Machines

16 pages, 6379 KiB

Open AccessEditor’s ChoiceArticle

Working Speed Analysis of the Gear-Driven Dibbling Mechanism of a 2.6 kW Walking-Type Automatic Pepper Transplanter

by Md Zafar Iqbal, Md Nafiul Islam, Milon Chowdhury, Sumaiya Islam, Tusan Park, Yong-Joo Kim and Sun-Ok Chung

Machines 2021, 9(1), 6; https://doi.org/10.3390/machines9010006 - 11 Jan 2021

Cited by 18 | Viewed by 4463

Abstract

The development of an automatic walking-type pepper transplanter could be effective in improving the mechanization rate in pepper cultivation, where the dibbling mechanism plays a vital role and determines planting performance and efficiency. The objective of this research was to determine a suitable [...] Read more.

The development of an automatic walking-type pepper transplanter could be effective in improving the mechanization rate in pepper cultivation, where the dibbling mechanism plays a vital role and determines planting performance and efficiency. The objective of this research was to determine a suitable working speed for a gear-driven dibbling mechanism appropriate for a pepper transplanter, while considering agronomic transplanting requirements. The proposed dibbling mechanism consisted of two dibbling hoppers that simultaneously collected free-falling seedlings from the supply mechanism and dibbled them into soil. To enable the smooth collection and plantation of pepper seedlings, analysis was carried out via a mathematical working trajectory model of the dibbling mechanism, virtual prototype simulation, and validation tests, using a physical prototype. In the mathematical model analysis and simulation, a 300 mm/s forward speed of the transplanter and a 60 rpm rotational speed of the dibbling mechanism were preferable in terms of seedling uprightness and low mulch film damage. During the field test, transplanting was conducted at a 40 mm planting depth, using different forward speed levels. Seedlings were freely supplied to the hopper from a distance of 80 mm, and the success rate for deposition was 96.79%. A forward speed of 300 mm/s with transplanting speed of 120 seedlings/min was preferable in terms of achieving a high degree of seedling uprightness (90 ± 3.26), a low rate of misplanting (8.19%), a low damage area on mulch film (2341.95 ± 2.89 mm²), high uniformity of planting depth (39.74 ± 0.48 mm), and low power consumption (40.91 ± 0.97 W). Full article

(This article belongs to the Section Machines Testing and Maintenance)

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15 pages, 37237 KiB

Open AccessEditor’s ChoiceArticle

Surface Finishing of Zirconium Dioxide with Abrasive Brushing Tools

by Eckart Uhlmann and Anton Hoyer

Machines 2020, 8(4), 89; https://doi.org/10.3390/machines8040089 - 21 Dec 2020

Cited by 10 | Viewed by 2573

Abstract

Brushing with bonded abrasives is a finishing process which can be used for the surface improvement of various materials. Since the machining mechanisms of abrasive brushing processes are still largely unknown and little predating research was done on brushing ceramic workpieces, within the [...] Read more.

Brushing with bonded abrasives is a finishing process which can be used for the surface improvement of various materials. Since the machining mechanisms of abrasive brushing processes are still largely unknown and little predating research was done on brushing ceramic workpieces, within the scope of this work technological investigations were carried out on planar workpieces of MgO-PSZ (zirconium dioxide, ZrO₂) using brushing tools with bonded grains of polycrystalline diamond. The primary goal was the reduction of grinding-related surface defects under the preservation of surface roughness valleys and workpiece form. Based on microscopy and topography measurements, the grain size s_g and the brushing velocity v_b were found to have a considerable influence on the processing result. Furthermore, excessive tool wear was observed while brushing ceramics. Full article

(This article belongs to the Special Issue Advances and Trends in Non-conventional, Abrasive and Precision Machining)

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15 pages, 4044 KiB

Open AccessEditor’s ChoiceArticle

A Smart Stent for Monitoring Eventual Restenosis: Computational Fluid Dynamic and Finite Element Analysis in Descending Thoracic Aorta

by Betsy D. M. Chaparro-Rico, Fabio Sebastiano and Daniele Cafolla

Machines 2020, 8(4), 81; https://doi.org/10.3390/machines8040081 - 24 Nov 2020

Cited by 7 | Viewed by 3936

Abstract

Even though scientific studies of smart stents are extensive, current smart stents focus on pressure sensors. This paper presents a novel implantable biocompatible smart stent for monitoring eventual restenosis. The device is comprised of a metal mesh structure, a biocompatible and adaptable envelope, [...] Read more.

