Toolpath planning is an essential component of robotic additive manufacturing. An efficient toolpath strategy allows parts to be made that are geometrically accurate, free of defects, have good mechanical properties and have low residual stress. Toolpaths for cold spray additive manufacturing have some technical constraints that need to be considered compared to their counterpart designed for conventional 3D printing machines. This study presents an automated toolpath planning method based on offset contours. The generated toolpath is globally continuous, layer-wise setting, making it suitable for robotic cold spray additive manufacturing. The toolpath algorithm was tested on a variety of geometries to demonstrate its robustness. One model was selected for printing using a commercial high-pressure cold spray system. The experimental results show that our method is applicable to cold spray robotic additive manufacturing for near-net shape construction. The method is particularly good for web-rib structures.

Additive manufacturing (AM) is rapidly evolving from “rapid prototyping” to “industrial production”. AM enables the fabrication of bespoke components with complicated geometries in the high-performance areas of aerospace, defence and biomedicine. Providing AM parts with a tagging feature that allows them to be identified like a fingerprint can be crucial for logistics, certification and anti-counterfeiting purposes. Whereas the implementation of an overarching strategy for the complete traceability of AM components downstream from designer to end user is, by nature, a cross-disciplinary task that involves legal, digital and technological issues, materials engineers are on the front line of research to understand what kind of tag is preferred for each kind of object and how existing materials and 3D printing hardware should be synergistically modified to create such tag. This review provides a critical analysis of the main requirements and properties of tagging features for authentication and identification of AM parts, of the strategies that have been put in place so far, and of the future challenges that are emerging to make these systems efficient and suitable for digitalisation. It is envisaged that this literature survey will help scientists and developers answer the challenging question: “How can we embed a tagging feature in an AM part?”.

Ensuring the integrity of product manufacture information and securely verifying the authenticity of a component is critical for both manufacturers and customers. However, traditional product identifying solutions offer little protection over counterfeit and cyber-attack. We propose a Blockchain-based product identification and certification system called Universal Identifier of Things (UIT) that enables fast product authenticity verification using low-cost devices. We leverage additive manufacturing technologies to embed a unique identifier into a product. The identifier is then digitalized by generating a digital certificate which is stored on Blockchain during the whole product life cycle for various applications/services (provenance, traceability, product warranty and call-back, etc.). We prove this concept by integrating 3D printing and Hyperledger Blockchain technologies to demonstrate that we can ensure the integrity of products with UIT by bridging the cyber and physical worlds.

Cold spray additive manufacturing (CSAM) is a solid-state deposition process well-suited to titanium that has the potential to make large, near-net-shape parts at high productivity. However, further research is required to truly accomplish the freedom of design expected from CSAM and, in particular, to address how to manufacture a specific 3D object with minimal porosity. Therefore, this paper focuses on understanding how tool path planning strategy and robot kinematics affect the geometry and porosity distribution in a 3D object. Square titanium frames were manufactured layer-by-layer using a continuous tool path planning strategy in which the contour spray angle, traverse speed and corner smoothing radius were varied selectively. The sample geometry was analysed by 3D laser scanning, and the capacity to produce straight, vertical walls and square corners were demonstrated. The total porosity in the manufactured objects was measured using the Archimedes’ principle, further investigated by metallographic cross-section analysis, and then validated by X-ray computed tomography on selected samples. Porosity was distributed layer by layer, creating a fishbone structure in the cross-section with higher porosity between the layers and near the edges of the walls and corners. The influence of robot kinematics and toolpath planning on forming underbuilt and overbuilt structures and how they influenced porosity development are also discussed. The knowledge generated from this research can significantly influence the development of tool path planning strategies in CSAM, providing the means to produce improved near-net-shapes with controlled porosity formation.

