Multiple Setups Research Articles

On the effect of stiffness discontinuities on the reeling of pipelines using FEM and machine learning strategy

ABSTRACT Under current DNV specifications, pipeline design for reeling installation involves extensive FE simulations considering wall thickness and yield stress mismatch. This process is laborious, requiring multiple setups and trial-and-error calculations, with each simulation taking several hours. This paper proposes a machine learning strategy to study the mismatch design during the reeling process. Four machine learning models—Gaussian Process regression, Multilayer Perceptron, Support Vector Machine, and Random Forest—are used. Parameters that may affect failure, including yield strength, wall thickness, hardening coefficient, back tension, reel diameter, and pipe diameter are considered as input features. FE analyses simulate the reeling process, extracting output parameters such as maximum ovality, peak strain, local curvature, and lift-off for training. Based on careful calibration, it is shown to form an accurate and efficient regression framework, which only takes less than one minute to finish, exhibiting significant advantages over the conventional process.

The Optimization Inventory Process on Identical Machine Job Shop with Multiple Setups Using Genetic Algorithm

The Optimization Inventory Process on Identical Machine Job Shop with Multiple Setups Using Genetic Algorithm

Model reduction-based Bayesian updating of non-classically damped systems using modal data from multiple setups

Model reduction-based Bayesian updating of non-classically damped systems using modal data from multiple setups

Automated Electrolyte Formulation and Coin Cell Assembly for High-Throughput Lithium-Ion Battery Research

Battery research involving performance evaluation by battery cycling is a time-consuming process with sometimes large cell-to-cell variations. To enhance the rate of testing and reproducibility, automation is one accelerator that can be used. In this study, we introduce ODACell1 (Figure 1), an automated system for high-throughput electrolyte screening and Li-ion cell assembly.ODACell enables the assembly of Li-ion battery cells in ambient atmosphere. We use LiFePO4||Li4Ti5O12-based full cells with a dimethyl sulfoxide-based model electrolyte for demonstrating the automated system. We investigate the reproducibility of the automated system on this system as well as the influence of water on the performance of the model electrolyte when exposed to ambient air. Our system is uniquely equipped for combinatorial experimentation, combining electrolyte formulation, batch coin cell assembly, and cycling into a single system. The integration of multiple setups into a single unit is made possible, overcoming the incompatibility issues with integrating multiple, different automated systems together.While there are several other robotic systems besides ODACell for battery material acceleration, ODACell’s unique combination of robots incorporated in the robotic setup allow a broader set of tasks to be automated. For example, Clio by Dave et al. 2 focus on electrolyte formulation and characterization, lacking assembly of battery cells; AutoBASS by Zhang et al. 3 only automates battery assembly; and Poseidon by Svensson et al. 4 can automate electrolyte formulation, characterization, and coin cell assembly, but lack high-throughput batch processing.ODACell ensures high reproducibility, which is needed to establish robust models and determine accurate electrochemical and physical properties. The system assembled 131 coin cells, with a conservative fail rate of only 5%. The relative standard deviation of the discharge capacity after 10 cycles was 2% for the studied system. The investigation into the influence of water on cycling performance show overlapping performance trends between coin cells with 2 vol% and 4 vol% of water in the electrolyte highlighting the nontrivial relationship between water and cycling performance.In conclusion, ODACell is a promising tool for high-throughput electrolyte screening and Li-ion cell assembly. It builds upon previously published robotic setups by automating more of the research workflow. Our study highlights the importance of reproducibility in battery research and the complex relationship between water contaminants in electrolytes and cycling performance. ODACell has the potential to be a useful tool for advancing battery research by enhancing the rate of testing and reducing the variation of results.

