8 research outputs found
Development of Feature Recognition Algorithm for Automated Identification of Duplicate Geometries in CAD Models
No full textThis research presents a feature recognition algorithm for the automated identification of duplicate geometries in the CAD assembly. The duplicate geometry is one of the seven indicators of the lazy parts mass reduction method. The lazy parts method is a light weight engineering method that is used for analyzing parts with the mass reduction potential. The duplicate geometry is defined as any geometries lying equal to or within the threshold distance with the user-defined orientation between them and have the percentage similarity that is equal to or greater than the threshold value. The feature recognition system developed in this research for the identification of duplicate geometries is also extended to retrieve the weighted bipartite graph of part connections for the assembly time estimation. The weighted bipartite graph is used as input for the part connectivity based assembly time estimation method. The SolidWorks API software development kit is used in this research to develop a feature recognition system in SolidWorks CAD software package using C++ programming language. The feature recognition system built in the SolidWorks CA
Development of Feature Recognition Algorithm for Automated Identification of Duplicate Geometries in CAD Models
Get PDFThis research presents a feature recognition algorithm for the automated identification of duplicate geometries in the CAD assembly. The duplicate geometry is one of the seven indicators of the lazy parts mass reduction method. The lazy parts method is a light weight engineering method that is used for analyzing parts with the mass reduction potential. The duplicate geometry is defined as any geometries lying equal to or within the threshold distance with the user-defined orientation between them and have the percentage similarity that is equal to or greater than the threshold value. The feature recognition system developed in this research for the identification of duplicate geometries is also extended to retrieve the weighted bipartite graph of part connections for the assembly time estimation. The weighted bipartite graph is used as input for the part connectivity based assembly time estimation method. The SolidWorks API software development kit is used in this research to develop a feature recognition system in SolidWorks CAD software package using C++ programming language. The feature recognition system built in the SolidWorks CAD software uses a combination of topology and geometric data for the evaluation of duplicate geometry. The measurement of distances between the sampling points strategy is used for the duplicate geometry feature recognition. The feature recognition algorithm has three phases of evaluation: first, is the evaluation for threshold distance condition of parts in the CAD assembly. Second, the part pairs that have satisfied the threshold distance condition are evaluated for the orientation condition. The threshold distance and orientation are the necessary but not the sufficient conditions for duplicate geometries. In the third phase, the geometries that have satisfied orientation condition are evaluated for the percentage similarity condition. The geometries that satisfy the percentage similarity condition are highlighted in order to help designers review the results of the duplicate geometry analysis. The test cases are used to validate the algorithm against the requirements list. The test cases are designed to check the performance of the algorithm for the evaluation of the threshold distance, orientation, and percentage similarity condition. The results indicate that the duplicate geometry algorithm is able to successfully conduct all the three phases of evaluation. The algorithm is independent of the geometric type and is able to analyze planar, cylindrical, conical, spherical, freeform, and toroidal shapes. The number of sampling points generated on the faces of parts for the orientation and percentage similarity evaluation has the significant effect on the analysis time. The worst case complexity of the algorithm is the big O (nC2x m12 x m22x p4), where n = the number of parts in the assembly m1 = the number of faces in the parts that meet the threshold distance condition m2 = the number of faces that meet the orientation condition p = the number of sampling points on the face The duplicate geometry feature recognition approach is used to demonstrate the applicability in the extraction of assembly relations for the part connectivity based assembly time estimation method. The algorithm is also able to extract part connectivity information for the patterns. Further research is required to automate the identification of other laziness indicators in order to make the lazy parts method a completely automated tool. With regards to the complete automation of part connectivity based assembly time estimation method, the duplicate geometry feature recognition system needs integration with the algorithm for the computation of bipartite graph of part connections for the prediction of assembly time
Representation: Extracting Mate Complexity From Assembly Models to Automatically Predict Assembly Times
No full textEvaluation and Comparison of Two Design for Assembly Methods: Subjectivity of Information Inputs
No full text
Author Correction: COVIDiSTRESS diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic (Scientific Data, (2022), 9, 1, (331), 10.1038/s41597-022-01383-6)
No full textThe original version of this Article contained an error in the spelling of the author Krzysztof Hanusz, which was incorrectly given as Hanusz Krzysztof. This has now been corrected in both the PDF and HTML versions of the Article. © The Author(s) 2023
Evaluating a community-based public health intervention using a complex systems approach
No full text© The Author 2017. Background This article outlines the methods being used to evaluate a community-based public health intervention. This evaluation approach recognizes that not only is the intervention, Healthy Families NZ, complex, but the social systems within which it is being implemented are complex. Methods To address challenges related to complexity, we discuss three developing areas within evaluation theory and apply them to an evaluation case example. The example, Healthy Families NZ, aims to strengthen the prevention system in Aotearoa/New Zealand to prevent chronic disease in 10 different geographic areas. Central to the evaluation design is the comparative case method which recognizes that emergent outcomes are the result of 'configurations of causes'. 'Thick', mixed-data, case studies are developed, with each case considered a view of a complex system. Qualitative Comparative Analysis is the analytical approach used to systematically compare the cases over time. Conclusions This article describes an approach to evaluating a community-based public health intervention that considers the social systems in which the initiative is being implemented to be complex. The evaluation case example provides a unique opportunity to operationalize and test these methods, while extending their more frequent use within other fields to the field of public health
COVIDiSTRESS diverse dataset on psychological and behavioural outcomes one year into the COVID-19 pandemic
Get PDFDuring the onset of the COVID-19 pandemic, the COVIDiSTRESS Consortium launched an open-access global survey to understand and improve individuals’ experiences related to the crisis. A year later, we extended this line of research by launching a new survey to address the dynamic landscape of the pandemic. This survey was released with the goal of addressing diversity, equity, and inclusion by working with over 150 researchers across the globe who collected data in 48 languages and dialects across 137 countries. The resulting cleaned dataset described here includes 15,740 of over 20,000 responses. The dataset allows cross-cultural study of psychological wellbeing and behaviours a year into the pandemic. It includes measures of stress, resilience, vaccine attitudes, trust in government and scientists, compliance, and information acquisition and misperceptions regarding COVID-19. Open-access raw and cleaned datasets with computed scores are available. Just as our initial COVIDiSTRESS dataset has facilitated government policy decisions regarding health crises, this dataset can be used by researchers and policy makers to inform research, decisions, and policy. © 2022, The Author(s).U.S. Department of Education, ED: P031S190304; Texas A and M International University, TAMIU; National Research University Higher School of Economics, ВШЭThe COVIDiSTRESS Consortium would like to acknowledge the contributions of friends and collaborators in translating and sharing the COVIDiSTRESS survey, as well as the study participants. Data analysis was supported by Texas A&M International University (TAMIU) Research Grant, TAMIU Act on Ideas, and the TAMIU Advancing Research and Curriculum Initiative (TAMIU ARC) awarded by the US Department of Education Developing Hispanic-Serving Institutions Program (Award # P031S190304). Data collection by Dmitrii Dubrov was supported within the framework of the Basic Research Program at HSE University, RF