Even though scientific studies of smart stents are extensive, current smart stents focus on pressure sensors. This paper presents a novel implantable biocompatible smart stent for monitoring eventual restenosis. The device is comprised of a metal mesh structure, a biocompatible and adaptable envelope, and pair-operated ultrasonic sensors for restenosis monitoring through flow velocity. Aside from continuous monitoring of restenosis post-implantation, it is also important to evaluate whether the stent design itself causes complications such as restenosis or thrombosis. Therefore, computational fluid dynamic (CFD) analysis before and after stent implantation were carried out as well as finite element analysis (FEA). The proposed smart stent was put in the descending thoracic section of a virtually reconstructed aorta that comes from a computed tomography (CT) scan. Blood flow velocity showed that after stent implantation, there is not liquid retention or vortex generation. In addition, blood pressures after stent implantation were within the normal blood pressure values. The stress and the factor of safety (FOS) analysis showed that the stress values reached by the stent are very far from the yield strength limit of the materials and that the stent is stiff enough to support the applied loads exported from the CFD results. Full article

(This article belongs to the Special Issue Italian Advances on MMS)

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15 pages, 8335 KiB

Open AccessEditor’s ChoiceArticle

Gallium Nitride Inverter Design with Compatible Snubber Circuits for Implementing Wireless Charging of Electric Vehicle Batteries

by Fatemeh Rahmani, Payam Niknejad, Tanushree Agarwal and Mohammadreza Barzegaran

Machines 2020, 8(3), 56; https://doi.org/10.3390/machines8030056 - 15 Sep 2020

Cited by 10 | Viewed by 3908

Abstract

High-frequency wireless power transfer (WPT) technology provides superior compatibility in the alignment with various WPT standards. However, high-efficiency and compact single-phase power switching systems with ideal snubber circuits are required for maximum power transfer capability. This research aims to develop an inverter using [...] Read more.

High-frequency wireless power transfer (WPT) technology provides superior compatibility in the alignment with various WPT standards. However, high-efficiency and compact single-phase power switching systems with ideal snubber circuits are required for maximum power transfer capability. This research aims to develop an inverter using Gallium Nitride (GaN) power transistors, optimized RCD (resistor/capacitor/diode) snubber circuits, and gate drivers, each benefitting WPT technology by reducing the switching and conduction loss in charging electric vehicle batteries. A full-bridge GaN inverter was simulated and instituted as part of the wireless charging circuit design. The RCD circuits were adjusted by transferring maximum power from the power supply to the transmitter inductor. For verification of the simulated output, lab-scale experiments were implemented for two half-bridges controlled by gate drivers with corresponding snubber circuits. After authenticating the output results, the GaN inverter was tested with an input range of 30 V to deduce the success of charging electric vehicle batteries within an efficient time frame. The developed inverter, at 80 kHz frequency, was applied in place of a ready-to-use evaluation board, fully reducing less harmonic distortion and greatly increasing WPT system efficiency (~93%). In turn, the designed GaN inverter boasts considerable energy savings, resulting in a more cost-effective solution for manufacturers. Full article

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18 pages, 13110 KiB

Open AccessEditor’s ChoiceArticle

Studying the Effect of Working Conditions on WEDM Machining Performance of Super Alloy Inconel 617

by Stefan Dzionk and Mieczysław S. Siemiątkowski

Machines 2020, 8(3), 54; https://doi.org/10.3390/machines8030054 - 9 Sep 2020

Cited by 21 | Viewed by 3412

Abstract

Wire electrical discharge machining (WEDM) has been, for many years, a precise and efficient non-conventional manufacturing solution in various industrial applications, mostly involving the use of hard-to-machine materials like, among others, the Inconel super alloys. The focus of the present study is on [...] Read more.