Non-Newtonian ViRheometry via Similarity Analysis

We estimate the three Herschel-Bulkley parameters (yield stress σ Y , power-law index n , and consistency parameter η ) for shear-dependent fluid-like materials possibly with large-scale inclusions, for which rheometers may fail to provide a useful measurement. We perform experiments using the unknown material for dam-break (or column collapse) setups and capture video footage. We then use simulations to optimize for the material parameters. For better match up with the simple shear flow encountered in a rheometer, we modify the flow rule for the elasto-viscoplastic Herschel-Bulkley model. Analyzing the loss landscape for optimization, we realize a similarity relation ; material parameters far away within this relation would result in matched simulations, making it hard to distinguish the parameters. We found that by exploiting the setup dependency of the similarity relation, we can improve on the estimation using multiple setups, which we propose by analyzing the Hessian of the similarity relation. We validate the efficacy of our method by comparing the estimations to the measurements from a rheometer (for materials without large-scale inclusions) and show applications to materials with large-scale inclusions, including various salad or pasta sauces, and congee.

On the field uniformity of reverberation chambers – a global sensitivity analysis study

PurposeGlobal sensitivity analysis (SA) by means of Sobol’ indices enhanced with different surrogate modeling techniques is performed in this work. The purpose is to investigate the influence of measurement uncertainties and the environment characteristics themselves on the desired field uniformity in reverberation chambers (RCs). This yields an efficient apparatus for the stirring and chamber design process.Design/methodology/approachThe technique of Sobol’ indices, as a candidate of global SA methods, is suitable for high fluctuations due to its robustness, which can be addressed to the stochastic nature of the RC environment. The aim of using surrogate modeling techniques is to compute the indices efficiently with a moderate number of required simulations. The powerfulness of this approach is introduced in a simple numerical example in which the physical phenomena can be identified more straightforwardly.FindingsThis method can provide useful knowledge in the lower frequency range, where the ideal properties of the electromagnetic field in RCs cannot be established, and the importance of the setup parameters can vary from configuration to configuration. In addition, it can serve as a basis for setup adaptation during parallelized electromagnetic compatibility tests, which would result in a more time- and cost-saving option in industrial applications in the future.Originality/valueDespite the previous attempts, a profound investigation of multiple setup parameters is still a hot topic. The main contribution of this work is the extension of the application area of the method of Sobol’ indices to RCs, which has not been done so far.

Emergent Quantized Communication

The field of emergent communication aims to understand the characteristics of communication as it emerges from artificial agents solving tasks that require information exchange. Communication with discrete messages is considered a desired characteristic, for scientific and applied reasons. However, training a multi-agent system with discrete communication is not straightforward, requiring either reinforcement learning algorithms or relaxing the discreteness requirement via a continuous approximation such as the Gumbel-softmax. Both these solutions result in poor performance compared to fully continuous communication. In this work, we propose an alternative approach to achieve discrete communication -- quantization of communicated message. Using message quantization allows us to train the model end-to-end, achieving superior performance in multiple setups. Moreover, quantization is a natural framework that runs the gamut from continuous to discrete communication. Thus, it sets the ground for a broader view of multi-agent communication in the deep learning era.

FDD based modal identification of structures using least squares approach

In the conventional frequency domain modal identification procedures, modal parameters are attempted to be identified using power spectral density matrix decomposition. As it is well stated in the literature, the quality of estimated mode shapes significantly increases when an average PSD matrix over a selected frequency band is utilized in the identification procedure. In this context, this study aims at deriving a weighted average for the PSD matrix to increase the quality of the estimated mode shapes by using an FDD-based modal model. Then, the remaining modal parameters comprising modal frequency, damping ratio and spectral density of modal exciting force are calculated through a least squares equation. Moreover, the presented FDD based approach is also adapted to the solution of multiple setup problem. The proposed method for single and multiple setup cases is validated by making use of one numerical and two field data examples. The obtained results reveal that the presented frequency domain technique can successfully achieve high quality of modal estimates in the presence of measurement noise, and it can be easily implemented to field applications.

