A data-driven approach for predicting printability in metal additive manufacturing processes

Mycroft, William; Katzman, Mordechai; Tammas‐Williams, Samuel; Hernández-Nava, Everth; Panoutsos, George; Todd, Iain; Kadirkamanathan, Visakan

doi:10.1007/s10845-020-01541-w

Cited by 49 publications

(20 citation statements)

References 40 publications

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“…The use of neural networks has also been extended to other areas of laser manufacturing, such as laser welding (Asif et al 2020;Günther et al 2014Günther et al , 2016, additive manufacturing (Li et al 2020;Mahato et al 2020;Mycroft et al 2020), and a method to reconstruct laser pulses (Zahavy et al 2018). Here, we extend on both these, and previous (Arnaldo et al 2018), works in the area of laser surface texturing.…”

Section: Introductionmentioning

confidence: 88%

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

et al. 2021

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Interactions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.

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Section: Introductionmentioning

confidence: 88%

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

et al. 2021

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“…As reviewed in [30], Yao et al [31] made design feature recommendations to help inexperienced designers. In a recent paper, Mycroft et al [32] address the systematic design approach need by developing a machine learning framework to predict small-scale features printability. More precisely, relying on a customised machine learning database, geometric mesh descriptors are defined such as curvature, thickness or overhang and used to analyse printability of a triangulated CAD artefact prior to manufacturing.…”

Section: E Toward a Systematic Design Methodologymentioning

confidence: 99%

Test artefacts for additive manufacturing: A design methodology review

Pastre

Tagne

Anwer

2020

CIRP Journal of Manufacturing Science and Technology

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For the past few decades, additive manufacturing (AM) has paved the way to several processes through a wide range of commercially available machines. Benchmark artefacts were developed to set a common reference in order to assess and compare AM machine limitations. In this paper, a review of different AM benchmark artefact design methodologies is presented. More precisely, the evolution of design methods is described. Originally, additive manufacturing machines were assessed by establishing their ability to produce defined features. Indeed, AM benchmark artefact design inherited traditional subtractive manufacturing methods by defining simple geometries. However, due to the AM available freedom, no standard artefact can be sufficiently representative of the diversity of studied criteria. Furthermore, metrology aspects were not considered. Facing the variety of benchmark artefacts available, proposed guidelines then focused on defining systematic design methods rather than standard artefacts. Several methods have been proposed to help designing benchmark artefact suited for considered criteria. Nevertheless, some traditional simple geometries are found incompatible with measuring instruments that can hardly characterise AM free-form surfaces for example. That is why, more recently, significant efforts have been made to consider measurement issues and uncertainties in the artefact design stage. As this paper concludes, benchmark artefacts now tend to be designed in a more metrological way integrating the whole post-manufacturing measurement process relying on statistical modelling and instrument comparisons. Regarding the raised stakes, a final set of recommendations is provided to conciliate both manufacturers' and metrologists' point of view in benchmark artefact design.

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“…Allaire et al used triangular 3D meshes for to find the optimal build orientation of the object to be manufactured as well as for the shape and topology optimization of supports [38]. A datadriven predictive model able to predict the printability of a given artefact is proposed by Mycroft1 et al [39]. Tominski et al [22] proposed a software-based design check for AM concept.…”

Section: ) Three Dimensional Object Model Analysismentioning

confidence: 99%

Knowledge-Driven Manufacturability Analysis for Additive Manufacturing

Mayerhofer¹,

Lepuschitz²,

Hoebert³

et al. 2021

IEEE Open J. Ind. Electron. Soc.

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Additive Manufacturing (AM) evolved recently from a rapid prototyping process to a standard manufacturing tool. Nevertheless, it is still not a widely used method due to different process-related challenges. In recent years printer technologies and possible printable materials emerged but there are still challenging demands on the printing process. Hence, it is of vital importance to inspect the manufacturability of the designed parts. This work focuses on the not yet widely researched ceramic printing with the Lithography-based Ceramic Manufacturing (LCM) processes. It presents a knowledge-driven framework able to automatically examine geometric properties of a part and compare it to AM guidelines. As a knowledge base, an ontology is used which contains information about the capabilities of AM processes, printers and materials. The manufacturability system uses triangle-based mesh processing algorithms to recognize features and check the guidelines necessary for LCM. The evaluation shows the feasibility of manufacturability analysis with the developed framework and its limitations. INDEX TERMSAdditive manufacturing, computer-aided design, manufacturability analysis, knowledge representation, ontology.

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A data-driven approach for predicting printability in metal additive manufacturing processes

Cited by 49 publications

References 40 publications

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

Test artefacts for additive manufacturing: A design methodology review

Knowledge-Driven Manufacturability Analysis for Additive Manufacturing

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