Wire electrical discharge machining (WEDM) has been, for many years, a precise and efficient non-conventional manufacturing solution in various industrial applications, mostly involving the use of hard-to-machine materials like, among others, the Inconel super alloys. The focus of the present study is on exploring the effect of selected control parameters, including pulse duration, pulse-off time and the dielectric flow pressure on the WEDM process performance characteristics of Inconel 617 material, such as: volumetric material removal rate (MRR), the dimensional accuracy of cutting (reflected by the kerf width) and surface roughness (SR). The research experiment has been designed and carried out using the response surface methodology (RSM) accordingly with the Box–Behnken design scheme. The results of experiments derived in the form of a fitted regression model have been subjected to the analysis of variance (ANOVA) tests. Thus, the variable process parameters and the relevant interactions between them, characterized by a significant influence on the values of the derived output responses, could be explicitly determined. Full article

(This article belongs to the Special Issue Advances and Trends in Non-conventional, Abrasive and Precision Machining)

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20 pages, 5518 KiB

Open AccessEditor’s ChoiceArticle

Advanced Strategy of Speed Predictive Control for Nonlinear Synchronous Reluctance Motors

by Ahmed Farhan, Mohamed Abdelrahem, Christoph M. Hackl, Ralph Kennel, Adel Shaltout and Amr Saleh

Machines 2020, 8(3), 44; https://doi.org/10.3390/machines8030044 - 1 Aug 2020

Cited by 18 | Viewed by 3502

Abstract

To gain fast dynamic response, high performance, and good tracking capability, several control strategies have been applied to synchronous reluctance motors (SynRMs). In this paper, a nonlinear advanced strategy of speed predictive control (SPC) based on the finite control set model predictive control [...] Read more.

To gain fast dynamic response, high performance, and good tracking capability, several control strategies have been applied to synchronous reluctance motors (SynRMs). In this paper, a nonlinear advanced strategy of speed predictive control (SPC) based on the finite control set model predictive control (FCS-MPC) is proposed and simulated for nonlinear SynRMs. The SPC overcomes the limitation of the cascaded control structure of the common vector control by employing a novel strategy that considers all the electrical and mechanical variables in one control law through a new cost function to obtain the switching signals for the power converter. The SynRM flux maps are known based on finite element method (FEM) analysis to take into consideration the effect of the nonlinearity of the machine. To clear the proposed strategy features, a functional and qualitative comparison between the proposed SPC, field-oriented control (FOC) with an anti-windup scheme, and current predictive control (CPC) with outer PI speed control loop is presented. For simplicity, particle swarm optimization (PSO) is performed to tune all the unknown parameters of the control strategies. The comparison features include controller design, dynamic and steady-state behaviors. Simulation results are presented to investigate the benefits and limitations of the three control strategies. Finally, the proposed SPC, FOC, and CPC have their own merits, and all methods encounter the requirements of advanced high-performance drives. Full article

(This article belongs to the Special Issue Design and Control of Rotating Electrical Machines)

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16 pages, 4644 KiB

Open AccessEditor’s ChoiceReview

An Overview of Electric Machine Trends in Modern Electric Vehicles

by Emmanuel Agamloh, Annette von Jouanne and Alexandre Yokochi

Machines 2020, 8(2), 20; https://doi.org/10.3390/machines8020020 - 17 Apr 2020

Cited by 101 | Viewed by 30738

Abstract

Electric machines are critical components of the drivetrains of electric vehicles. Over the past few years the majority of traction drive systems have converged toward containing some form of a permanent magnet machine. There is increasing tendency toward the improvement of power density [...] Read more.

Electric machines are critical components of the drivetrains of electric vehicles. Over the past few years the majority of traction drive systems have converged toward containing some form of a permanent magnet machine. There is increasing tendency toward the improvement of power density and efficiency of traction machines, thereby giving rise to innovative designs and improvements of basic machine topologies and the emergence of new classes of machines. This paper provides an overview of present trends toward high specific power density machines for traction drive systems. The focus will be on current technology and the trends that are likely to be pursued in the near future to achieve the high specific power goals set for the industry. The paper discusses machines that are applied in both hybrid and battery electric drivetrains without distinction and does not discuss the associated power electronic inverters. Future electric machine trends that are likely to occur are also projected. Full article

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22 pages, 2573 KiB

Open AccessEditor’s ChoiceArticle

Electric Machine Design Tool for Permanent Magnet Synchronous Machines and Induction Machines

by Svenja Kalt, Jonathan Erhard and Markus Lienkamp

Machines 2020, 8(1), 15; https://doi.org/10.3390/machines8010015 - 24 Mar 2020

Cited by 21 | Viewed by 9508

Abstract

The rising mobility demand of today’s society leads to an increasing strain of noise and pollutant emissions on people and the environment. An increasing environmental awareness and the scarcity of fossil fuels are increasingly placing alternative-powered vehicles in the focus of politics, research [...] Read more.

