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In typical model-based calibration, linearization errors are derived inevitably, and non-negligible negative impact will be induced on the identification results if the rotational kinematic errors are not small enough or the lengths of links are too long, which is common in the industrial cases. Thus, an accurate two-step kinematic calibration method minimizing the linearization errors is presented for a six-DoF serial robot to improve the calibration accuracy.

The negative impact of linearization on identification accuracy is minimized by removing the responsible linearized kinematic errors from the complete kinematic error model. Accordingly, the identification results of the dimension-reduced new model are accurate but not complete, so the complete kinematic error model, which achieves high identification accuracy of the rest of the error parameters, is combined with this new model to create a two-step calibration procedure capable of highly accurate identification of all the kinematic errors.

The proportions of linearization errors in measured pose errors are quantified and found to be non-negligible with the increase of rotational kinematic errors. Thus, negative impacts of linearization errors are analyzed quantitatively in different cases, providing the basis for allowed kinematic errors in the new model. Much more accurate results were obtained by using the new two-step calibration method, according to a comparison with the typical methods.

This new method achieves high accuracy with no compromise on completeness, is easy to operate and is consistent with the typical method because the second step with the new model is conveniently combined without changing the sensors or measurement instrument setup.

The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

This paper aims to provide an integrated BIM-based approach for quantity take-off for progress payments in the context of high-rise buildings in Vietnam. It tries to find answers for the following questions: (1) When to start the QTO processes to facilitate the contract progress payments? (2) What information is required to measure the quantity of works to estimate contract progress payment (3) What are the challenges to manage (i.e. create, store, update and exploit)? What are the required information for this BIM use? and (4) How to process the information to deliver BIM-based QTO to facilitate contract progress payment?

The paper applied a deductive approach and expert consensus through a Delphi procedure to adapt to current innovation around BIM-based QTO. Starting with a literature review, it then discusses current practices in BIM-based QTO in general and high-rise building projects in particular. Challenges were compiled from the previous studies for references for BIM-based QTO to facilitate contract progress payment for high-rise building projects in Vietnam. A framework was developed considering a standard information management process throughout the construction lifecycle, when the BIM use of this study is delivered. The framework was validated with Delphi technique.

Four major challenges for BIM-based QTO discovered: new types of information required for the BIM model, changes and updates as projects progress, low interoperability between BIM model and estimation software, potentiality of low productivity and accuracy in data entry. Required information for QTO to facilitate progress payments in high-rise building projects include Object Geometric/Appearance Information, Structural Components' Definition and Contextual Information. Trade-offs between “Speed – Level of Detail–Applicable Breadth” and “Quality – Productivity” are proposed to consider the information amount to input at a time when creating/updating BIM objects. Interoperability check needed for creating, authoring/updating processing the BIM model's objects.

This paper is not flawless. The first limitation lies in that the theoretical framework was established only based on desk research and small number of expert judgment. Further primary data collection would be needed to determine exactly how the framework underlies widespread practices. Secondly, this study only discussed the quantity take-off specifically for contract progress payment, but not for other purposes or broader BIM uses. Further research in this field would be of great help in developing a standard protocol for automatic quantity surveying system in Vietnam.

A new theoretical framework for BIM-based QTO validated with Delphi technique to facilitate progress payments for high-rise building projects, considering all information management stages and the phases of information development in the project lifecycle. The framework identified four types of information required for this QTO, detailed considerations for strategies (Library Objects Development, BIM Objects Information Declaration, BIM-based QTO) for better managing the information for this BIM use. Two trade-offs of “Speed – LOD–Applicable Breadth” and “Quality – Productivity” have been proposed for facilitating the strategies and also for enhancing the total efficiency and effectiveness of the QTO process.

This paper aims to investigate the problem of estimating the angle of attack (AoA) and relative velocity for vertical axis wind turbine (VAWT) blades from computational fluid dynamics data.

Two methods are implemented as function objects within the OpenFOAM framework for estimating the blade’s AoA and relative velocity. For the numerical analysis of the flow around and through the VAWT, 2 D unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are carried out and validated against experimental data.