Modeling of the Variation Propagation for Complex-Shaped Workpieces in Multi-Stage Machining Processes

Variation prediction and quality control for complex-shaped workpieces in automotive and aerospace fields with multi-stage machining processes have drawn significant attention because of the widespread application and increasing diversity of these kinds of workpieces. To finish the final workpieces with complex shapes, multiple setups and operations are often applied in machining processes. However, sources of geometric error, such as fixture error, datum error, machine tool path error, and the dimensional quality of the product, interact complicatedly at different stages. These complex interactions pose significant challenges to final product error prediction and reduction. Manufacturing error prediction based on stream of variation is an effective way to control the machining quality. However, there are few integrated models that can describe the interactions among types of geometric error sources from different stages for different kinds of complex workpieces. This paper proposes a modified error prediction model to systematically capture the interactions of different error sources among different operations for complex-shaped workpieces in multi-stage machining processes. Using differential motion vectors, the connection of all key variations from machine, fixture, and workpiece is established. This modified model can not only handle general fixture layouts for complex workpieces, but also introduce machining-induced variations. Based on this model, the main error sources identification method and error compensation method are proposed. In order to evaluate the effectiveness of the proposed method, engine blocks are used to be machined as an example. Compared with a machining process without a compensating strategy, the average machining error of the key feature is reduced by 80.5% after compensating for the main error sources.

Anisotropic AE Attenuation in Mapping of Composite Specimen Progressive Failure under Unconfined Loading

The mapping of failure that uses acoustic emission (AE) is an advanced approach when tracking the location of cracks in rock materials under progressive stress. However, attenuation of the acoustic wave significantly affects the identification of the acoustic source. The isotropic attenuation model suggests a constant attenuation in all rock material directions, which provides unrealistic failure mapping for anisotropic rocks. This study investigated the impact of high attenuation on the mapping failure of a composite (sandstone–shale–sandstone), intact sandstone, and shale specimens that used AE under axile stress. The results proved that wave attenuation is the primary obstacle when obtaining a compatible failure map to the real specimen failure. Multiple setups of AE sensors were implemented to quantify the attenuation values for a wave that propagated across composite joints and shale bedding planes. The amplitude and energy attenuation (EA) values increased by 85% and 47%, respectively, when the bedding plane of the shale was from 0° to 90°, which reflected anisotropic behavior. The energy and amplitude reduction reached 99% and 39%, respectively, by propagating across a single joint. In addition, behind a double joint, no signal was received. However, the application of a load perpendicular to the joint interface improved the acoustical–wave characteristics. Therefore, when the rock material anisotropy was increased, there was lower accuracy when mapping specimen failure, because the attenuation varied at each angle between the source and sensor. In addition, the EA method suggested in this study presented hits location more precisely compared with the P-velocity (Vp) method. These findings could contribute to the development of an anisotropic attenuation model for field and laboratory application of AE.

DIAGNOSTIC ACCURACY OF MAGNETIC RESONANCE SPECTROSCOPY AND DIFFUSION WEIGHTED IMAGING IN GRADING OF INTRACRANIAL GLIOMAS TAKING HISTOPATHOLOGY AS GOLD STANDARD

Objectives: To find diagnostic accuracy of magnetic resonance spectroscopy and magnetic resonance diffusion in grading of gliomas with histopathology as gold standard.
 Methods: It is a Cross Sectional Study conducted at Department of Radiology, Mayo Hospital, Lahore. Duration was 06 months i.e Sep, 2018 to Mar, 2019. One hundred patients fulfilling selection criteria were enrolled in the study. Then all patients underwent MRI including conventional sequences as well as DWI and MRS using a spin echo sequence (SE) with short TE (35 mm/s) and long TE (144 mm/s). Patients were labeled as positive or negative (as per operational definition). Subsequently patients underwent biopsy under general anesthesia. Reports were assessed and patients were confirmed as positive or negative according to operational definition. All of this information was recorded in a predesigned Performa.
 Results: Total number of patients in the study were one hundred. Mean age of the patients were 38.61(years) +11.40 whereas there were 43 (43.0) male and 57 (57.0) female patients included in the study. The diagnostic accuracy, sensitivity, specificity, PPV and NPV of magnetic resonance spectroscopy was 84.00%, 89.86%, 70.97%, 87.32% and 75.86% respectively whereas diagnostic accuracy, sensitivity, specificity, PPV and NPV of diffusion weighted imaging was 75.00%, 78.26%, 67.74%, 84.38% and 58.33% respectively.
 Conclusion: The study concluded that diagnostic accuracy of MRS is more helpful and reliable than DWI in grading of gliomas taking histopathology as gold standard. Future studies at multiple setups must be conducted which could help in deciding whether to rely on MRS or DWI for determination of gliomas grading.

