Search results

The paper aims to propose a generation approach for unstructured surface mesh to speed up mesh generation.

The paper proposes a lightweight interactive generation approach for unstructured surface mesh and presents several key technologies to support this approach.

The experimental results show that the proposed approach is feasible for unstructured meshes and it can accelerate the mesh generation process.

More geometric defects should be covered, and more convenient and efficient interactive means need to be provided.

The proposed approach and key technologies are implemented in NNW-GridStar.UG, which is the unstructured version of the mesh generation software of National Numerical Windtunnel (NNW).

This paper proposes a lightweight interactive approach for unstructured surface mesh generation, which can speed up mesh generation.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

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.

This study establishes an ontology-based framework for rework risk identification (RRI) by integrating heterogeneous data from the information flow of the prefabricated construction (PC) process. The main objective is to enhance the automation level of rework management and reduce the degree of reliance on human factors and manual operations.

The proposed framework comprises four levels aimed at managing dispersed rework risk knowledge and integrating heterogeneous data. The functionalities were realised through an integrated ontology that aligned the rework risk ontology with the PC ontology. The ontologies were developed and edited with Protégé. Ultimately, the potential benefit of the framework was validated through a case study and an expert questionnaire survey.

The framework is proven to effectively manage rework risk knowledge and can identify risk objects, clarify risk factors, determine risk events, and retrieve risk measures, thereby enabling the pre-identification of prefabricated rework risk (PRR) and improving the automation level. This study is meaningful and lays the foundation for the application of other computer methods in rework management research and practice in the future.

This research provides insights into the application of ontology to solve rework risk issues in the PC process and introduces a novel risk management method for future prefabricated project research and practice. The findings have significant theoretical value in terms of enriching the methods of risk assessment and control and the information management system of prefabricated projects.