Digital transformation is at the forefront of manufacturing considerations, but often excludes discrete event simulation and cost modelling capabilities, meaning digital twin capabilities are in their infancy. As cost and time are critical metrics for manufacturing companies it is vital the associated tools become a connected digital capability. The aim is to digitize cost modelling functionality and its associated data requirements in order to couple cost analysis with digital factory simulation. The vast amount of data existing in today’s industry alongside the standardization of manufacturing processes has paved the way for a ‘data first’ cost and discrete event simulation environment that is required to facilitate the automated model building capabilities required to seamlessly integrate the digital twin within existing manufacturing environments. An ISA-95 based architecture is introduced where phases within a cost modelling and simulation workflow are treated as a series of interconnected modules: process mapping (including production layout definition); data collection and retrieval (resource costs, equipment costs, labour costs, learning rates, process/activity times etc.); network and critical path analysis; cost evaluation; cost optimisation (bottleneck identification, production configuration); simulation model build; cost reporting (dashboard visualisation, KPIs, trade-offs). Different phases are linked to one another to enable automated cost and capacity analysis. Leveraging data in this manner enables the updating of standard operating procedures and learning rates in order to better understand manufacturing cost implications, such as actual cost versus forecasted, and to incorporate cost implications into scheduling and planning decisions. Two different case studies are presented to highlight different applications of the proposed architecture. The first shows it can be used within a feasibility study to benchmark novel robotic joining techniques against traditional riveting of stiffened aero structures. In the second case study discrete event digital factory simulations are used to supply important production metrics (process times, wait times, resource utilisation) to the cost model to provide ‘real-time’ cost modelling. This enables both time and cost to be used for more informed decision making within an ever demanding manufacturing landscape. In addition, this approach will add value to simulation processes by enabling simulation engineers to focus on value adding activities instead of time consuming model builds, data gathering and model iterations.

Generating fit-for-purpose CAE models from complex CAD assemblies is time consuming and error-prone. Tedious tasks include identifying and isolating the components of interest, removing duplicate components, and correcting inconsistent component interfaces. In this paper a new approach to help engineers identify similar features and analyse the consistency of CAD assembly models is proposed. The method utilises a tensor factorisation technique developed for relational machine learning and applies it to B-Rep topological and geometrical relations. The model considers globally all the input relations to identify which entities in the assembly are similar (within a user-defined threshold) to a selected input entity. It is shown that a hierarchical clustering method can group entities, based on the similarities of their attributes and relationships with adjacent components. It is shown how some unsuspected CAD modelling errors show up as features which should be similar, but which are not. It is demonstrated how the technique can be used to support the, currently highly manual, task of decomposing a volume representing an internal fluid network into sub-volumes and features of significance.

Generating structured meshes is often an expensive process, limiting the use of high-fidelity numerical simulation methods for design, especially when the design is not yet fixed and design updates are likely. For hexahedral meshes generated by decomposing a B-Rep model into regions for which simple meshing strategies are known, robustly propagating design modifications to the decomposed representation and subsequent mesh is very challenging. In this paper, an approach is presented to propagate parametric update and feature changes to structured meshes. Geometric and topological modifications on the design model are identified first, enabling the equivalent modifications to be identified on the decomposition of the design model. Virtual topology operations used to generate the initial decomposition are combined with the meshing constraints of individual sub-regions to update the decomposition and associated mesh locally. The approach is demonstrated for a number of design updates.

This work introduces a cost modelling architecture in order to determine the cost-effectiveness of the latest joining technology developments. Riveting is the conventional joining method in the aerospace industry, but is a time-consuming, expensive process that adds excessive weight to a structure. As part of the JTI Clean Sky 2 Joint Technology Initiative, the OASIS project (“Optimization of Friction Stir Welding (FSW) and Laser Beam Welding (LBW) for assembly of structural aircraft parts”) aims to demonstrate the feasibility and cost-effectiveness of novel joining technologies. The technologies being investigated are LBW, FSW and Friction Stir Spot Welding (FSSW). Physical demonstrators, simulation studies and access to industry leading technical expertise from OASIS project partners have helped develop detailed production process maps and input accurate process metrics to determine manufacturing costs. To this end, an activity-based cost modelling architecture has been developed to predict the cost-effectiveness of the joining technologies and assess them against both manual and automatic riveted solutions. The model has been designed in a manner that enables integration into current manufacturing eco-systems, has scalability for large aerospace companies and the ability to perform multi-fidelity process cost models that can be integrated with one another as required.

