Uncertainty in the physicochemical and optical properties of volcanic ash particles creates errors in the detection and modeling of volcanic ash clouds and in quantification of their potential impacts. In this study, we provide a data set that describes the physicochemical and optical properties of a representative selection of volcanic ash samples from nine different volcanic eruptions covering a wide range of silica contents (50–80 wt % SiO2). We measured and calculated parameters describing the physical (size distribution, complex shape, and dense‐rock equivalent mass density), chemical (bulk and surface composition), and optical (complex refractive index from ultraviolet to near‐infrared wavelengths) properties of the volcanic ash and classified the samples according to their SiO2 and total alkali contents into the common igneous rock types basalt to rhyolite. We found that the mass density ranges between ρ = 2.49 and 2.98 g/cm3 for rhyolitic to basaltic ash types and that the particle shape varies with changing particle size (d < 100 μm). The complex refractive indices in the wavelength range between λ = 300 nm and 1500 nm depend systematically on the composition of the samples. The real part values vary from n = 1.38 to 1.66 depending on ash type and wavelength and the imaginary part values from k = 0.00027 to 0.00268. We place our results into the context of existing data and thus provide a comprehensive data set that can be used for future and historic eruptions, when only basic information about the magma type producing the ash is known.

Purpose This paper aims to introduce the ideas of practical implications of using industrial robots to implement additive/hybrid manufacturing. The process is discussed and briefly demonstrated. This paper also introduces recent developments on human–machine interface for robotic manufacturing cells, namely the ones used for additive/hybrid manufacturing, as well as interoperability methods between the computer-aided design (CAD) data and material modeling systems. It is presented – using a few solutions developed by the authors – as a set of conceptual guidelines discussed throughout the paper and as a way to demonstrate how they can be applied and their practical implications. Design/methodology/approach The possibility to program the system from CAD information, which is argued to be crucial, is explored, and the methods necessary for connecting the CAD data to material modeling systems are introduced. This paper also discusses in detail the main requirements (also from a system point-of-view) needed for a full implementation of the presented ideas and methods. A few simulations to better characterize the interactions from heat conduction and physical metallurgy were conducted in an effort to better tune the additive manufacturing process. The results demonstrate how the toolpath planning and deposition strategies can be extracted and studied from a CAD model. Findings The paper fully demonstrates the possibility to use a robotic setup for additive manufacturing applications and shows the first steps of an innovative system designed with that objective. Originality/value Using the aimed platform, unsupervised net-shaping of complex components will substitute the cumbersome processes, and it is expected that such a visionary concept brings about a significant reduction in cost, energy consumption, lead time and production waste through the introduction of optimized and interactive processes. This can be considered as a breakthrough in the field of manufacturing and metal processing as the performance is indicated to increase significantly compared to the current instruction-dependent methods.

Improving the success rate in additive manufacturing and designing highly optimized structures require proper understanding of material behaviour. This study proposes a novel experimental method by which anisotropic mechanical properties of additively manufactured materials can be assessed. The procedure is based on tensile testing of flat specimens, manufactured by laser powder bed fusion (LPBF) at different orientations relative to the build plate. In this study, the procedure was applied to the Inconel 718 alloy. Three identical specimen sets were built, each of which received complementary postprocessing treatments. The tensile tests were carried out on specimens with as-built surface finish. Digital image correlation was used to record the strain field evolution on two perpendicular surfaces of the tensile specimens under loading. An optimization algorithm is also proposed for determining the anisotropic elastic constants using only a few tensile test results. It was observed that both build orientation and postprocessing have strong influence on the anisotropic mechanical properties of the material. The effect of microstructure was also investigated and characterised. Consequently, three transversely isotropic compliance matrices were constructed, representing the effect of the different processing conditions.

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The fatigue life of metal components is known to depend on the surface topography. For components made by laser powder bed fusion, the roughness of the as‐built surfaces depends on the orientation of the component surface with respect to the build plate. Surface topographies of AlSi10Mg and Inconel 718 specimens built at 0° to 90° inclination, with 15° increments, were characterised by white light interferometry. Two methods for calculating the stress concentration factor using the surface roughness data are proposed, and the results of each approach are presented and compared. Moreover, a finite element model was developed, in order to analyse the stress field when subsurface porosity is present. The fatigue lifetime estimates suggest that the lifetime of components may differ up to two orders of magnitude, depending on the build orientation.

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Purpose Additive manufacturing (AM) technologies have recently turned into a mainstream production method in many industries. The adoption of new manufacturing scenarios led to the necessity of cross-disciplinary developments by combining several fields such as materials, robotics and computer programming. This paper aims to describe an innovative solution for implementing robotic simulation for AM experiments using a robot cell, which is controlled through a system control application (SCA). Design/methodology/approach For this purpose, the emulation of the AM tasks was executed by creating a robot working station in RoboDK software, which is responsible for the automatic administration of additive tasks. This is done by interpreting gcode from the Slic3r software environment. Posteriorly, all the SCA and relevant graphical user interface (GUI) were developed in Python to control the AM tasks from the RoboDK software environment. As an extra feature, Slic3r was embedded in the SCA to enable the generation of gcode automatically, without using the original user interface of the software. To sum up, this paper adds a new insight in the field of AM as it demonstrates the possibility of simulating and controlling AM tasks into a robot station. Findings The purpose of this paper is to contribute to the AM field by introducing and implementing an SCA capable of executing/simulating robotic AM tasks. It also shows how an advanced user can integrate advanced simulation technologies with a real AM system, creating in this way a powerful system for R&D and operational manufacturing tasks. As demonstrated, the creation of the AM environment was only possible by using the RoboDk software that allows the creation of a robot working station and its main operations. Originality/value Although the AM simulation was satisfactory, it was necessary to develop an SCA capable of controlling the whole simulation through simple commands instructed by users. As described in this work, the development of SCA was entirely implemented in Python by using official libraries. The solution was presented in the form of an application capable of controlling the AM operation through a server/client socket connection. In summary, a system architecture that is capable of controlling an AM simulation was presented. Moreover, implementation of commands in a simple GUI was shown as a step forward in implementation of modern AM process controls.

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