Occurrence of Bleeding Complications of Radial and Femoral Artery Routes in Patients Undergoing Percutanous Coronary Intervention

Background: Percutaneous coronary angiography and percutaneous trans-luminal coronary angioplasty are the standard diagnostic and therapeutic strategies for coronary artery disease [1]. Angiography/ percutaneous coronary intervention (PCI) is usually performed by the femoral and radial artery routes. However, the ulnar artery can also be used [2]. Conventionally, the femoral route has been favored for coronary angiography and percutaneous coronary intervention, but there is growing interest for radial access due to reduced hemorrhagic complications during and after the procedure. This is due to the fact that severe bleeding is considered a determinant of poor prognosis in patients with acute coronary syndrome [3]. In the current practice of interventional cardiology, it has been recognized that bleeding has become the most common early complication associated with PCI [4]. Objective: To determine the frequency of bleeding complications of radial and femoral artery routes (major bleeding and localized hematoma formation) in patients undergoing Percutanous Coronary Intervention. Setting: This cross sectional study Department of Cardiology, Cardiology, AFIC & National Institute of Heart Diseases Rawalpindi. Methodology: Patients fulfilling the inclusion criteria was recruited for the study after informed consent and explanation about risks and benefits of the procedure. Bleeding complications was determined in transradial or transfemoral access groups undergoing Percutanous Coronary Intervention. The data was gathered through a pre-tested questionnaire after informed consent. Every patient was given an ID, Privacy of the patient was ensured. Post procedure patient was examined immediately for localized swelling, hematoma formation and hemoglobin were assessed. Results: Total 205 patients were included according to the inclusion criteria of the study. Mean age (years) in the study was 53.51 ± 13.97 whereas there were 146 (71.2) male and 59 (28.8) female patients who were included in the study according to the inclusion criteria. Frequency and percentage of localized haematoma among radial and femoral artery was 02 (2.1) and 06 (5.6) respectively and retroperitonial haematoma among radial and femoral artery was 8 (8.2) and 17 (15.7) respectively. Conclusion: The study concluded that common bleeding complications were observed more in femoral artery route then radial artery route. Future studies must be conduct at multiple setups to know the proportion of bleeding complication either in radial or femoral artery route, so that to suggest a uniform protocol for the management of patients undergoing percutaneous coronary intervention (PCI).

Volume staging for arteriovenous malformation SRS treatment using VMAT.

Volume staging involves dividing the target volume into smaller parts and treating each part separately. In this study, the feasibility of volume-staged stereotactic radiosurgery (VS-SRS) on a linear accelerator using volumetric modulated arc therapy (VMAT) and a frameless patient positioning system is investigated. Ten patients, previously treated with hypofractionated stereotactic radiotherapy with arteriovenous malformation (AVM) sized from 1.6 to 4.0cm in diameter, were selected. VS-SRS plans were created with the VMAT technique on the Varian Eclipse treatment planning system (TPS) using the TrueBeam STx linear accelerator. For each patient, an AVM-VMAT set was planned with the AVM as the target and a PTV-VMAT set using the (PTV=AVM+1mm) as the target. All targets were divided into two sub-volumes. The TPS data from the AVM-VMAT plans was compared to Gamma Knife (GK) VS-SRS plan data available in the literature. The AVM-VMAT and PTV-VMAT plans were compared to investigate the effect of a 1mm PTV margin on normal brain (NB) dose. End-to-end testing was performed using a GaFchromic EBT3 film and point-dose measurements. Dosimetric effects of multiple setups were investigated through film-to-film comparisons. Median target dose coverage, NB V12Gy , and conformity index for the AVM-VMAT plans were 97.5%, 17cm3 , and 0.8, respectively. PTV-VMAT plans attained comparable target dose coverage, but the average NB V12Gy increased by 48.9% when compared to the AVM-VMAT plans. Agreement of point-dose measurements with TPS calculations was -0.6% when averaged over all patients. Gamma analysis passing rates were above 90% for all film-to-film comparisons (2%/1mm criteria), and for the film to TPS comparison (5%/1mm). This work suggests that VMAT is capable of producing VS-SRS plans with similar dose falloff characteristics as GK plans. NB dose depends on PTV margin size, and two-stage treatment setups do not appear to contribute additional uncertainty to treatment delivery.