This paper details a single framework that takes an Industry 4.0 approach to the automation of assembly based tasks in aerospace assembly. Research to date has shown that 80% of total assembly time is consumed by only 20% of the tasks within a jet engine nacelle assembly line. These tasks have the potential to be automated through industrial robots, as the proportion of time required for their completion supports a case for the required capital expenditure. The achievable accuracy of industrial robots has limited their broader application in the aerospace industry, where assembly tasks remain predominantly manual. A novel framework which establishes the virtual and physical connections to enable a Cyber-Physical System is presented, which has the potential to address this challenge. The framework is complemented by a developing implementation of the system. A closed-loop, digital construct is proposed whereby data from physical system execution informs changes to coded hardware inputs. This is then used to affect changes or correct actions to robotic operations, in order to meet assembly quality requirements. To date, the research has established independent, bi-directional data streams between the demonstration robot, metrology, and the PLM system. A method has also been developed which integrates positional information (actual & digital) within a single coordinate system to aid the smooth transition of data between the cyber and physical elements of the proposed framework.

Generating fit-for-purpose CAD models from complex assemblies is time consuming for analysts. Tedious tasks include to identify and isolate the components of interest for the analysis, remove duplicate components, or correct inconsistent components’ interfaces are common for large assemblies during the product development process. In this paper a new approach to help engineers analyse the consistency of CAD assembly models is proposed. The method utilises a tensor factorisation technique developed for relational machine learning and applies it on B-Rep topological and geometrical relations. The generated decomposition is used to identify which entities in the assembly are similar (within a threshold) to a selected input entity. The factorisation model regards globally all input relationships, e.g. the connections between components, to identify similar entities based on their relationships in the relational domain. It is shown that a hierarchical clustering method can group entities based on the similarities of their attributes and relationships.

Generating hexahedral meshes is often an expensive process, which limits the use of high-fidelity numerical simulation methods for design. Hexahedral meshes can be generated by decomposing a geometric model into simpler meshable regions, but robustly propagating design modifications to the decomposed representation makes any attempt to update the mesh very challenging. In this paper, a virtual topology workflow enabling automatic generation of hex-dominant meshes is extended to propagate parametric modifications and feature changes to the decomposition and resulting mesh. Geometric and topological modifications are identified and linked to the decomposition through virtual topology relationships. Modified regions are localized and reasoning on the virtual decomposition enables their definition and associated meshing strategy to be updated. Instead of starting the meshing process from the beginning, only modified cells are re-meshed. This provides an efficient and automated method to propagate design changes down to the analysis model.

An equivalent non-manifold cellular model is used to enrich manifold decompositions of a CAD model to create a model suitable for finite element analysis.Thin-sheet and long-slender decomposition tools are integrated around the common data structure in order to automatically define a meshing recipe based on analysis attributes identified during the decomposition.Virtual topology operations are used to replicate the hard geometry splits in the non-manifold representation and create a robust bidirectional mapping between manifold and non-manifold representations.Adjacency information extracted from the non-manifold cellular model, alongside the appropriate analysis attributes and linear integer programming methods, are used to define a hex-dominant meshing recipe, which can then be applied to automatically generate a mesh.

Computational simulation is critical in the modern engineering design process. Currently, the use of simulation is limited by the time-consuming process of converting CAD assemblies into FEA models which are efficient to run and yet sufficiently accurate. In addition to the geometric representation of components, analysts require additional knowledge to describe the complete 3D simulation model. To speed up the generation of CAE models from CAD assemblies, we propose to capture high-level modelling and idealisation decisions, characterising the simulation intent, into a knowledge-based CAE model. In this framework, a simulation intent ontology formalises and structures the analysis parameters, the modelling and idealisation decisions. The ontological concepts and relations required to incorporate two of the key capabilities, cellular modelling and equivalencing, are described. Cellular modelling introduces the concept of cells, which subdivide the 3D space, and to which simulation attributes can be attached. Equivalencing maintains the link between different representations of the cells required for different analyses throughout the analysis lifecycle. Illustrative examples show how the knowledge-based CAE model is used to manage the idealisation decisions and to apply inference rules replicating current modelling practices.

