Real Machine Research Articles

Metaheuristics on quantum computers: Inspiration, simulation and real execution

Quantum-inspired metaheuristics are solvers that incorporate principles inspired from quantum mechanics into classical-approximate algorithms using non-quantum machines. Due to the uniqueness of quantum principles, the inspiration of quantum phenomena and the way it is done in fundamentally different non-quantum systems rather than real or simulated quantum computers raise important questions about these algorithms’ design and the reproducibility of their results in real or simulated quantum devices. Thus, this work’s contribution stands in a first step towards answering those questions as an attempt to identify key findings in the existing literature that should be considered or adapted in order to build hybrid or fully-quantum algorithms that can be used in quantum machines. This is done by proposing and studying four inspired, simulated and real quantum cellular genetic algorithms that, as far as the authors’ knowledge, are the first quantum structured metaheuristics studied in the three quantum realms using a quantum simulator with 32 quantum bits and a real quantum machine employing 15 superconducting quantum bits. The users’ mobility management in cellular networks is taken as a validation problem using 13 real-world instances. The comparisons have been made against 6 diverse algorithms using 9 comparison metrics. Thorough statistical tests and parameters’ sensitivity analysis have been also conducted. The experiments allowed answering several questions, including how quantum hardware influences the studied-algorithms’ search process. They also enabled opening new perspectives in quantum metaheuristics’ design.

Influence of 3D Printing Topology by DMLS Method on Crack Propagation.

The presented text deals with research into the influence of the printing layers’ orientation on crack propagation in an AlSi10Mg material specimen, produced by additive technology, using the Direct Metal Laser Sintering (DMLS) method. It is a method based on sintering and melting layers of powder material using a laser beam. The material specimen is presented as a Compact Tension test specimen and is printed in four different defined orientations (topology) of the printing layers—0°, 45°, 90°, and twice 90°. The normalized specimen is loaded cyclically, where the crack length is measured and recorded, and at the same time, the crack growth rate is determined. The evaluation of the experiment shows an apparent influence of the topology, which is essential especially for possible use in the design and technical preparation of the production of real machine parts in industrial practice. Simultaneously with the measurement results, other influencing factors are listed, especially product postprocessing and the measurement method used. The hypothesis of crack propagation using Computer Aided Engineering/Finite Element Method (CAE/FEM) simulation is also stated here based on the achieved results.

Simulation Technology in Anesthesiology.

Simulation Technology in Anesthesiology.

Modeling and experimental research on the evolution process of micro through-slit array generated with masked jet electrochemical machining

This paper proposed to prepare micro through-slit array on thin metallic plate with masked jet electrochemical machining. An ECM mathematic model was developed with ANSYS Parametric Design Language to analyze the evolution process of micro through-slit, including the broken through process of workpiece. In each step, machining status was monitored to judge whether the workpiece was broken through, then both model and boundary condition were reset accordingly, thus the broken through process was well solved. The simulation results indicated that with moving speed of workpiece decreasing (increasing machining time in ECM model), the sectional profile evolved from groove to a tapering micro through-slit, and a micro through-slit with low taper was formed at last. The changes of electric field distribution in both X and Z direction affected the material dissolution along the side wall of micro through-slit and contributed to the formation of micro through-slit. The experiments were performed with different moving speeds of workpiece, and the sectional profile evolution process agreed well with that in simulation. Moreover, with the same real machining time, low pulse duty cycle was useful to enhance the transport of electrolytic product in the small machining area and reduce the dimensional difference of micro through-slit. At last, ten micro through-slits with the length of 30 mm were well prepared at one time on a thin metallic plate of 50 μm. The width of upper surface and lower surface was 206.1 ± 2.28 μm and 193.7 ± 1.97 μm, respectively, and the taper was about 7.1°.

The need to update NCRP 151 data for 10 MV linear accelerator bunker shielding based on new measurements and Monte Carlo simulations

Linear accelerator bunker shielding protocols such as NCRP 151 have previously been tested against a large sample of measured data from real bunkers and machines but differences in per-energy concrete penetration (TVLs) for 10 MV has not yet been resolved. These differences are likely due to historical beam data and can potentially result in over-exposure of radiation workers and the public. This study examines a cohort of clinical linac bunker survey measurements and compares them to popular shielding protocols. Differences were investigated using contemporary beam data for both Monte Carlo simulation and in analytical equations. For primary barriers, NCRP 151 underestimates the dose rate through concrete by on average a factor of 2 with secondary barriers and maze entrance doses having much better agreement. Use of contemporary beam data in Monte Carlo simulation and an analytical equation yielded TVL values much closer to the measured values compared to NCRP 151. The TVL data in NCRP 151 is outdated and needs to be updated based upon the energy spectra of modern linear accelerators. Until then, physicists should use the TVL values shown in this study instead.

