Prototype pipeline modelling using interval scanning point clouds

doi:10.1007/s40436-024-00515-y

Advances in Manufacturing ›› 2025, Vol. 13 ›› Issue (2): 444-461.doi: 10.1007/s40436-024-00515-y

Prototype pipeline modelling using interval scanning point clouds

Toa Pečur¹, Frédéric Bosché¹, Gabrielis Cerniauskas¹, Frank Mill¹, Andrew Sherlock², Nan Yu¹

1. School of Engineering, The University of Edinburgh, Edinburgh EH9 3FB, Scotland, UK;
2. National Manufacturing Institute Scotland, Paisley PA3 2EF, Scotland, UK

收稿日期:2024-01-08 修回日期:2024-03-21 发布日期:2025-05-16
通讯作者: Nan Yu,E-mail:nan.yu@ed.ac.uk E-mail:nan.yu@ed.ac.uk
作者简介:Toa Pečur Ph.D. candidate at the Institute for Materials and Processes at the University of Edinburgh (UoE). Having completed his Master of Engineering with Honours in Mechanical Engineering with Management in UoE, his current focus is on large scale metrology with AI digital aspects when manufacturing.
Frédéric Bosché Reader in Construction Informatics at the Institute for Infrastructure and Environment, the University of Edinburgh (UoE). Following a Ph.D. in Civil Engineering at the University of Waterloo (Canada), he worked for 2 years as researcher in the Computer Vision Laboratory at ETH Zurich, before becoming Assistant Professor in Construction Informatics at Heriot-Watt University. In 2019, He joined the UoE where he was first Senior Lecturer and now Reader. He teaches on construction management and informatics, and leads the Cyber Build Lab that delivers research and innovation in these areas.
Gabrielis Cerniauskas Postdoctoral Researcher at the Institute for Materials and Processes, School of Engineering at the University of Edinburgh. His current research investigates thermoplastic liquid composite moulding and strives towards a more sustainable development of glass and carbon composites. Recently, he obtained a Ph.D. degree in computational design and development of mechanical metamaterials, with a focus on leveraging optimisation and machine learning methods for efficient design processes. Prior to this, he obtained a Master of Engineering degree in Mechanical Engineering from the University of Edinburgh. Dr. Cerniauskas’ research interests lay in computational design, optimisation, machine learning, smart structures, cellular solids and digital manufacturing.
Frank Mill Chartered engineer and Honorary Professor of Digital Design at The University of Edinburgh. He graduated with a B.Sc. (honours: first class) Technology with Industrial Studies in 1983 from Napier College and with a Ph.D. in Shape Optimisation in 2004 from the University of Edinburgh. He has worked with Redcastle Systems and ShapesSpace Ltd. on technical aspects of CAD and he is a co-founder and director of ShapeSpace Ltd. He has carried out extensive research, consultancy and teaching in CAD.
Andrew Sherlock Director of Data-Driven Manufacturing at the National Manufacturing Institute Scotland and Professor of Practice at the University of Strathclyde. His first degree was in Mechanical Engineer and his Ph.D. focused on novel shape optimisation techniques for aerospace components. His subsequent career, both in academia and industry, has focused on the application of AI, data science and search techniques to design and manufacturing. In 2006 he founded ShapeSpace Ltd, a spinout from the University of Edinburgh, to commercialise 3D search-byshape technology, initially developed as a 3D search engine for components and subsequently enhanced to allow analysis of assemblies. This technology has been deployed at a number of large manufacturers in automotive, aerospace and industrial equipment industries where the ability to understake analytics on component portfolios and large numbers of bills of materials has uncovered significant cost savings within the supply chain. Between 2016 and 2019 he was the Royal Academy of Engineering (RAEng) Visiting Professor in Design for Product Profitability at the University of Edinburgh.
Nan Yu Senior lecturer in Digital Manufacture at the Institute for Materials and Processes, the University of Edinburgh (UoE). Before joining UoE, he was trained at Cranfield University (Ph.D.: plasma figuring of large optics), University College Dublin (UCD, as a postdoc in precision manufacturing of medical devices), and the European Organisation of Nuclear Research (CERN, as an Associate Scientist in precision alignment and metrology). His research interests include digitally supported process, precision manufacturing and metrology, plasma technologies. He receives the prestigious Marie Curie International Fellowship (2018-2020), Irish Research Council Fellowship (2020), and Royal Academy of Engineering Industrial Fellowship (2023-2025).
基金资助:
This research was founded by Scottish Research Partnership in Engineering (SRPe), the National Manufacturing Institute Scotland (NMIS), ShapeSpace Ltd (Grant No. SFC-NMIS-IDP/020), and State Key Laboratory of Robotics and System (Grant No. SKLRS/2022/KF/01).