The rising mobility demand of today’s society leads to an increasing strain of noise and pollutant emissions on people and the environment. An increasing environmental awareness and the scarcity of fossil fuels are increasingly placing alternative-powered vehicles in the focus of politics, research and development. Electric vehicles represent a promising solution to this problem. The electric machine represents a design control lever for the optimization of the electric powertrain with regard to efficiency, power, weight and size. Therefore, accurate and realistic machine design tools for the design of electric machines are becoming increasingly important. In this paper, the authors present an electric machine design tool for electric machines using MATLAB^® in order to enable an automated machine design. The electric machine design tool is published under an LGPL open source license. Full article

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18 pages, 5063 KiB

Open AccessEditor’s ChoiceArticle

Electromagnetic Analysis and Design Methodology for Permanent Magnet Motors Using MotorAnalysis-PM Software

by Vladimir Kuptsov, Poria Fajri, Andrzej Trzynadlowski, Guoliang Zhang and Salvador Magdaleno-Adame

Machines 2019, 7(4), 75; https://doi.org/10.3390/machines7040075 - 6 Dec 2019

Cited by 18 | Viewed by 10968

Abstract

This article presents a new and powerful freeware software called MotorAnalysis-PM and discusses its application in electromagnetic design and analysis of permanent magnet (PM) motors for the electric vehicle (EV) industry. This new PM motor software utilizes both finite element (FE) and analytical [...] Read more.

This article presents a new and powerful freeware software called MotorAnalysis-PM and discusses its application in electromagnetic design and analysis of permanent magnet (PM) motors for the electric vehicle (EV) industry. This new PM motor software utilizes both finite element (FE) and analytical methods to speed up the analysis and design process of PM motors significantly. The analysis and design methodology using MotorAnalysis-PM is presented and discussed for a 50 kW PM motor utilized in a commercial EV. To validate the accuracy of the software, the numerical results obtained from the PM motor design and analysis tool are compared with experimental results. The numerical and experimental results validate the flexibility of this software in achieving accurate motor design with short design times which is of great interest to EV and PM motor manufacturers. Full article

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19 pages, 3899 KiB

Open AccessEditor’s ChoiceArticle

Experimental Vibration Analysis of a Small Scale Vertical Wind Energy System for Residential Use

by Francesco Castellani, Davide Astolfi, Mauro Peppoloni, Francesco Natili, Daniele Buttà and Alexander Hirschl

Machines 2019, 7(2), 35; https://doi.org/10.3390/machines7020035 - 22 May 2019

Cited by 27 | Viewed by 9889

Abstract

In the recent years, distributed energy production has been one of the main research topics about renewable energies. The decentralization of electric production from wind resources raises the issues of reducing the size of generators, from the MW scale of industrial wind farm [...] Read more.

In the recent years, distributed energy production has been one of the main research topics about renewable energies. The decentralization of electric production from wind resources raises the issues of reducing the size of generators, from the MW scale of industrial wind farm turbines to the kW scale, and possibly employing them in urban areas, where the wind flow is complex and extremely turbulent because of the presence of buildings and obstacles. On these grounds, the use of small-scale vertical axis small wind turbines (VASWT) is a valid choice for on-site generation (OSG), considering their low sensitivity with respect to turbulent flow and that there is no need to align the turbine with wind direction, as occurs with horizontal axis small wind turbines (HASWT). In addition, VASWTs have a minor acoustic impact with respect to HASWTs. The aim of this paper is to study the interactions that take place between a 1.2 kW, vertical axis, Darrieus VASWT turbine and a small, experimental building, in order to analyze the noise and the vibrations transmitted to the structure. One method to damp the vibrations is then assessed through spectral analysis of data acquired through accelerometers located both in the mast of the wind turbine and at the building walls. The results confirm the usefulness of dampers to increase the building comfort regarding vibrations. Full article

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14 pages, 3752 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

by Sivapong Nilwong, Delowar Hossain, Shin-ichiro Kaneko and Genci Capi

Machines 2019, 7(2), 25; https://doi.org/10.3390/machines7020025 - 18 Apr 2019

Cited by 41 | Viewed by 6915

Abstract

Outdoor mobile robot applications generally implement Global Positioning Systems (GPS) for localization tasks. However, GPS accuracy in outdoor localization has less accuracy in different environmental conditions. This paper presents two outdoor localization methods based on deep learning and landmark detection. The first localization [...] Read more.

