Search results
1 – 10 of 239Siming Cao, Hongfeng Wang, Yingjie Guo, Weidong Zhu and Yinglin Ke
In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance…
Abstract
Purpose
In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.
Design/methodology/approach
A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.
Findings
Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.
Originality/value
First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.
Details
Keywords
This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking…
Abstract
Purpose
This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.
Design/methodology/approach
The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.
Findings
The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.
Originality/value
The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).
Details
Keywords
Meike Huber, Dhruv Agarwal and Robert H. Schmitt
The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid…
Abstract
Purpose
The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.
Design/methodology/approach
This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration
Findings
The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.
Originality/value
The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.
Details
Keywords
Diana L. Ramírez-Gutiérrez, Enrique Cuan-Urquizo and Rita Q. Fuentes-Aguilar
Demanding applications could benefit from the mathematical parametrization of lattice structures as this could lead not only to the characterization of structure–property relation…
Abstract
Purpose
Demanding applications could benefit from the mathematical parametrization of lattice structures as this could lead not only to the characterization of structure–property relation but also facilitates the tailoring of the effective mechanical properties. This paper aims to characterize the mechanical performance of sine-based lattices. The characterization includes the results of in-plane Poisson’s ratio plates models, and the stiffness of additively manufactured lattice plates when loaded in the out-of-plane direction, with the objective of obtaining a relation with their geometrical parameters.
Design/methodology/approach
The geometrical parameter–Poisson’s ratio relationship was characterized via finite element (FE) simulations. The stiffness was also measured on additively manufactured polylactic acid lattice plates and contrasted with FE computations.
Findings
The characterization of auxetic lattice plates performed using in-plane and out-of-plane loading leads to key properties when deciding the geometry specific for applications: relative density, auxetic behavior and stiffness. Approximately 26% reduction of stiffness was observed between the square lattice and sine-based lattices of the same volume fraction.
Originality/value
Auxetic metamaterials are potential candidates for applications in biomedical engineering, smart sensors, sports and soft robotics. This paper aims to contribute to the existing gap in the study of auxetic metamaterials subjected to complex loading conditions, other than simple tension and compression, required for the mentioned applications.
Details
Keywords
Carolina Bermudo Gamboa, Sergio Martín Béjar, Francisco Javier Trujillo Vilches and Lorenzo Sevilla Hurtado
The purpose of this study is to cover the influence of selected printing parameters at a macro and micro-geometrical level, focusing on the dimensions, geometry and surface of…
Abstract
Purpose
The purpose of this study is to cover the influence of selected printing parameters at a macro and micro-geometrical level, focusing on the dimensions, geometry and surface of printed parts with short carbon fibers reinforced PLA. For this case study, a hollow cylindrical shape is considered, aiming to cover the gap detected in previous works analyzed.
Design/methodology/approach
Nowadays, additive manufacturing plays a very important role in the manufacturing industry, as can be seen through its numerous research and applications that can be found. Within the engineering industry, geometrical tolerances are essential for the functionality of the parts and their assembly, but the variability in three-dimensional (3D) printing makes dimensional control a difficult task. Constant development in 3D printing allows, more and more, printed parts with controlled and narrowed geometrical deviations and tolerances. So, it is essential to continue narrowing the studies to achieve the optimal printed parts, optimizing the manufacturing process as well.
Findings
Results present the relation between the selected printing parameters and the resulting printed part, showing the main deviations and the eligible values to achieve a better tolerance control. Also, from these results obtained, we present a parametric model that relates the geometrical deviations considered in this study with the printing parameters. It can provide an overview of the piece before printing it and so, adjusting the printing parameters and reducing time and number of printings to achieve a good part.
Originality/value
The main contribution is the study of the geometry selected under a 3D printing process, which is important because it considers parts that are created to fit together and need to comply with the required tolerances. Also, we consider that the parametric model can be a suitable approach to selecting the optimal printing parameters before printing.
Details
Keywords
Margarita Ntousia, Ioannis Fudos, Spyridon Moschopoulos and Vasiliki Stamati
Objects fabricated using additive manufacturing (AM) technologies often suffer from dimensional accuracy issues and other part-specific problems. This study aims to present a…
Abstract
Purpose
Objects fabricated using additive manufacturing (AM) technologies often suffer from dimensional accuracy issues and other part-specific problems. This study aims to present a framework for estimating the printability of a computer-aided design (CAD) model that expresses the probability that the model is fabricated correctly via an AM technology for a specific application.
Design/methodology/approach
This study predicts the dimensional deviations of the manufactured object per vertex and per part using a machine learning approach. The input to the error prediction artificial neural network (ANN) is per vertex information extracted from the mesh of the model to be manufactured. The output of the ANN is the estimated average per vertex error for the fabricated object. This error is then used along with other global and per part information in a framework for estimating the printability of the model, that is, the probability of being fabricated correctly on a certain AM technology, for a specific application domain.
Findings
A thorough experimental evaluation was conducted on binder jetting technology for both the error prediction approach and the printability estimation framework.
Originality/value
This study presents a method for predicting dimensional errors with high accuracy and a completely novel approach for estimating the probability of a CAD model to be fabricated without significant failures or errors that make it inappropriate for a specific application.
Details
Keywords
Tugrul Oktay and Yüksel Eraslan
The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design…
Abstract
Purpose
The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.
Design/methodology/approach
The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.
Findings
Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.
Originality/value
This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.
Details
Keywords
Shun-Peng Zhu, Xiaopeng Niu, Behrooz Keshtegar, Changqi Luo and Mansour Bagheri
The multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In…
Abstract
Purpose
The multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.
Design/methodology/approach
In this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.
Findings
The accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.
Originality/value
The results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.
Details
Keywords
Niranjan Chikkanna, Shankar Krishnapillai and Velmurugan Ramachandran
Auxetics are the class of cellular materials with a negative Poisson’s ratio. This paper aims to study the low-cost 3D printing capabilities and printing variations and improve…
Abstract
Purpose
Auxetics are the class of cellular materials with a negative Poisson’s ratio. This paper aims to study the low-cost 3D printing capabilities and printing variations and improve the indentation performance of the re-entrant diamond auxetic metamaterial by tuning the structural parameters that have not been reported.
Design/methodology/approach
The design of experiment strategy was adopted to study the influence of re-entrant angle, diamond angle and thickness-to-length ratio on relative density, load, stiffness and specific energy absorption (SEA) during indentation experimentally. Grey relational analysis was chosen as a multi-objective optimisation technique to optimise structural performance. Surrogate models were proposed to uphold the metamaterial’s tailorability for desired application needs. The fit and efficacy of the proposed models were tested using specific statistical techniques. The predominant deformation mechanisms observed with the alteration in structural parameters were discussed.
Findings
The improvements noticed are 48 times hike in load, 112 times improvement in stiffness and 10 times increase in SEA for optimised structures. The surrogate models are proven to predict the outputs accurately for new input parameters. In-situ displacement fields are visualised with an image processing technique.
Originality/value
To the best of the authors’ knowledge, the indentation performance of the re-entrant diamond auxetic metamaterials has not been reported and reported for the first time. The influence of geometrical parameters on the newly developed structure under concentrated loading was evaluated. The geometry-dependent printing variations associated with 3D printing have been discussed to help the user to fabricate re-entrant diamond auxetic metamaterial.
Details
Keywords
Neeraj Joshi, Sudeep R. Bapat and Raghu Nandan Sengupta
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).
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Details