Prototype pipeline modelling using interval scanning point clouds

Toa Pečur¹, Frédéric Bosché¹, Gabrielis Cerniauskas¹, Frank Mill¹, Andrew Sherlock², Nan Yu¹

1. School of Engineering, The University of Edinburgh, Edinburgh EH9 3FB, Scotland, UK;
2. National Manufacturing Institute Scotland, Paisley PA3 2EF, Scotland, UK

Received:2024-01-08 Revised:2024-03-21 Published:2025-05-16
Contact: Nan Yu,E-mail:nan.yu@ed.ac.uk E-mail:nan.yu@ed.ac.uk
Supported by:
This research was founded by Scottish Research Partnership in Engineering (SRPe), the National Manufacturing Institute Scotland (NMIS), ShapeSpace Ltd (Grant No. SFC-NMIS-IDP/020), and State Key Laboratory of Robotics and System (Grant No. SKLRS/2022/KF/01).

摘要/Abstract

摘要： With the aid of computer aided design (CAD) and building information modelling (BIM), as-built to as-designed comparison has seen many developments in improving the workflow of manufacturing and construction tasks. Recently, evolution has been centred around automation of scene interpretation from three-dimensional (3D) scan data. The scope of this paper is to assess assemblies as the installation process progresses and inferring if arising deviations are problematic (complex task). The adequacy of utilising unorganised point clouds to compliance check are trialled with a real life down-scaled prototype model in conjunction with a synthetic dataset. This work aims to highlight areas where large rework could be avoided, in part by the detection of potential clashes of components early in the pipeline installation process. With the help of an extracted model in the form of a point cloud generated from a scanned physical model and a 3D CAD model representing the nominal geometry, an operator can be made visually aware of potential deviations and component clashes during a pipeline assembly process.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00515-y

关键词: Point clouds, Modelling, Computer aided design (CAD), Digital manufacturing, Interval scanning

Abstract: With the aid of computer aided design (CAD) and building information modelling (BIM), as-built to as-designed comparison has seen many developments in improving the workflow of manufacturing and construction tasks. Recently, evolution has been centred around automation of scene interpretation from three-dimensional (3D) scan data. The scope of this paper is to assess assemblies as the installation process progresses and inferring if arising deviations are problematic (complex task). The adequacy of utilising unorganised point clouds to compliance check are trialled with a real life down-scaled prototype model in conjunction with a synthetic dataset. This work aims to highlight areas where large rework could be avoided, in part by the detection of potential clashes of components early in the pipeline installation process. With the help of an extracted model in the form of a point cloud generated from a scanned physical model and a 3D CAD model representing the nominal geometry, an operator can be made visually aware of potential deviations and component clashes during a pipeline assembly process.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00515-y

Key words: Point clouds, Modelling, Computer aided design (CAD), Digital manufacturing, Interval scanning

Toa Pečur, Frédéric Bosché, Gabrielis Cerniauskas, Frank Mill, Andrew Sherlock, Nan Yu. Prototype pipeline modelling using interval scanning point clouds[J]. Advances in Manufacturing, 2025, 13(2): 444-461.

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Prototype pipeline modelling using interval scanning point clouds

Prototype pipeline modelling using interval scanning point clouds

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