packages fail to yield acceptable results.

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.

This paper presents a study to predict the pressures in the cylinder chambers of a variable-speed pump controlled hydraulic system using structured recurrent neural network topologies where the rotational speed of the pumps, the position and the average velocity of the hydraulic actuator are used as their inputs. The paper elaborates the properties of such networks in extended time periods through detailed simulation- and experimental studies where black-box modeling approaches generally fail to yield acceptable performance. As alternative estimation techniques, both linear- and extended Kalman filters are considered in this paper. The estimation properties of the devised network models are comparatively evaluated and their potential application areas are discussed in detail.

For the purpose of generating 2D curve offsets used in 2.5D machining, four new methods based on morphological operations on different mathematical entities are presented in this paper. All of the methods, which lend themselves for parallel processing, exploit the idea that the boundaries formed by a circular structuring element whose center sweeps across the points on a generator/base curve comprise the entire offsets of the progenitor. The first approach, which is a carry-over from image processing, makes good use of morphological operations on binary images to produce 2D offsets via contour tracing algorithms. The second method, which is to rectify the high memory cost associated with the former technique, utilizes morphological operations on (boundary data) sets. The implementation of this basic technique is illustrated by two Matlab functions given in the paper. Despite its simplicity, the time complexity of this paradigm is found to be high. Consequently, the third method, which is evolved from the preceding one, reduces the time complexity significantly with the utilization of a geometric range search method. This technique, which has a considerable margin for improvement, is found to be suitable to be used as a part of the real-time motion command generator for CNC applications. Unlike the previous schemes, the final approach uses polygon operations to generate such curves. The run-time of this technique is highly governed by the complexity of the polygon overlay algorithm selected. The paper analyzes the complexity of each technique. Finally, the presented methods are evaluated (in terms of run-time and geometric accuracy) via two test cases where most CAD/CAM packages fail to yield acceptable results.

This study focuses on the slip prediction in a cable-drum system using artificial neural networks for the prospect of developing linear motion sensing scheme for such mechanisms. Both feed-forward and recurrent-type artificial neural network architectures are considered to capture the slip dynamics of cable-drum mechanisms. In the article, the network development is presented in a progressive (step-by-step) fashion for the purpose of not only making the design process transparent to the readers but also highlighting the corresponding challenges associated with the design phase (i.e. selection of architecture, network size, training process parameters, etc.). Prediction performances of the devised networks are evaluated rigorously via an experimental study. Finally, a structured neural network, which embodies the network with the best prediction performance, is further developed to overcome the drift observed at low velocity. The study illustrates that the resulting structured neural network could predict the slip in the mechanism within an error band of 100 µm when an absolute reference is utilized.

The main goal of this paper is to predict the chamber pressures in hydraulic cylinder of a servo-valve controlled hydraulic system accurately using advanced modeling tools like artificial neural networks. After showing that the black-box modeling approaches are not sufficient for long-term prediction of pressures, a structured neural network model is proposed to capture the pressure dynamics of this inherently nonlinear system. The paper shows that the proposed network model could be easily trained to predict the pressure dynamics of an experimental hydraulic test setup provided that the training session is initiated with the weights of the developed model.

A novel touch-trigger probe system is proposed in this paper. The probe houses the stylus shaft on a special diaphragm spring that is exclusively designed with the utilization of Finite Element Analysis (FEA). In the probe system, Giant Magneto-Resistive (GMR) sensors are employed to measure the motion of the stylus shaft in the principal directions. The proposed (and implemented) probe is tested on a three-axis CNC electrical discharge machine (EDM) and the results acquired from those experiments are discussed. The preliminary work presented in this paper demonstrates the capabilities of GMR based probing technology in dimensional metrology.

In this study, novel solutions to Global Urban Localization problem is proposed and examined rigorously. Classical approaches including Particle Filter, mixture of Gaussians, as well as novel solutions like Viterbi Algorithm and differential evolution are evaluated. The contribution of this paper is twofold: The Viterbi algorithm is extended by exploiting the structure of the problem at hand that is the states are partially connected temporally. Differential evolution is modified by taking into account the covariance matrix of states. Thus states encoded in genes are only allowed to interact locally within the region described by covariance matrix. This prevents the differential evolution from getting trapped into false maxima in the early stages of optimization. Finally, it is demonstrated with extensive experiments that solution of Global Urban Localization problem is possible.

