Applied Sciences

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Special Issue Editors

Dr. George Panoutsos

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Guest Editor

Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S10 2TN, UK
Interests: computational intelligence; human-level machine intelligence; artificial intelligence; interpretable machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hector Basoalto

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Guest Editor

Department of Materials Science and Engineering, University of Sheffield, Sheffield S10 2TG, England
Interests: Simulation of Manufacturing and Materials (PRISM2); microstructure; manufacturing process; engineering alloys. ICME frameworks; simulation of materials behaviour relevant to manufacturing and in-service conditions

Special Issue Information

Dear Colleagues,

The topic of Additive Manufacturing has seen a dramatic increase in research activity in recent years, with significant attempts to better understand processes at a fundamental level, develop hardware and software technologies that will enable better process monitoring, control and optimisation, as well as develop computational frameworks to ensure robust manufacture. At the core of many research efforts is the non-trivial task of development and use of mathematical models to capture and understand process-powder-part behaviours. A range of mathematical modelling methods are used, such as physics-driven analytical and numerical methods as well as data-driven methods relying on data-science and machine learning attempts. In this special issue, we aim to exemplify state-of-the-art modelling approaches with application to additive manufacturing, towards demonstrations of better understanding underlying AM physics and processes, as well as demonstrations of standalone models or integrated model-based systems that aim to monitor, control or optimise an AM process. Case studies describing real-life applications within laboratory or industrial environments are also very welcome.

Prof. Dr. George Panoutsos
Prof. Dr. Hector Basoalto
Guest Editor

Manuscript Submission Information

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Keywords

Additive Manufacturing
Modelling in Additive Manufacturing
Numerical Modelling
Data-Driven Modelling
Machine Learning
Process Optimisation
Process Control

Published Papers (2 papers)

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Research

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21 pages, 5086 KiB

Open AccessArticle

Computationally Efficient Models of High Pressure Rolling for Wire Arc Additively Manufactured Components

by Valeriy Gornyakov, Yongle Sun, Jialuo Ding and Stewart Williams

Appl. Sci. 2021, 11(1), 402; https://doi.org/10.3390/app11010402 - 4 Jan 2021

Cited by 13 | Viewed by 2364

Abstract

High pressure multi-layer rolling is an effective method to reduce residual stress and distortion in metallic components built by wire arc additive manufacturing (WAAM). However, the mechanisms of the reduction in residual stress and distortion during multi-layer rolling are not well understood. Conventional finite element models for rolling are highly inefficient, hindering the simulation of multi-layer rolling for large WAAM components. This study aims to identify the most suitable modelling technique for finite element analysis of large WAAM component rolling. Four efficient rolling models were developed, and their efficiency and accuracy were compared with reference to a conventional large-scale rolling model (i.e., control model) for a WAAM built wall. A short-length transient model with fewer elements than the control model was developed to reduce computational time. Accurate predictions of stress and strain and a reduction in computational time by 96.5% were achieved using the short-length model when an implicit method for numerical solution was employed, while similar efficiency but less accurate prediction was obtained when an explicit solution method was adopted. A Eulerian steady-state model was also developed, which was slightly less efficient (95.91% reduction in computational time) but was much less accurate due to unrealistic representation of rolling process. The applicability of a 2D rolling model was also examined and it was found that the 2D model is highly efficient (99.52% time reduction) but less predictive due to the 2D simplification. This study also shows that the rigid roller adopted in the models is beneficial for improving efficiency without sacrificing accuracy. Full article

(This article belongs to the Special Issue Advances in Modelling for Additive Manufacturing)

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Review

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27 pages, 4590 KiB

Open AccessReview

Towards Machine Learning for Error Compensation in Additive Manufacturing

by Amzar Omairi and Zool Hilmi Ismail

Appl. Sci. 2021, 11(5), 2375; https://doi.org/10.3390/app11052375 - 8 Mar 2021

Cited by 23 | Viewed by 4991

Abstract

Additive Manufacturing (AM) of three-dimensional objects is now being progressively realised with its ad-hoc approach with minimal material wastage (lean manufacturing) being one of its benefit by default. It could also be considered as an evolutional paradigm in the manufacturing industry with its long list of application as of late. Artificial Intelligence is currently finding its usefulness in predictive modelling to provide intelligent, efficient, customisable, high-quality and sustainable-oriented production process. This paper presents a comprehensive survey on commonly used predictive models based on heuristic algorithms and discusses their applications toward making AM “smart”. This paper summarises AM’s current trend, future opportunity, gaps, and requirements together with recommendations for technology and research for inter-industry collaboration, educational training and technology transfer in the AI perspective in-line with the Industry 4.0 developmental process. Moreover, machine learning algorithms are presented for detecting product defects in the cyber-physical system of additive manufacturing. Based on reviews on various applications, printability with multi-indicators, reduction of design complexity threshold, acceleration of prefabrication, real-time control, enhancement of security and defect detection for customised designs are seen of as prospective opportunities for further research. Full article

(This article belongs to the Special Issue Advances in Modelling for Additive Manufacturing)

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