Two X-ray computed tomography (CT) datasets have been acquired for a cold-sprayed titanium sample before and after heat treatment. The datasets were collected with a beam energy of 30 keV at the Australian Synchrotron. Three-dimensional (3D) distributions of porosity in the Ti sample were reconstructed using a data constrained modelling (DCM) technique. Quantitative analysis indicated that the heat treatment caused morphological changes to the pores and a small decrease in the overall porosity. After heat treatment, some fine porosity disappeared while the large porosity regions were essentially unaffected except for a change towards a more rounded pore shape. Interconnectivity between pores was reduced, which has implications for sealing and trapping of contaminant gases in cold-sprayed parts. The characterization technique and the workflow presented in the paper are applicable to non-destructive 3D characterization of other materials.

This article is intended as a tutorial guide for new users of the DCM (data-constrained modelling) software for quantitative characterization of material 3D microstructures using multi-energy X-ray CT data. It guides users through the steps necessary for processing a small CIPS (Calcite In-situ Precipitation System) sandstone data set. It also covers some built-in and plug-in features to analyze and visualize the microstructures.

The increasing demand for environmentally-friendly and non-toxic coating systems from the aerospace and heavy industry sectors is driving innovation in corrosion inhibitor design and functional coating development. A fundamental understanding of how molecular structure and functionality influences the electrochemical responses of inhibited coatings is crucial for the design of effective functional coatings to replace stalwart, yet highly toxic industrial solutions. In this paper, an artificial neural network approach is presented to quantitatively study the relationship between the structural/molecular features of inhibitor compounds and their experimentally measured electrochemical properties. The presented method is applied to correlate molecular features of corrosion inhibitors with experimentally obtained corrosion potential (Ecorr), corrosion current (Icorr) and anodic/cathodic Tafel slopes. The neural network model, trained through an automatic optimization process, was able to predict the electrochemical performance for a given inhibitor molecule candidate. We will demonstrate how it can be utilised to assess the impact of molecular structure on the final effectiveness of the candidate corrosion inhibitor molecule. The presented neural network learning method could be applied to other areas in materials science for accelerating general materials discovery and functional coating design.

The paper reports on a collaboration between BOEING and CSIRO that aims to dramatically cut the time required to develop aircraft materials . At present the time required to develop material can be excess of 15 years, much longer than that required to develop a new craft .. One way to cut the time for materials development is design materials virtually. This virtual design does not eliminate the need for actual materials design and certification but it allows this process to focus only on material designs with a high probability of fulfilling their design function. Virtual design requires a platform that links models from the molecular scale to the engineered object scale. The project has two typical components 1. Developing a platform that permits models to be linked 2. Selection and/or development of models across the relevant platform scales The models developed include a) Molecular scale models to define corrosion inhibitor and surface interactions b) Molecular and continuum models to define inhibitor movement through a polymer and to a defect c) Models of pit initiation and propagation on structural aluminium d) Scenario builder to define the location and activity of an aircraft e) Microclimate models defining the conditions within and exterior to an aircraft in service and on the ground f) Damage accumulator that keeps track of the accumulation of damage The models have been linked through the platform and the life of a range of primers with different inhibitors calculated for different flight patterns Current and future activities consist of validating the models at each scale and validating the complete model by assessing how will it predicts the life in neutral salt spray tests. Future work will look at validating the model against life of components on actual aircraft and extending the model to composites.

Innovative new methods are required to cut the development time of new materials (currently in excess of 15 years) so that it is commensurate with the development time of new aircraft (less than 8 years). Virtual design of materials would allow selection of material designs that have a high probability of fulfilling their design function and thus focus material development. It requires the integration of models from the molecular to engineering scale. This paper reports on this platform and the actual models across the scales. The models include: molecular scale models defining corrosion inhibitor/surface interactions and inhibitor movement, models of pit initiation and propagation, microclimate models defining conditions in and on the exterior of an aircraft and damage accumulation models that keep track of the advance of damage. The system can estimate the life of a range of primers with different inhibitors for different flight patterns.