Trans-orbital sonographic assessment of optic nerve diameter in a sampled Nigerian population

Background: Studies have reported variants in the dimensions of optic nerve diameter among different ethnic groups, just as other body anatomy differs from regions to regions.Aim: To establish normal range of optic nerve diameter in a sampled Nigerian population, sonographically.Materials and Method: A total of 725 apparently healthy adult subjects (362 males aged 32 to 65 years and 363 females aged 30 to 68 years) were recruited from the South South and South Eastern parts of Nigeria for this prospective descriptive study. The optic nerve diameter (OND) was measured using a high-resolution digital dedicated small-parts real time ultrasound machine (Sonoace 5500; Medicol, Medison, Miami, FL, USA) with a high frequency (10-MHz) linear array transducer. Subjects were in supine position and were asked to keep their eyes closed and still. Coupling gel was placed on the closed eye lid with the transducer softly placed over the upper temporal eyelid in an axial plane. The OND was measured perpendicular to the vertical axis of the scanning plane as a horizontal distance between the two walls of the nerve sheath. The height and weight of the subjects were determined using a meter rule and a weighing scale.Results: The mean optic nerve sheath diameter of males and females was 4.2 ± 0.13 mm. It ranged from 4.0 to 4.45 mm. The optic nerve sheath diameter of males was not significantly different from that of females (p = 0.345). No significant difference between the mean OND of both eyes (p = 0.345). Body mass index and age did not have any association with OND (r = 0.017, 0.034), the data were normally distributed.Conclusion: Optic nerve diameter of apparently normal Nigerian adults ranges from 4.0 to 4.5 mm. Values outside this range may demand further evaluation in the study population.

METHOD FOR SAFE EXPERIMENTAL TESTING OF MACHINE TOOL USABLE SPINDLE POWER

The regenerative chatter during milling is a consequence of the machine tool dynamic compliance and the specific setting of the cutting process. The basic request on the machine structure is to enable a stable cut with dominant portion of the installed spindle power. In order to improve the dynamic properties of newly-designed machine tools, the machine tool structures are optimized using various advanced simulation methods and are made of various advanced materials. The final design should be tested experimentally. The test of usable spindle power provides important feedback on the machine tool design. The testing of large machine tools is time consuming due to the significantly varying machine dynamic compliance dependent on the working space. Due to the installed high power and large diameter tools used, the occurrence of chatter might be dangerous with a high risk of machine tool damage. The article presents a method for the safe and time-effective experimental testing of the machine tool usable spindle power. The method is based on the experimental identification of the cutting force coefficients and the experimental identification of machine tool behavior with the support of extrapolation models based on identified experimental data. The dynamic compliance is measured within a rough network of measurement points in the machine working space. The measured dynamic compliance results are characterized by a model based on modal identification data. Only a few cutting tests are done for the cutting force coefficient identification. Finally, a map of the usable spindle power is calculated. This approach makes it possible to calculate the results with respect to the real machining process and the current machine tool conditions across the whole working space in an acceptable time. The approach is demonstrated on a real case study.

Experimental visualization of two-phase flow inside a real-size piston of a crosshead-type marine engine

ABSTRACT The oscillating cooling method is one of the most effective cooling solutions to improve the piston cooling ability for diesel engines. Most of the experimental studies were focused on the simplified model, and few were based on the actual complex piston cooling cavities. To give a better insight into the flow mechanism inside the actual cavities, a flow visualization test rig was built based on a real-size piston model with a stroke of 1550 mm and a cylinder diameter of 350 mm. The model replicates the piston and cooling cavities of a typical low-speed two-stroke crosshead marine engine MAN B&W 6S35ME. The test rig ensures that the coolant supply mode, the coolant flow path, and the kinematic curve of the piston head are consistent with the real machine. The piston model is an exact replica of the original scale made of PC material. A high-speed camera was used to capture the transient flow patterns of the gas-liquid two-phase flow inside the cooling cavities during the reciprocating motion of the whole stroke. More accurate boundary conditions are thus provided for the numerical simulation method, which significantly improves the accuracy of previous simulations under the same research topic. Analysis of the piston’s oscillatory cooling method is beneficial to optimizing the thermal management of engines in the future.