Robust optimization of VMAT for prostate cancer accounting for geometric uncertainty.

The aim of this study was to propose optimal robust planning by comparing the robustness with setup error with the robustness of a conventional planning target volume (PTV)‐based plan and to compare the robust plan to the PTV‐based plan for the target and organ at risk (OAR). Data from 13 patients with intermediate‐to‐high‐risk localized prostate cancer who did not have T3b disease were analyzed. The dose distribution under multiple setup error scenarios was assessed using a conventional PTV‐based plan. The clinical target volume (CTV) and OAR dose in moving coordinates were used for the dose constraint with the robust plan. The hybrid robust plan added the dose constraint of the PTV‐rectum to the static coordinate system. When the isocenter was shifted by 10 mm in the superior–inferior direction and 8 mm in the right‐left and anterior directions, the doses to the CTV, bladder, and rectum of the PTV‐based plan, robust plan, and hybrid robust plan were compared. For the CTV D99% in the PTV‐based plan and hybrid robust plan, over 95% of the prescribed dose was secured in all directions, except in the inferior direction. There was no significant difference between the PTV‐based plan and the hybrid robust plan for rectum V70Gy, V60Gy, and V40Gy. This study proposed an optimization method for patients with prostate cancer. When the setup error occurred within the PTV margin, the dose robustness of the CTV for the hybrid robust plan was higher than that of the PTV‐based plan, while maintaining the equivalent OAR dose.

An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic

Several research works have addressed the different aspects and technologies associated with Smart Manufacturing Systems (SMS); however, the evaluation challenges while establishing a new SMS that requires pre-implementation planning and assessment have given little attention. To overcome this limitation, this paper formulates an evaluation framework by identifying apparent evaluation factors to measure the effectiveness of a particular SMS configuration before implementation. Three factors from the literature studies have been used as inputs to control the final output of the configuration modal. Compositions were manipulated based on how factors affected the manufacturing cost justification in multiple setups. Different configurations were analyzed based on the trained Fuzzy Logic model by configurations and based on the trained Fuzzy Logic model using MATLAB’s Fuzzy Logic Designer tool. Results obtained from the evaluation performed by various configuration experiments were later presented to actual field engineers from the manufacturing industry to evaluate the satisfaction level of the evaluation framework. The result showed that this proposed configuration model has a satisfactory rate of 83.7%, as this was achieved by significant feedback from field engineers. This study has significantly facilitated the identification of influential factors and the measured relationship of the factors in the formulated configurations, enabling the best configuration approach to be identified. Therefore, it can be concluded that a visualized and measured configuration system can influence decision-making in the manufacturing industry, thus allowing manufacturers to stay competitive by making well-versed decisions proactively. Exclusively, this research has staged a framework for the industry to follow suit and adapt for future research work related to the SMS field.

Dynamic performance investigation of a long-span suspension bridge using a Bayesian approach

In the past few decades, many large-scale bridges have been built to meet the different needs of styles and characteristics. Large bridges usually carry heavy traffic, and some bridges work in harsh environments. All these make the condition assessment of bridges very significant. On-site vibration testing is a general way to obtain the real response of the structure. Among a variety of measurements, the ambient vibration test has become popular due to its convenient usage and better economics. Based on the vibration data, modal identification can be used to identify the modal parameters that represent the dynamic characteristics of a structure. In this study, before being opened to traffic, a short-term non-destructive field vibration test was conducted on a newly-built long-span suspension bridge. Due to the limited number of sensors, multiple setups were designed to complete the entire measurement. For the measurement locations on the bridge deck, three-dimensional coordinates were determined using an electronic level. Analysis was performed based on the fast Bayesian FFT method, which can provide theoretical estimates for the posterior uncertainty and the most probable values of modal parameters. Nine modes of the bridge deck and four modes of the bridge tower were determined. The analysis results are discussed, and the posterior coefficient of variations (COVs) and sample COVs in different setups are compared. The analysis results in a healthy state can be used to verify the finite element model built in the design stage. It can also provide a benchmark for the future SHM of the bridge and improve the decision-making during usage and maintenance.

