Automatic Model Reconstruction Research Articles

Design of an Automobile Injection Mould Based on Automation Technology

At present, there are frequent occurrences in the industry of manufacturers only having products, but they have no product data, resulting in manufacturers unable to open molds to produce the products themselves and can only buy them at high prices. This article takes the rear case of an automobile generator produced by a company as an example, which aims to show the design of automobile injection molds through automation technology. This paper studies the use of automation technology to achieve the automatic model reconstruction of products, and uses the CAD technology to design injection molds based on the obtained digital models of products. This paper also uses the CAE technology to verify the feasibility and rationality of the designed mold, exploring the optimization method of the injection molding process of the mold. In this paper, a special product pouring system and demolding mechanism are designed according to the product process requirements. The design greatly simplifies the mold structure while fulfilling the product process requirements, which avoids the use of expensive hot runners and complex flip-chip mold structures, reducing the mold manufacturing cost. Secondly, the overall design of the injection mold is carried out, and the external dimensions and other main parameters of the mold are determined. Then, Moldflow software is used to simulate the injection molding process, verifying the feasibility and rationality of the designed mold. The optimization method of the injection molding process is also proposed, and the optimal injection process parameters are determined to ensure that the mold can produce qualified products and ensure high production efficiency. It can be seen from the analysis results that the maximum warpage deformation of the plastic part under all effects is 0.65 mm, which meets the product process requirements.

Automatic modeling of prefabricated components with laser‐scanned data for virtual trial assembly

AbstractVirtual trial assembly (VTA) simulates the preassembly by verifying the correspondence of as‐built and as‐designed prefabricated component (PC) models in virtual reality. Notably, obtaining accurate as‐built PC models ensures the success of VTA. To accomplish it, current methods require matching the scanned PC data with the as‐designed model and manually modifying the model to approximate the actual dimensions, which is inefficient and leads to subjective errors. Therefore, it is desirable to develop an automatic modeling approach to obtain reliable as‐built PC models for further improving the efficiency of VTA. However, to the best of our knowledge, few studies have been done on the automatic modeling of as‐built PC models in VTA procedure. To this end, based on the prior knowledge of as‐designed model, this study proposes an approach to automatically reconstruct the primary structure via plane fitting and structural feature point calculation for geometric modeling. Then, two adaptive methods for inner boundary detection and round‐hole estimation are developed for the reconstruction of detailed structure. Finally, as‐built model visualization is realized based on the data extraction and modeling development of a building information modeling software. To verify the feasibility of the proposed approach, a validation experiment was conducted on a full‐scale precast concrete stair using two scanning resolutions (1/5 and 1/8). Experiment results show that the estimation of the dimensions consists of multifaceted corners that are stable and not affected by scanning resolution. Low scan resolution (1/8) may increase estimation errors of the edge, which has a single‐sided turning point. The experiment results also confirm the effectiveness of the proposed approach for the automatic model reconstruction of as‐built PCs.

Constraint-based modelling captures the metabolic versatility of Desulfovibrio vulgaris.

A refined Desulfovibrio vulgaris Hildenborough flux balance analysis (FBA) model (iJF744) was developed, incorporating 1016 reactions that include 744 genes and 951 metabolites. A draft model was first developed through automatic model reconstruction using the ModelSeed Server and then curated based on existing literature. The curated model was further refined by incorporating three recently proposed redox reactions involving the Hdr-Flx and Qmo complexes and a lactate dehydrogenase (LdhAB, DVU 3027-3028) indicated by mutation and transcript analyses to serve electron transfer reactions central to syntrophic and respiratory growth. Eight different variations of this model were evaluated by comparing model predictions to experimental data determined for four different growth conditions - three for sulfate respiration (with lactate, pyruvate or H2 /CO2 -acetate) and one for fermentation in syntrophic coculture. The final general model supports (i) a role for Hdr-Flx in the oxidation of DsrC and ferredoxin, and reduction of NAD+ in a flavin-based electron confurcating reaction sequence, (ii) a function of the Qmo complex in receiving electrons from the menaquinone pool and potentially from ferredoxin to reduce APS and (iii) a reduction of the soluble DsrC by LdhAB and a function of DsrC in electron transfer reactions other than sulfite reduction.

TENSOR-BASED QUALITY PREDICTION FOR BUILDING MODEL RECONSTRUCTION FROM LIDAR DATA AND TOPOGRAPHIC MAP

Abstract. A quality prediction method is proposed to evaluate the quality of the automatic reconstruction of building models. In this study, LiDAR data and topographic maps are integrated for building model reconstruction. Hence, data registration is a critical step for data fusion. To improve the efficiency of the data fusion, a robust least squares method is applied to register boundary points extracted from LiDAR data and building outlines obtained from topographic maps. After registration, a quality indicator based on the tensor analysis of residuals is derived in order to evaluate the correctness of the automatic building model reconstruction. Finally, an actual dataset demonstrates the quality of the predictions for automatic model reconstruction. The results show that our method can achieve reliable results and save both time and expense on model reconstruction.

A Quality Prediction Method for Building Model Reconstruction Using LiDAR Data and Topographic Maps

This paper integrates light detection and ranging (LiDAR) data and topographic maps and predicts the quality of 3-D building model reconstruction. In this paper, the tensor voting algorithm and a region-growing method are adopted to extract building roof planes and structural lines from LiDAR data, and a robust least squares method is applied to register LiDAR data with building outlines obtained from topographic maps. The minimal square sum of the separations of the most peripheral points to building outlines is adopted as the criterion for determining the transformation parameters in order to improve the efficiency of data fusion. After registration, a novel quality indicator of data fusion based on the tensor analysis of residuals is derived in order to evaluate the quality of the automatic reconstruction of 3-D building models. Finally, an actual LiDAR data set and its corresponding topographic map demonstrate the fusion procedure and the quality of the predictions related to automatic model reconstruction.

Automatic Model Simplification and Reconstruction from Geographic Information System Data for Computer-Aided Engineering

AbstractThis paper describes an automatic reconstruction methodology for the creation of 3D models suitable for Computational Fluid Dynamics (CFD) simulation. The objective is to design and develop an automatic model reconstruction methodology to convert Geographic Information System (GIS) data to CAD data which can be used directly for finite element mesh generation. This will equip users with a reliable and effective tool for the rapid reconstruction of geometrical models which satisfy their computational requirements and simulation contraints. Specifically, the reconstruction algorithm will be able to automatically process building data in the ESRI shapefile format, suppress unnecessarily detailed features, resolve non-manifold topology, cluster buildings which are close together and export the 3D model into a universal exchange format suitable for the intended CFD application.