Data-driven application of MEMS-based accelerometers for leak detection in water distribution networks

Water scarcity is a global concern; 68 countries are facing extremely-high to medium-high risk of water stress. In this era of crisis, where water conservation is an absolute necessity, the water distribution networks (WDNs) globally are experiencing significant leaks. These leaks cause tremendous financial loss and unacceptable environmental hazards, thus further aggravating the water scarcity situation. To minimize such damage, the adoption of advanced technologies and methodologies for leak detection in the WDNs is absolutely necessary. In this regard, we have investigated the application of cost-effective MEMS-based accelerometers. Experiments were conducted on real networks (metal and non-metal pipes), over the course of ten months, and the acquired acceleration signals were analyzed using a monitoring algorithm. Monitoring index efficiencies and standard deviations for every leak and no-leak case was extracted. Two individual [KNN and Decision Tree] and two ensembles [Random Forest and Adaboost (Decision Tree)] based machine learning models were developed for the accurate classification of the leak and no-leak cases using extracted features; and separate models were developed for metal and non-metal pipes. Random Forest outperformed the other machine learning models and the overall accuracy reached 100% for metal pipes and 94.93% for non-metal pipes. The machine learning models were further validated using unseen/unlabeled cases and were highly effective in detecting leaks. This study demonstrated the applicability of MEMS-based accelerometers for leak detection and established real network-based machine learning models thereby contributing to the research scarcity in this important area.

Significance of risk priority number in machine condition monitoring

Machine’s condition is very important for industry’s efficient organization, growth, sustainability in the market and to increase profit margin. Condition monitoring indicates possible forthcoming failure well in advance before it becomes extensive serious issue. By the use of this maintenance strategy machines performance, quality of product as well as life of machines also gets improved. In condition monitoring, worn out parts will be identified and remedial actions i.e. repairing or replacement can be suggested. There are so many methods used for condition monitoring such as visual monitoring, temperature monitoring, lubricating oil monitoring, crack and leakage detection, vibration monitoring, corrosion of parts monitoring. From all these methods, used lubricating oil monitoring along with ferrography gives nanotribological results about real time machine condition. The data obtained from this monitoring, calculates risk priority number which gives information regarding probability of machine failure. Risk priority number finds all causes leads to failure. This number is also used to rank the failure causes and also suggests remedial measures. Hence wear monitoring followed by risk priority number to improve machines functionality and efficiency.

Predicting the position-dependent dynamics of machine tools using progressive network

The structural dynamics of a machine tool exhibit position-dependent behavior under operation due to the kinematic reconfiguration of its feed drives. Experimental identification of position-dependent dynamics can be time-consuming, and simulation models may lack accuracy due to modeling uncertainties. In this paper, the prediction of a machine tool's position-dependency is approached under the progressive network, a transfer learning technique to bridge the gap between the simulation and real-world domains. The dynamics simulation model may be somewhat inaccurate, but still describes a machine tool's overall position-dependency. The real-world domain is then characterized by sparse dynamics measurements made on the real machine tool. Under the progressive network architecture, a neural network is first trained in the simulation domain to extract valuable prior knowledge about the machine's position-dependency. A second network posed in the real-world domain then accesses such prior knowledge from the simulation domain via lateral connections to improve its dynamics predictions. Such transfer learning methodology may be most advantageous when the number of experimental data is limited, and the machine is unavailable for further testing due to production purposes. The proposed methodology was validated under numerical and experimental studies.

Impedance modeling and its application to the analysis of the collective effects

Impedance modeling for accelerator applications has improved over the years, largely as a result of advances in simulation capabilities. While this modeling has been successful in reproducing certain measurements, it is still a significant challenge to predict collective effects in real machines. In this paper, we review our approach to impedance modeling and the subsequent simulations of collective effects. We discuss the choice of the electrodynamics codes and the required computer power resources, modeling of the geometric, and resistive wall impedances, their comparison with analytical approaches, and their application for simulating of the collective effects with tracking and beam-induced heating.