To gain a better understanding of the complex flow features encountered by VAWT blades, the determination of the AoA is crucial. Relying on the geometrically-derived AoA may lead to wrong conclusions about blade aerodynamics.

This study can lead to the development of more robust optimization techniques for enhancing the variable-pitch control mechanism of VAWT blades and improving low-order models based on the blade element momentum theory.

Assessment of the reliability of AoA and relative velocity estimation methods for VAWT’ blades at low-Reynolds numbers using URANS turbulence models in the context of dynamic stall and blade–vortex interactions.

This study aims to enhance the fuel efficiency of jet transport aircraft based on mathematical models and flight crew operating manual (FCOM) for the purpose to assist the civil aviation industry in improving flight safety and operational efficiency.

The research applies flight data mining and fuzzy logic modeling technologies to set up lift-to-drag ratio (L/D) models and nine models of thrust, Mach number, engine pressure ratio and fuel flow rate to estimate the deviation of each flight parameter. All performance deviations are calculated based on the values of flight data recorded in the quick access recorder and FCOM at the observed flight conditions. The L/D model can obtain the influence of each flight parameter and estimate the insufficient amount of each parameter by averaging it with the least square method. In the estimation of optimal altitude, nine models are built based on data from FCOM to estimate the optimal altitude and complete comparative analysis of the airspeed, Mach number and fuel flow rate at the optimal altitude.

Analyze 11 relevant parameters from the sensitivity derivative of L/D model to obtain how each parameter affected fuel consumption and explore the causes of additional fuel consumption. Complete the estimation of the optimal cruise altitude of the aircraft, and calculate the comparative analysis of the altitude, speed, Mach number and other parameters with the sensitivity derivative of the L/D. The estimation of the optimal cruise altitude of the aircraft can meet the analysis of the sensitivity derivative.

This study is to enhance the fuel efficiency of jet commercial transport based on mathematical model and FCOM. FCOM is required to conduct this study. The estimation of the optimal cruise altitude through the nine models of the aircraft could meet the analysis of the sensitivity derivative.

The object of present research is to demonstrate the effectiveness of optimization of flight conditions through model analysis to get knowledge of the effects of each influencing flight variable to L/D for future flight operations’ reference.

The model-based derivative analysis had the ability to perform derivative prediction analysis on any input parameters, more flight parameters could be optimized in future research to help airlines improve flight safety and operational efficiency.

The present enhancement method of fuel efficiency is an innovation to examine the abnormal aircraft performance and its flight operations, thereby to explore the causes of additional fuel consumption. The present method can become an auxiliary tool for flight operations quality assurance to improve fuel efficiency for the airlines.

This study aims to explore entropy evaluation in the bi-directional flow of Casson hybrid nanofluids within a stagnated domain, a topic of significant importance for optimizing thermal systems. The aim is to investigate the behavior of unsteady, magnetized and laminar flow using a parametric model based on the thermo-physical properties of alumina and copper nanoparticles.

The research uses boundary layer approximations and the Keller-box method to solve the derived ordinary differential equations, ensuring numerical accuracy through convergence and stability analysis. A comparison benchmark has been used to authenticate the accuracy of the numerical outcomes.

Results indicate that increasing the Casson fluid parameter (ranging from 0.1 to 1.0) reduces velocity, the Bejan number decreases with higher bidirectional flow parameter (ranging from 0.1 to 0.9) and the Nusselt number increases with higher nanoparticle concentrations (ranging from 1% to 4%).

This study has limitations, including the assumption of laminar flow and the neglect of possible turbulent effects, which could be significant in practical applications.

The findings offer insights for optimizing thermal management systems, particularly in industries where precise control of heat transfer is crucial. The Keller-box simulation method proves to be effective in accurately predicting the behavior of such complex systems, and the entropy evaluation aids in assessing thermodynamic irreversibilities, which can enhance the efficiency of engineering designs.

These findings provide valuable insights into the thermal management of hybrid nanofluid systems, marking a novel contribution to the field.

Customer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers.

This study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including “the probability that customers will return in the future” as a key input for CLV calculation.

The CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins.

The integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings.

This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.

The study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O₃ index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.

The proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.

The study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.

The findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.

While primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.

This study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.