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 study presents a cutting force estimator topology for feed drives of CNC vertical machining centers to compute the machining forces accurately. The estimator employs recursive discrete Fourier transform to not only estimate inertial forces on the system but also to filter effectively the noise components in the measurements. The accuracy of the estimator is compared to that of a Luenberger observer while the overall performance of the estimator is evaluated through an experimental study. The paper also discusses its inherent limitations.

This paper focuses on the problem of estimating the unknown principal cutting force component with the available knowledge on the other component. The paper presents two compact (end-milling) process models. Using these models, two cross-reference models, which explore the functional relationships among the free parameters of these models, are developed. Within the framework of constructing an unknown force component, the estimation performance of the proposed models along with their Inherent limitations are assessed through rigorous experimental study in this paper

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 paper focuses on viable position estimation schemes for timing belt drives where the position of the carriage (load) is to be determined via reference models receiving input from a position sensor attached to the actuator of the timing belt. A detailed analysis of the transmission error sources is presented, and a number of relevant mathematical models are developed using a priori knowledge of the process. This paper demonstrates that such schemes are very effective when the drive system is not subjected to external loads and operating conditions do not change considerably i.e. ambient temperature, belt tension.

As a part of a “lab-at-home” education paradigm for control engineering courses, this paper proposes (and elaborates) a novel hardware-in-the-loop simulator with 3D animation capabilities. The developed software, which can be tailored to simulate any dynamic systems in non-real-time, is designed to work in conjunction with a control hardware. In the paper, the specific application of the software to a graduate-level course is presented within the framework of a final term project involving the control of a satellite tracking antenna. The success of the software (along with the methodology) is rigorously evaluated through the information collected in three academic semesters including the course instructor's feedback and the questionnaires filled out by the students.

This study focuses on an integrated software and hardware platform that is capable of performing (real-time/non-real-time) hardware-in-the-loop simulation of dynamic systems, including electrical machinery, CNC machine tools. In this approach, once the dynamics of the plant to be controlled is defined via C++ language, the resulting code is cross-compiled automatically on a PC. Executable files along with the necessary drivers are downloaded onto the composite hardware platform that consists of a Field Programmable Gate Array (FPGA) along with a powerful DSP board. The paper elaborates the overall performance of this novel hybrid HILS platform on a CNC machine tool application.

A novel measurement probe system is introduced in this paper. The stylus shaft of the probe is mounted on a special diaphragm spring that is exclusively designed with the utilization of finite element analysis. In the probe system, Giant Magneto-Resistive (GMR) sensors are utilized to measure the motion of the stylus shaft in the fundamental directions. The proposed implemented probe is tested on a three-axis CNC electrical discharge machine and the position estimation performance of the proposed paradigm is discussed briefly. The preliminary work presented in this paper demonstrates the capabilities of GMR based probing technology in dimensional metrology.

This paper presents a direct command generation technique for digital motion control systems. In this paradigm, higher-order differences of a given trajectory (i.e. position) are calculated and the resulting sequence is compacted via data compression techniques. The overall method is capable of generating trajectory data at variable rates in forward- and reverse-directions with the utilization of a linear interpolator. As a part of the command generation scheme, the paper also proposes a new data compression technique titled Delta Y10. Apart from this new method, the performances of the proposed generator employing different compression algorithms (such as Huffman coding, Arithmetic coding, LZW, and run length encoding) are also evaluated through three test cases. The paper illustrates that the Delta Y10 technique, which is suitable for real-time hardware implementation, exhibits satisfactory performance in terms of data compaction achieved in the test cases considered. (C) 2012 Elsevier Ltd. All rights reserved.