Numerical Evaluation and High-Speed Rotating Test on Circular Arc Spring Dampers for Centrifugal Compressors

Abstract A circular arc spring damper (CASD) is a recently proposed fluid-film damper that has two or more arc-shaped centering springs and dual radial clearances formed by wire electric discharge machining (WEDM). CASD requires less space and weight than a conventional cage-centered squeeze-film damper (SFD). It provides linear stiffness and stable damping force in rotor-bearing systems to attenuate vibration due to imbalance or to improve rotordynamic stability. The authors have been investigated the dynamic characteristics of CASD in component-level experiments. However, their performance and applicability to real machines have not been confirmed in system-level experiments. Additionally, a theoretical means of evaluation for CASD should be established to predict its dynamic coefficients and to understand the mechanism of dynamic force generation. In the first part of this study, a numerical evaluation method using two-way fluid–structure interaction (FSI) analysis and its theoretical background is presented. Transient structural analysis and fluid-film flow analysis with a simple homogeneous cavitation model are coupled in the commercial multiphysics platform ansys. The accuracy of the method was validated by comparing the damping and added-mass coefficients with results from previous experiments. Furthermore, several aspects of the force generation mechanism and the difference from conventional SFD were studied numerically. The second part of the study addresses the application of CASD in a multistage centrifugal compressor. A combined 4-in. diameter, five-pad tilting pad journal bearing (TPJB) with four-arc type CASD was newly designed and manufactured. To prove the applicability of the developed damper bearing, a series of rotating tests were conducted at a high-speed balancing facility with a full-scale dummy rotor with a critical speed ratio (CSR) of approximately 3.1. The measured unbalance response showed a much lower amplification factor (AF) than that of the conventional TPJB without the damper, which infers a significant improvement in the stability. The measured responses agreed with the rotordynamic analysis, which uses the dynamic coefficients of CASD derived from the proposed numerical evaluation method.

Design of an aeroelastic physical model of the DTU 10MW wind turbine for a floating offshore multipurpose platform prototype

Multi-purpose offshore structures are very complex systems to be designed as many different requirements have to be taken into account simultaneously. Experimental tests on scaled models can be very useful to verify structural behavior and to validate numerical models. The definition of the scale can play a key role in performing tests as high scales, closer to full-scale, are generally related to more reliable results. The present paper concerns the design of a wind turbine model for a large-scale model of a Multi-purpose offshore Platform developed within the EU project H2020 Blue Growth Farm. This project aims at developing an offshore farm, based on a modular floating structure that integrates wave energy converters and a wind turbine with aquaculture. The scale model of the complete structure will be deployed at the Natural Ocean Engineering Laboratory (NOEL). The paper will focus on the strategies adopted to scale 1:15 the DTU 10 MW wind turbine in order to represent both the main aeroelastic features and all the functionalities of a real machine. The complete design of the machine and its control will be also provided.

Lumped-Parameters Thermal Network of PM Synchronous Machines for Automotive Brake-by-Wire Systems

Thermal analysis represents a key factor in electrical machine design due to the impact of temperature increase on insulation lifetime. In this context, there has been a wide investigation on thermal modeling, particularly for machines used in harsh working conditions. In this perspective, brake-by-wire (BBW) systems represent one of the most challenging applications for electrical machines used for automotive smart actuators. Indeed, electro-actuated braking systems are required to repeatedly operate the electric machine in high overload conditions in order to limit the actuator response time, as well as to enhance gravimetric and volumetric specific performance indexes. Moreover, BBW systems often impose unconventional supply conditions to the electric machine, consisting of dc currents in three-phase windings to keep the rotor fixed during the braking intervals. However, a dc supply leads to uneven temperature distributions in the machine, and simplified thermal models may not accurately represent the temperature variations for the different machine parts. Considering such unconventional supply conditions, this paper initially investigates the applicability of a conventional lumped-parameters thermal network (LPTN) based on symmetry assumptions for the heat paths and suitable for surface-mounted PM synchronous machines used in BBW systems. An extensive test campaign consisting of pulses and load cycle tests representative of the real machine operations was conducted on a prototype equipped with several temperature sensors. The comparison between measurements and predicted average temperatures, together with insights on the unbalanced heat distribution under the dc supply obtained by means of finite element analyses (FEA), paved the way for the proposal of a phase-split LPTN with optimized parameters. The paper also includes a critical analysis of the optimized parameters, proposing a simplified, phase-split lumped-parameters thermal model suitable to predict the temperature variations in the different machine parts for PM synchronous electric machines used in BBW systems.