Several templates for 2D and 3D structured mesh refinement are presented. The templates have the property that the minimum number of irregular points or edges (mesh singularities) are added. For a given set of external division numbers a variety of interior meshes can be generated. The positions of the internal vertices in the template are calculated explicitly using an extended transfinite mapping scheme, which has previously been shown to be equivalent to iterative iso-parametric smoothing. Since calculating the block vertex positions requires the solution of a small number of linear equations, the optimum mesh in the interior of the template can be evaluated very cheaply before the block structured mesh is generated.

This paper describes an automatic method for identifying thin-sheet regions (regions with large lateral dimensions relative to the thickness) for complex thin-walled components, with a view to using this information to guide the hexahedral (hex) meshing process. This fully automated method has been implemented in a commercial CAD system (Siemens NX) and is based on the interrogation and manipulation of face pairs, which are sets of opposing faces bounding potential thin-sheet regions. Careful consideration is given to the mapping, merging and intersection of face pairs to generate topologies suitable for sweep meshing the thin-sheet regions, and for treating the junctions between adjacent thin-sheet regions. It is proposed that hex meshes be applied to thin-sheet regions by quad meshing one of the faces bounding the thin-sheet region and sweeping it through the thickness to create hex elements. Decisions on the generation and positioning of the cutting surfaces required to isolate thin-sheet regions are made by considering the likely impact on the quality of the resulting mesh. The method delivers a substantial step towards automatic hex meshing for complex thin-walled geometries. A significant reduction of the degrees of freedom (DOF) can be achieved by applying anisotropic hex elements to the identified thin-sheet regions.

Hexahedral (hex) meshes can be generated for CAD models of complex thin-walled components by isolating thin-sheet and long-slender regions, quad meshing one of the bounding faces, and sweeping the quad mesh through the volume to create hex elements. Continuing the work in Sun et al. (Proc Eng 163:225–237, 2016 ), where an improved approach to thin-sheet identification was presented, an enhanced automatic method for long-slender region identification is proposed in this paper. The objective is to improve the efficiency and decomposition quality compared to existing long-slender region identification processes. Geometric measures such as the edge length and the face width are employed to generate sizing measures, which are in turn used to identify candidate long-slender regions. Careful consideration is given to the generation and positioning of the cutting faces required to isolate the long-slender regions by assessing a priori quad mesh quality on the wall faces. It is shown that a significant reduction in the number of degrees of freedom (DOF) can be achieved by applying anisotropic hex elements on the identified regions.

Quasi-axisymmetric structural components are very common in any mechanical equipment containing rotating components, such as turbo machinery. Identifying and exploiting symmetric properties in these components is key to simplify the creation of FE volume meshes. In this paper, a novel approach is proposed to detect exact cyclic symmetries, in order to decompose a quasi-axisymmetric CAD component for FEA. Starting from a B-Rep CAD model, axisymmetric and cyclic repeated sectors are automatically identified to generate a subdivided representation, which can then be used to produce a good quality mesh. Using this new component structure, the resulting decomposition produces a reduced number of mesh-able sub-domains. Symmetry properties can then be inferred to generate the full component mesh from meshes on the individual sub-domains. The approach shows a major reduction of the geometry to be meshed leading to less manual intervention from the user.

In 2-dimensional geometric constraint solving, graph-based techniques are a dominant approach, particularly in CAD context.These methods transform the geometric problem into a graph which is decomposed into small sub-graphs.Each one is solved, separately, and the final solution is obtained by recomposing the solved sub-graphs.To the best of our knowledge, there is no random geometric constraint graph generator so far.In this paper, we introduce a simple, but efficient generator that produces any possible geometric configuration.It would be parameterized to generate graphs with some desirable proprieties, like highly or weakly decomposable graphs, or restricting the generated graph to a specific class of geometric configuration.Generated graphs can be used as a benchmark to make consistent tests, or to observe algorithm behaviour on the geometric constraint graphs with different sizes and structural properties.We prove that our generator is complete and suitable for two main classes of solving approaches.