Outdoor mobile robot applications generally implement Global Positioning Systems (GPS) for localization tasks. However, GPS accuracy in outdoor localization has less accuracy in different environmental conditions. This paper presents two outdoor localization methods based on deep learning and landmark detection. The first localization method is based on the Faster Regional-Convolutional Neural Network (Faster R-CNN) landmark detection in the captured image. Then, a feedforward neural network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The second localization employs a single convolutional neural network (CNN) to determine location and compass orientation from the whole image. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The experimental results pointed both presented localization methods to be promising alternatives to GPS for outdoor localization. Full article

(This article belongs to the Special Issue Selected Papers from Conference on Control, Mechatronics and Automation (ICCMA))

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14 pages, 522 KiB

Open AccessEditor’s ChoiceArticle

Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot

by Yiwei Fu, Devesh K. Jha, Zeyu Zhang, Zhenyuan Yuan and Asok Ray

Machines 2019, 7(2), 24; https://doi.org/10.3390/machines7020024 - 15 Apr 2019

Cited by 15 | Viewed by 4927

Abstract

This paper presents and experimentally validates a concept of end-to-end imitation learning for autonomous systems by using a composite architecture of convolutional neural network (ConvNet) and Long Short Term Memory (LSTM) neural network. In particular, a spatio-temporal deep neural network is developed, which [...] Read more.

This paper presents and experimentally validates a concept of end-to-end imitation learning for autonomous systems by using a composite architecture of convolutional neural network (ConvNet) and Long Short Term Memory (LSTM) neural network. In particular, a spatio-temporal deep neural network is developed, which learns to imitate the policy used by a human supervisor to drive a car-like robot in a maze environment. The spatial and temporal components of the imitation model are learned by using deep convolutional network and recurrent neural network architectures, respectively. The imitation model learns the policy of a human supervisor as a function of laser light detection and ranging (LIDAR) data, which is then used in real time to drive a robot in an autonomous fashion in a laboratory setting. The performance of the proposed model for imitation learning is compared with that of several other state-of-the-art methods, reported in the machine learning literature, for spatial and temporal modeling. The learned policy is implemented on a robot using a Nvidia Jetson TX2 board which, in turn, is validated on test tracks. The proposed spatio-temporal model outperforms several other off-the-shelf machine learning techniques to learn the policy. Full article

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18 pages, 3300 KiB

Open AccessEditor’s ChoiceArticle

Tailor-Made Hand Exoskeletons at the University of Florence: From Kinematics to Mechatronic Design

by Nicola Secciani, Matteo Bianchi, Alessandro Ridolfi, Federica Vannetti, Yary Volpe, Lapo Governi, Massimo Bianchini and Benedetto Allotta

Machines 2019, 7(2), 22; https://doi.org/10.3390/machines7020022 - 3 Apr 2019

Cited by 19 | Viewed by 6581

Abstract

Recently, robotics has increasingly become a companion for the human being and assisting physically impaired people with robotic devices is showing encouraging signs regarding the application of this largely investigated technology to the clinical field. As of today, however, exoskeleton design can still [...] Read more.

Recently, robotics has increasingly become a companion for the human being and assisting physically impaired people with robotic devices is showing encouraging signs regarding the application of this largely investigated technology to the clinical field. As of today, however, exoskeleton design can still be considered a hurdle task and, even in modern robotics, aiding those patients who have lost or injured their limbs is surely one of the most challenging goal. In this framework, the research activity carried out by the Department of Industrial Engineering of the University of Florence concentrated on the development of portable, wearable and highly customizable hand exoskeletons to aid patients suffering from hand disabilities, and on the definition of patient-centered design strategies to tailor-made devices specifically developed on the different users’ needs. Three hand exoskeletons versions will be presented in this paper proving the major taken steps in mechanical designing and controlling a compact and lightweight solution. The performance of the resulting systems has been tested in a real-use scenario. The obtained results have been satisfying, indicating that the derived solutions may constitute a valid alternative to existing hand exoskeletons so far studied in the rehabilitation and assistance fields. Full article