Modeling and hardware-in-the-loop system realization of electric machine drives — A review

This paper presents a state-of-the-art review in modeling approach of hardware in the loop simulation (HILS) realization of electric machine drives using commercial real time machines. HILS implementation using digital signal processors (DSPs) and field programmable gate array (FPGA) for electric machine drives has been investigated but those methods have drawbacks such as complexity in development and verification. Among various HILS implementation approaches, more efficient development and verification for electric machine drives can be achieved through use of commercial real time machines. As well as implementation of the HILS, accurate modeling of a control target system plays an important role. Therefore, modeling trend in electric machine drives for HILS implementation is needed to be reviewed. This paper provides a background of HILS and commercially available real time machines and characteristics of each real time machine are introduced. Also, recent trends and progress of permanent magnet synchronous machines (PMSMs) modeling are presented for providing more accurate HILS implementation approaches in this paper.

Emulation of an Induction Machine for Unbalanced Grid Faults

A laboratory power level 5.0 hp induction machine emulation with grid interface for various asymmetric fault transients is implemented in this article. It is done in real time with power-hardware-in-loop (PHIL) technology. The PHIL emulation procedure permits the testing of complex control strategies of an ac microgrid while determining the machine behavior during grid faults. Since asymmetric grid faults generate opposite sequence components in the individual harmonic currents, a novel emulator control strategy with combination of proportional-integral (PI) and proportional resonant (PR) controllers is proposed to mimic the behavior of the real machine under grid faults. A step-by-step procedure for designing the PI and PR controllers is given in detail. An improved induction machine mathematical model with trapezoidal integration is also suggested for precise tracking of the emulator. An emulator laboratory setup is developed with Opal-Rt as the real-time controller and linear amplifier as the emulating device. Simulation and experimental results are provided to validate the tracking capability of the proposed emulator. The results confirm that with the proposed control, emulator currents track the real machine currents.

A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding.

The optimal machine settings in polymer processing are usually the result of time-consuming and expensive trials. We present a workflow that allows the basic machine settings for the plasticizing process in injection molding to be determined with the help of a simulation-driven machine learning model. Given the material, screw geometry, shot weight, and desired plasticizing time, the model is able to predict the back pressure and screw rotational speed required to achieve good melt quality. We show how data sets can be pre-processed in order to obtain a generalized model that performs well. Various supervised machine learning algorithms were compared, and the best approach was evaluated in experiments on a real machine using the predicted basic machine settings and three different materials. The neural network model that we trained generalized well with an overall absolute mean error of 0.27% and a standard deviation of 0.37% on unseen data (the test set). The experiments showed that the mean absolute errors between the real and desired plasticizing times were sufficiently small, and all predicted operating points achieved good melt quality. Our approach can provide the operators of injection molding machines with predictions of suitable initial operating points and, thus, reduce costs in the planning phase. Further, this approach gives insights into the factors that influence melt quality and can, therefore, increase our understanding of complex plasticizing processes.

A machine learning-based workflow for automatic detection of anomalies in machine tools

Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%.

Embedding of complete graphs in broken Chimera graphs

In order to solve real-world combinatorial optimization problems with a D-Wave quantum annealer, it is necessary to embed the problem at hand into the D-Wave hardware graph, namely Chimera or Pegasus. Most hard real-world problems exhibit a strong connectivity. For the worst-case scenario of a complete graph, there exists an efficient solution for the embedding into the ideal Chimera graph. However, since real machines almost always have broken qubits, it is necessary to find an embedding into the broken hardware graph. We present a new approach to the problem of embedding complete graphs into broken Chimera graphs. This problem can be formulated as an optimization problem, more precisely as a matching problem with additional linear constraints. Although being NP-hard in general, it is fixed-parameter tractable in the number of inaccessible vertices in the Chimera graph. We tested our exact approach on various instances of broken hardware graphs, both related to real hardware and randomly generated. For fixed runtime, we were able to embed larger complete graphs compared to previous, heuristic approaches. As an extension, we developed a fast heuristic algorithm which enables us to solve even larger instances. We compared the performance of our heuristic and exact approaches.