Robert R. Lipman

Bio: Robert R. Lipman is an academic researcher from National Institute of Standards and Technology. The author has contributed to research in topics: NIST & Interoperability. The author has an hindex of 18, co-authored 53 publications receiving 2205 citations.

Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST

Abstract: Building information models (BIMs) are maturing as a new paradigm for storing and exchanging knowledge about a facility. BIMs constructed from a CAD model do not generally capture details of a facility as it was actually built. Laser scanners can be used to capture dense 3D measurements of a facility's as-built condition and the resulting point cloud can be manually processed to create an as-built BIM — a time-consuming, subjective, and error-prone process that could benefit significantly from automation. This article surveys techniques developed in civil engineering and computer science that can be utilized to automate the process of creating as-built BIMs. We sub-divide the overall process into three core operations: geometric modeling, object recognition, and object relationship modeling. We survey the state-of-the-art methods for each operation and discuss their potential application to automated as-built BIM creation. We also outline the main methods used by these algorithms for representing knowledge about shape, identity, and relationships. In addition, we formalize the possible variations of the overall as-built BIM creation problem and outline performance evaluation measures for comparing as-built BIM creation algorithms and tracking progress of the field. Finally, we identify and discuss technology gaps that need to be addressed in future research.

Streamlining the additive manufacturing digital spectrum: A systems approach

Abstract: Additive manufacturing (AM) promises great potential benefits for industrial manufacturers who require low volume and functional, highly complex, end-use products. Commercial adoption of AM has been slow due to factors such as quality control, production rates, and repeatability. However, given AM's potential, numerous research efforts are underway to improve the quality of the product realization process. A major area of opportunity is to complement existing efforts with advancements in end-to-end digital implementations of AM processes. New paradigms are needed to support more efficient and consistent design-to-product transformations. Systematically configured digital implementations would facilitate informational transformations through standard interfaces, streamlining the AM digital spectrum. Here, we propose the development of a federated, information systems architecture for additive manufacturing. We establish an information requirements workflow for streamlining information throughput during product realization. The architecture is delivered through the development of a solution stack, including the identification of areas where advancements in information representations will have the highest impact. The architecture will specify the stages of the product realization process, and the interfaces needed to link those stages together. Common data structures and interfaces will allow developers and end users of additive manufacturing technologies to simplify, coordinate, validate, and verify end-to-end digital implementations.

Design for additive manufacturing: trends, opportunities, considerations, and constraints

Abstract: The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM's full potential in industry.