(This article belongs to the Special Issue Advances of Italian Machine Design)

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13 pages, 4208 KiB

Open AccessEditor’s ChoiceArticle

Application of IoT-Aided Simulation to Manufacturing Systems in Cyber-Physical System

by Yifei Tan, Wenhe Yang, Kohtaroh Yoshida and Soemon Takakuwa

Machines 2019, 7(1), 2; https://doi.org/10.3390/machines7010002 - 3 Jan 2019

Cited by 54 | Viewed by 7820

Abstract

With the rapid development of mobile and wireless networking technologies, data has become more ubiquitous and the IoT (Internet of Things) is attracting much attention due to high expectations for enabling innovative service, efficiency, and productivity improvement. In next-generation manufacturing, the digital twin [...] Read more.

With the rapid development of mobile and wireless networking technologies, data has become more ubiquitous and the IoT (Internet of Things) is attracting much attention due to high expectations for enabling innovative service, efficiency, and productivity improvement. In next-generation manufacturing, the digital twin (DT) has been proposed as a new concept and simulation tool for collecting and synchronizing real-world information in real time in cyber space to cope with the challenges of smart factories. Although the DT is considered a challenging technology, it is still at the conceptual stage and only a few studies have specifically discussed methods for its construction and implementation. In this study, we first explain the concept of DT and important issues involved in developing it within an IoT-aided manufacturing environment. Then, we propose a DT construction framework and scheme for inputting data derived from the IoT into a simulation model. Finally, we describe how we verify the effectiveness of the proposed framework and scheme, by constructing a DT-oriented simulation model for an IoT-aided manufacturing system. Full article

(This article belongs to the Special Issue Smart Manufacturing)

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16 pages, 4281 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

by Kenisuomo C. Luwei, Akilu Yunusa-Kaltungo and Yusuf A. Sha’aban

Machines 2018, 6(4), 59; https://doi.org/10.3390/machines6040059 - 20 Nov 2018

Cited by 47 | Viewed by 5468

Abstract

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount [...] Read more.

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis, and Recovery: Concept, Modeling, and Optimization)

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22 pages, 5766 KiB

Open AccessEditor’s ChoiceArticle

Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems

by Borja Ramis Ferrer, Usman Muhammad, Wael M. Mohammed and José L. Martínez Lastra

Machines 2018, 6(3), 39; https://doi.org/10.3390/machines6030039 - 1 Sep 2018

Cited by 36 | Viewed by 8507

Abstract

The employment of tools and techniques for monitoring and supervising the performance of industrial systems has become essential for enterprises that seek to be more competitive in today’s market. The main reason is the need for validating tasks that are executed by systems, [...] Read more.

The employment of tools and techniques for monitoring and supervising the performance of industrial systems has become essential for enterprises that seek to be more competitive in today’s market. The main reason is the need for validating tasks that are executed by systems, such as industrial machines, which are involved in production processes. The early detection of malfunctions and/or improvable system values permits the anticipation to critical issues that may delay or even disallow productivity. Advances on Information and Communication Technologies (ICT)-based technologies allows the collection of data on system runtime. In fact, the data is not only collected but formatted and integrated in computer nodes. Then, the formatted data can be further processed and analyzed. This article focuses on the utilization of standard Key Performance Indicators (KPIs), which are a set of parameters that permit the evaluation of the performance of systems. More precisely, the presented research work demonstrates the implementation and visualization of a set of KPIs defined in the ISO 22400 standard-Automation systems and integration, for manufacturing operations management. The approach is validated within a discrete manufacturing web-based interface that is currently used for monitoring and controlling an assembly line at runtime. The selected ISO 22400 KPIs are described within an ontology, which the description is done according to the data models included in the KPI Markup Language (KPIML), which is an XML implementation developed by the Manufacturing Enterprise Solutions Association (MESA) international organization. Full article

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22 pages, 2176 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

by Fabrizio Balducci, Donato Impedovo and Giuseppe Pirlo

Machines 2018, 6(3), 38; https://doi.org/10.3390/machines6030038 - 1 Sep 2018

Cited by 145 | Viewed by 19623

Abstract

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that [...] Read more.