This paper focuses on a viable position estimation scheme for timing-belt drives using artificial neural networks. In this study, the position of a carriage (load) is calculated via a structured neural network topology accepting input from a position sensor on the actuator side of the timing belt. The paper presents a detailed discussion on the source of transmission errors. The characteristics of the error in different operation regimes are exploited to construct different network topologies. That is, a relevant neural network model is developed by the sketchy guidance of a priori knowledge on the process. The resulting structured neural network is shown to estimate the error of the carriage quite accurately whereas generic recurrent neural networks fail to capture the dynamics of the system under investigation altogether. Extensive testing demonstrates the effectiveness of proposed method when the drive system is not subjected to external loads while the operating conditions such as ambient temperature and belt tensions do not deviate from the experimental conditions. (C) 2011 Elsevier Ltd. All rights reserved.

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.

This study evaluates cable-drum mechanisms as linear motion sensors for certain CNC applications. In this work, the dynamical attributes of a generic device are studied experimentally. The conducted research indicates that despite the significant traction force induced between the cable and its drum, small fluctuations in mechanism's speed yields a considerable (micro) slip at the interface.

This paper focuses on a direct command generation technique for Computer Numerical Control (CNC) machine systems. In this paradigm, higher-order differences of a given trajectory (i.e, position) are computed and the resulting data are compacted via data compression techniques. As a part of the command generation scheme, the paper also introduces a new data compression technique titled ΔY10. Apart from this new method, the performances of the proposed generator employing different compression algorithms (such as Huffman Coding, Arithmetic Coding, LZW, and ΔY10) are also assessed through a test case. The paper shows that the ΔY10 technique, which is suitable for real-time hardware implementation, exhibits much better performance than its counterparts in terms of data compaction achieved.

This paper focuses on a novel command generator for servo-motor drives to be used as an integral part of their motion controllers. The method, which incorporates a new data compression algorithm, is capable of generating trajectory data at variable rates. In this paradigm, higher-order differences of a given trajectory (i.e. position) are first computed and thus the resulting data are compressed via the proposed technique. The generation of the commands is carried out according to the feedrate (i.e. the speed along the trajectory) set by the external logic dynamically. The paper discusses the implementation of the method on a Field Programmable Gate Array (FPGA). During implementation Very High Speed Integrated Circuit Hardware Description Language (VHDL) is used rather than using embedded processors on the FPGA chip. The performance of the method is assessed according to the resources used in the FPGA chip on the development board and these results are also compared with the same approach without an interpolator.

Field Programmable Gate Arrays (FPGAs) are suitable choices for demanding real-time control applications including CNC machine tools, robotics, advanced automation, aviation, automotive systems. Logic-level design capabilities of FPGAs allow engineers to develop efficient yet flexible methods for motion control applications. In fact, certain properties of FPGAs (parallelism, layout management, logic optimization, etc.) can be exploited to reduce the resources used on the FPGA without sacrificing the performance. This paper focuses on this aspect and presents novel implementation methods for motion controllers using FPGAs. The presented methods are applied to a full-state space controller utilizing a Luenberger-type state observer. This controller topology, which can be easily tailored to any application, is implemented on an Altera Cyclone II FPGA chip utilizing the methods elaborated in the paper. Furthermore, the control performances of the resulting systems are investigated through a hardware in the loop simulation (HILS) of a nonlinear system (inverted pendulum) using MATLAB.

This paper presents a comprehensive comparison of several real-time motion controller topologies implemented on a field programmable gate array (FPGA). Controller topologies are selected as proportional-integral-derivative controller with command feedforward, sliding mode controller, fuzzy controller, and a hysteresis controller. Controllers and other necessary modules are developed using Verilog HDL and they are implemented on a ML505 development board with a Xilinx Virtex-5 FPGA chip. In order to take full advantage of FPGA and to provide a more accurate comparison, an (soft-core) embedded processor is not employed in the design. The developed modules, which include PWM generator, quadrature encoder decoder, velocity estimator, reference profile generator etc, are fully tailored for the application. To perform the necessary calculations for certain controller topologies, an open-core floating point unit (FPU) is also adopted to the design. The performances of the aforementioned controllers are rigorously evaluated via a hardware-in-the-loop simulation of a field-oriented induction motor system.

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.