As structures built now will be expected to last well past 2064 (50 years) it is vital that the effect of climate change be considered in their design and material selection. In particular changes in the rate of corrosion of metal components must be considered. To this end this study estimates the maximum likely change in the corrosion rate for the year 2070 so it can be included in current design. Changes in corrosion are estimated for 11 coastal and inland locations in Australia. For each station the climatic data (3-hourly) in 2070 is estimated by modifying current data with probable changes based on two climate change models (CSIRO: CSIRO-Mk 3.5 and MRI: MRI-CGCM 3.2.2). The former is for high global warming rate and the later the A1FI scenario. This climatic data is then run the Corrosion “predictor” (a multi-scale process model) to predict corrosion at each location. It is found that significant changes occur with corrosion in coastal locations increasing substantially, in contrast the corrosion at inland locations will decrease moderately. The increase in coastal locations is associated with a greater build up of salt due to less frequent rain evens while the reduction in inland locations is associated with a reduction in RH and thus surface wetness.

This paper will present a multi-scale of corrosion that spans scales from molecular scale (where it considers the binding of inhibitor to metal surfaces) to the continental scale where it considers ocean production of marine aerosols and the aerosols subsequent transport across continents. The multi-scale model has been developed in order to both guide the development of new corrosion resistant materials and to aid in their design and selection. Multi-scale modelling will facilitate computational assisted design by allowing the effect of a large number of design variables to be assessed computationally before the final reduced selection can be investigated experimentally.The combination of macro - micron scale allows a definition of the state of the surface (on a 3 hourly basis) with four states being defined: wet from rain, wet from the wetting of hygroscopic salts, drying, and dry. Then for each these states the rate of corrosion and changes in the nature of the metal/oxide/moisture layer system are predicted. On active metals such as zinc and steel, multi-layers of oxide may develop. Adjacent to the metal surface a compact oxide is observed under certain circumstances. This compact oxide layer dramatically reduces the rate of metallic corrosion. Above, or in the absence of the compact layer, bands of porous oxide may develop with variable porosity from the metal or compact layer interface to the moisture layer. To model this porous oxide a model based on porous-electrode theory has been developed. The porous oxide model (POM) is a continium model that accounts for diffusion, chemical and electrochemical process. In a system where the oxide is semi-conducting (such as zinc or iron) the oxide itself may support the oxygen reduction reaction (ORR). The POM considers the relative rate of the ORR at the metal surface and at the porous oxide-solution boundary. It is found that the ORR occurs both at the metal surface and at the pore boundary but with time as oxygen is depleted at the bottom of the pores the ORR occurs predominantly on the oxide surface. The conditions generated within the porous layer will affect the conditions at the porous layer/compact layer interface and thus affect the compact layer stability. The POM model is then combined with a fine scale model of the condition of and processes within compact barrier oxides. The integration of the state model the POM and the model of the compact oxide will provide both a design and materials selection tool for uncoated metalsModelling of inhibitor/metal surface interaction is being undertaken to assess the effect of molecular structure and functional groups that lead to effective inhibition. This model combines a range of sub-modules, including modules which cover extensive length scales.

Relative Debugging allows a user to compare the internal state of two programs as they run, making it possible to test whether two programs perform the same function given the same input. When implemented with a command line user interface, a relative debugger looks like traditional debugging tools with the addition of commands that describe which structures should be equivalent in the two programs. In this paper, we discuss relative debugging within an integrated development environment, and show that there are significant advantages over a command line form. We describe a pluggable, modular, architecture that works with a variety of different products, including Microsoft's Visual Studio, SUN's NetBeans, and IBM's Eclipse. Copyright © 2009 John Wiley & Sons, Ltd.

This paper describes an implementation of relative debugging under the Eclipse framework. The paper describes what relative debugging is and how it helps test and debug evolved applications. We give a brief overview of the architecture of Eclipse and show how our relative debugger, Guard, has been integrated into that environment.