Patent DK-202001160-A1 (DK)
Title: Graph-based control model for digital manufacturing
Inventor: NARENDRAKRISHNAN NEYTHALATH
IPC: G06F30/00
Filed: 2020-10-11
Abstract: The present disclosure relates to a computer-implemented method for customizing an automated product manufacturing process. It further relates to a robotic manufacturing system for manufacturing a product based on a fabrication model generated by means of the aforementioned computer-implemented method. Related Patent Application This application claims the priority of PCT Pending Application No. PCT/EP2020/060492, filed April. 14, 2020 and entitled “Smart Manufacturing Framework”. Background of the invention Despite significant research efforts spanning more than four decades, the use of robotic manufacturing in global construction has to date seen no large-scale industrial adoption. Due this absence of automation, global construction productivity has remained stagnant over four decades, while other sectors – such as the manufacturing industries, which have seen pervasive penetration of automation technologies – have experienced a quadrupling of productivity in the same timeframe. One central challenge preventing wide-scale adoption is the circumstance that automating construction work requires the close integration of disciplines that have historically worked un-related. The fields required to collaborate are 1) User Experience Design, 2) Computational Design, 3) Manufacturing Engineering; 4) Construction manufacturing; 5) Computer Science; 6) Robotic Engineering. Bridging the knowledge of these domains to establish automatic, file-to-factory workflows requires the development of highly specialized technical knowledge, that remains widely unavailable throughout the traditionally crafts-based, global construction industry. Prior Art Recent developments in construction automation have seen an increasing body of research explore the paradigm of ‘parametric manufacturing’ in which parametric CADmodels of a target product is manufactured through an associated robotic manufacturing unit. This approach enables a partial solution to a second central challenge within construction automation, which is that while production in the manufacturing industry can operate under a mass-manufacturing paradigm of repeated production of identical products, construction manufacturing is project specific, requiring one-off or small-series production runs. The solution offered from parametric manufacturing is to drive a digital production line from a customizable CAD-geometry, which enables flexibility of creating products that are unique within the bounds of predetermined variables. One aspect of this development is the increasing use of Visual Programming environments, such as McNeel Grasshopper or AutoDesk Dynamo to create graph-based parametric models, which relates to the general use of such models for project designs within the practise of the Architectural, Engineering and Construction industry. However, as previously indicated, this approach requires highly specialized knowledge to establish and operate, while simultaneously drawing on a cross-disciplinary domain knowledge, which has so far emerged only within a research context. It further entails the development of a unique specialization of this knowledge to each sub-segment of construction manufacturing. Since the construction industry is segmented into a vast number of niche areas of manufacturing, due to the high degree of variability in design, construction materials used and assembly sequences deployed, this implies a prohibitive overhead in expertly development of such digital manufacturing workflows. Summary of the invention The present disclosure uniquely addresses the above challenge by offering a novel model of knowledge encapsulation, which enables a standardization of digital construction workflows across highly variable and seemingly unrelated applications. Hereby, it significantly reduces the software development effort required to establish a particular digital production workflow; as well as significantly reducing the degree of specialist knowledge required to do so. Specifically, one aspect of the invention is a method of defining and subsequently controlling a digital production workflow by means of an n-layered Directed Acyclic Graph (DAG). It is the finding of the inventors that any digital construction workflow may be abstracted to the following five constituents: a) a parametric product model with associated control variables; b) a parametric fabrication model, which is a derivative of a) and computes toolpaths as a function thereof; c) an execution entity, which comprise a numeric representation of a physical production system and utilize this representation to calculate motion instructions and validations which can be transmitted to d) a cyberphysical production unit, which entail a digitally actuated entity with an associated processing tool, the totality hereof controlled through a computer-implemented control module; and e) a Human Machine Interface, which enables a production user to interact and control the entirety of the system through the means of a Graphical User Interface (GUI). In one further aspect of the invention, the first layer of the n-layered DAG represents these five constituent through five nodes with associated edges. Hereby, a high-level, operational entity is abstracted, which remain invariant throughout a plurality of embodiments of digital construction workflows. The nodes of the first layer are constituted by sub-graphs on a second layer, which holds a non-bound variability of node content, node configuration and edge topology. As a result of this non-bound variance, sub-graphs cannot be directly projected into the nodes of the first layer. Instead, in one further aspect of the disclosure, a novel method of projection is deployed, the method comprising the steps of: a) Appending a constraint to a process routine of the projection b) Extracting an ordered point set from the projected content using functional composition c) Specifying the constraint as a numerical operation on the on the obtained,ordered point set d) Compare the generated values with a pre-decided threshold to evaluate a Boolean output e) Determine the state of the constraint evaluation based on the output Hereby, the validation of sub-graphs to be projected is abstracted, enabling the following functional advantages, which jointly reduce the complexity and workload of establishing custom manufacturing workflows: - The first layer graph can be repeatedly utilized a general control model for any custom workflows, hereby standardizing the workflow establishment process. - The abstracted representation hereby established enables a simplification of the workflow, enabling less specialized users to customize and change workflows - Through the specialization of sub-graphs, a separation of concern is achieved, in which only a single domain of knowledge is required to create each subgraph, as opposed to current methods, in which the generation of one, comprehensive graph requires extensive, multidisciplinary domain expertise. - Through the abstraction of the sub-graph validation, error-checking is simplified. Hereby, implementation and stabilization of new workflows is eased. - Finally, through the standardization implied by the disclosed method, automatic generation of Graphical User Interfaces for custom workflows are enabled, through exposing product model variables within a GUI template. Brief description of drawings. Fig. 1 depicts an embodiment of the presently disclosed method, wherein the method is represented as a sequence of 7 consecutive steps. Fig 2. displays an embodiment of the disclosed methods, in which a sequence of projection between 2 layers of a graph is detailed as 5 consecutive steps. Fig. 3 shows an embodiment the presently disclosed method, wherein the method is represented as a directed acyclic graph (DAG) comprising five main nodes. Fig 4. Display an embodiment of the disclosed method, wherein the method is represented as an n-layered graph, exemplified with 2 layers. Fig. 5. Display a structure of a function composition node according to an embodiment of the present disclosure. Fig. 6. Shows a workflow of matrix transformations according to an embodiment of the disclosure. Fig. 7. Display one embodiment of a constraint checking node according to an embodiment of the disclosure. Fig. 8. Shows an example Projected Product Model sub-graph for a hemisphere product according to an embodiment of the disclosure. Fig. 9 Displays an example Projected Fabrication Model sub-graph related to fig. 8 according to an embodiment of the disclosure. Fig. 10. Display the geometric output of the Projected Fabrication Model sub-graph of fig. 8 according to an embodiment of the disclosure. Fig. 11. Displays an embodiment of a Robotic Production Unit, created to support the manufacturing of the product types produced by fig. 8. Fig. 12. Shows an example Projected Product Model sub-graph for a helical product according to an embodiment of the disclosure. Fig. 13 Displays an example Projected Fabrication Model sub-graph related to fig. 12 according to an embodiment of the disclosure. Fig. 14. Display the geometric output of the Projected Fabrication Model sub-graph of fig. 12 according to an embodiment of the disclosure. Fig. 15. Displays an embodiment of a Robotic Production Unit, created to support the manufacturing of the product types produced by fig.12.