Spectral Considerations for Standoff Infrared Detection of RDX on Reflective Aluminum.

This paper examines infrared spectroscopic effects for the standoff detection of an explosive material, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), inkjet printed on an aluminum surface. Results of a spectroscopic study are described, using multiple optical setups. These setups were selected to explore how variations in the angles of incidence and collection from the surface of the material result in corresponding variations in the spectral signatures. The goal of these studies is to provide an understanding of these spectral changes since it affects standoff detection of hazardous materials on a reflective substrate. We demonstrate that variations in spectral effects are dependent on the relative surface concentration of the deposited RDX. We also show that it is reasonable to use spectroscopic data collected in a standard laboratory infrared spectrometer outfitted with a variable angle reflectometer set at 0° as reference spectra for data collected in a standoff configuration. These results are important to provide a systematic approach to understanding infrared (IR) spectra collection using standoff systems in the field, and to allow for comparison between such data, and data collected in the laboratory. Although the precise results are constrained to a specific material system (thin layers on a reflective substrate), the approach and general discussion provided are applicable to a broad range of IR standoff sensing techniques and applications.

Some results on multiple regression analysis with data cleaned by trimming and winsorization

In a multiple regression model setup, we examine the effects of trimming and winsorization of regressors on the regression estimates and their efficiency. The exact results developed here to enable us to evaluate the effects of winsorizing one regressor on the estimates of the coefficients associated with another regressor. These results have important implications in accounting and finance literature wherein trimming or winsorization is frequently adopted before performing regression analysis.

Uncertainty quantification in Bayesian operational modal analysis with multiple modes and multiple setups

In full-scale ambient vibration tests, multiple setups are often performed for measurement when it is demanded to obtain a detailed mode shape with more measured degrees of freedom than the available number of synchronous data channels. In a previous work, a Bayesian operational modal analysis framework for the general case of multiple modes identified with ambient data from multiple setups was developed, together with an Expectation-Maximisation algorithm for efficiently calculating the most probable value (MPV) of modal parameters. Complementing the previous effort, this work investigates the posterior uncertainty of the modal parameters in terms of their posterior covariance matrix. Mathematically, the posterior covariance matrix is equal to the inverse of the Hessian of negative log-likelihood function at the MPV. The computational issues are investigated and analytical expressions for the Hessian matrix are derived, allowing the covariance matrix to be determined efficiently and accurately without resorting to the finite difference method. The proposed algorithm is verified with synthetic data, where the Bayesian and frequentist statistics are compared, and the effect of reference location is investigated. The developed computational tools are applied to investigate identification uncertainty with field data, where associated practical issues are also discussed.

Estimate the Unknown Environment with Biosonar Echoes-A Simulation Study.

Unmanned aerial vehicles (UAVs) have shown great potential in various applications such as surveillance, search and rescue. To perform safe and efficient navigation, it is vitally important for a UAV to evaluate the environment accurately and promptly. In this work, we present a simulation study for the estimation of foliage distribution as a UAV equipped with biosonar navigates through a forest. Based on a simulated forest environment, foliage echoes are generated by using a bat-inspired bisonar simulator. These biosonar echoes are then used to estimate the spatial distribution of both sparsely and densely distributed tree leaves. While a simple batch processing method is able to estimate sparsely distributed leaf locations well, a wavelet scattering technique coupled with a support vector machine (SVM) classifier is shown to be effective to estimate densely distributed leaves. Our approach is validated by using multiple setups of leaf distributions in the simulated forest environment. Ninety-seven percent accuracy is obtained while estimating thickly distributed foliage.