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it. Full article

(This article belongs to the Special Issue Multi-Body System Dynamics: Monitoring, Simulation and Control)

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19 pages, 1186 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Development of a Methodology for Condition-Based Maintenance in a Large-Scale Application Field

by Marco Cocconcelli, Luca Capelli, Jacopo Cavalaglio Camargo Molano and Davide Borghi

Machines 2018, 6(2), 17; https://doi.org/10.3390/machines6020017 - 16 Apr 2018

Cited by 13 | Viewed by 6388

Abstract

This paper describes a methodology, developed by the authors, for condition monitoring and diagnostics of several critical components in the large-scale applications with machines. For industry, the main target of condition monitoring is to prevent the machine stopping suddenly and thus avoid economic [...] Read more.

This paper describes a methodology, developed by the authors, for condition monitoring and diagnostics of several critical components in the large-scale applications with machines. For industry, the main target of condition monitoring is to prevent the machine stopping suddenly and thus avoid economic losses due to lack of production. Once the target is reached at a local level, usually through an R&D project, the extension to a large-scale market gives rise to new goals, such as low computational costs for analysis, easily interpretable results by local technicians, collection of data from worldwide machine installations, and the development of historical datasets to improve methodology, etc. This paper details an approach to condition monitoring, developed together with a multinational corporation, that covers all the critical points mentioned above. Full article

(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)

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17 pages, 4950 KiB

Open AccessEditor’s ChoiceArticle

The Setup Design for Selective Laser Sintering of High-Temperature Polymer Materials with the Alignment Control System of Layer Deposition

by Alexey Nazarov, Innokentiy Skornyakov and Igor Shishkovsky

Machines 2018, 6(1), 11; https://doi.org/10.3390/machines6010011 - 5 Mar 2018

Cited by 10 | Viewed by 7542

Abstract

This paper presents the design of an additive setup for the selective laser sintering (SLS) of high-temperature polymeric materials, which is distinguished by an original control system for aligning the device for depositing layers of polyether ether ketone (PEEK) powder. The kinematic and [...] Read more.

This paper presents the design of an additive setup for the selective laser sintering (SLS) of high-temperature polymeric materials, which is distinguished by an original control system for aligning the device for depositing layers of polyether ether ketone (PEEK) powder. The kinematic and laser-optical schemes are given. The main cooling circuits are described. The proposed technical and design solutions enable conducting the SLS process in different types of high-temperature polymer powders. The principles of the device adjustment for depositing powder layers based on an integral thermal analysis are disclosed. The PEEK sinterability was shown on the designed installation. The physic-mechanical properties of the tested 3D parts were evaluated in comparison with the known data and showed an acceptable quality. Full article

(This article belongs to the Special Issue Process Innovation in Digital Manufacturing)

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17 pages, 5261 KiB

Open AccessEditor’s ChoiceArticle

Design Procedure for High-Speed PM Motors Aided by Optimization Algorithms

by Francesco Cupertino, Riccardo Leuzzi, Vito Giuseppe Monopoli and Giuseppe Leonardo Cascella

Machines 2018, 6(1), 5; https://doi.org/10.3390/machines6010005 - 11 Feb 2018

Cited by 10 | Viewed by 4826

Abstract

This paper considers the electromagnetic and structural co-design of superficial permanent magnet synchronous machines for high-speed applications, with the aid of a Pareto optimization procedure. The aim of this work is to present a design procedure for the afore-mentioned machines that relies on [...] Read more.

This paper considers the electromagnetic and structural co-design of superficial permanent magnet synchronous machines for high-speed applications, with the aid of a Pareto optimization procedure. The aim of this work is to present a design procedure for the afore-mentioned machines that relies on the combined used of optimization algorithms and finite element analysis. The proposed approach allows easy analysis of the results and a lowering of the computational burden. The proposed design method is presented through a practical example starting from the specifications of an aeronautical actuator. The design procedure is based on static finite element simulations for electromagnetic analysis and on analytical formulas for structural design. The final results are validated through detailed transient finite element analysis to verify both electromagnetic and structural performance. The step-by-step presentation of the proposed design methodology allows the reader to easily adapt it to different specifications. Finally, a comparison between a distributed-winding (24 slots) and a concentrated-winding (6 slots) machine is presented demonstrating the advantages of the former winding arrangement for high-speed applications. Full article

(This article belongs to the Special Issue High Speed Motors and Drives: Design, Challenges and Applications)

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