C.S. Lim

Bio: C.S. Lim is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Digital manufacturing. The author has an hindex of 1, co-authored 1 publications receiving 64 citations.

Machine learning in additive manufacturing: State-of-the-art and perspectives

Abstract: Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology. However, its broad adoption in industry is still hindered by high entry barriers of design for additive manufacturing (DfAM), limited materials library, various processing defects, and inconsistent product quality. In recent years, machine learning (ML) has gained increasing attention in AM due to its unprecedented performance in data tasks such as classification, regression and clustering. This article provides a comprehensive review on the state-of-the-art of ML applications in a variety of AM domains. In the DfAM, ML can be leveraged to output new high-performance metamaterials and optimized topological designs. In AM processing, contemporary ML algorithms can help to optimize process parameters, and conduct examination of powder spreading and in-process defect monitoring. On the production of AM, ML is able to assist practitioners in pre-manufacturing planning, and product quality assessment and control. Moreover, there has been an increasing concern about data security in AM as data breaches could occur with the aid of ML techniques. Lastly, it concludes with a section summarizing the main findings from the literature and providing perspectives on some selected interesting applications of ML in research and development of AM.

About metastable cellular structure in additively manufactured austenitic stainless steels

Abstract: The quick-emerging paradigm of additive manufacturing technology has revealed salient advantages in enabling the tailored-design of structural components with more exceptional performances over ordinary subtractive processing routines. As a peculiar feature, sub-micro cellular structures widely exist in additively manufactured (AM) metallic materials. This phenomenon primarily appears with high-density dislocations and segregated elements or precipitates at the cellular boundaries. The discovery of novel metastable substructures in various alloys through numerous investigations has proven their substantial effects on the engineering properties of AM components. This paper reviews the most recent research momentum regarding the formation mechanisms (elemental segregation, dislocation cell and oxide inclusion), the kinetics of the size and morphology, the growth orientation and the thermodynamic stability of these cellular structures by taking AM austenitic stainless steel as an exemplary material. Another topic of concern here is the inherent correlation between the unique cellular microstructure and the corresponding mechanical properties (strength, ductility, fatigue, etc.) and corrosion responses (passivity, irradiation damage, hydrogen embrittlement, etc.) for this category of AM materials. The design, control, and optimization of cellular structures for additive manufacturing techniques are expected to inspire new strategies for advancing high-performance structural alloy development.

Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing

Abstract: The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited despite the significant progress made in the development of the process chain. One of the key obstacles i...

Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

Abstract: Biomimicry, adapting and implementing nature's designs provides an adequate first-order solution to achieving superior mechanical properties. However, the design space is too vast even using biomimetic designs as prototypes for optimization. Here, we propose a new approach to design hierarchical materials using machine learning, trained with a database of hundreds of thousands of structures from finite element analysis, together with a self-learning algorithm for discovering high-performing materials where inferior designs are phased out for superior candidates. Results show that our approach can create microstructural patterns that lead to tougher and stronger materials, which are validated through additive manufacturing and testing. We further show that machine learning can be used as an alternative method of coarse-graining – analyzing and designing materials without the use of full microstructural data. This novel paradigm of smart additive manufacturing can aid in the discovery and fabrication of new material designs boasting orders of magnitude increase in computational efficacy over conventional methods.

A Review on Additive Manufacturing of Titanium Alloys for Aerospace Applications: Directed Energy Deposition and Beyond Ti-6Al-4V

Abstract: Titanium alloys are expensive and difficult to process into large complex components for aerospace applications. Directed energy deposition (DED), one of the additive manufacturing (AM) technologies, offers a high deposition rate, being suitable for fabricating large metallic components. So far, most review articles on the AM of titanium discuss the popular powder bed fusion method with the emphasis on the “workhorse” titanium alloy—Ti-6Al-4V. There have been few review articles on the DED process of a broad range of titanium alloys—near-α, β, and other α + β alloys beyond Ti-6Al-4V. This article focuses on the processing–microstructure–property relationships in the DED-processed titanium alloys (Ti-6Al-4V and beyond) with the following aspects: (1) microstructure evolution induced by solidification, thermal cycles, and post-processing heat treatment; (2) tensile properties of as-deposited and heat-treated titanium alloys; (3) defects, residual stresses, and fatigue properties; and (4) micro/nanomechanical properties. The article concludes with perspectives about future directions in this field.

Machine learning in predicting mechanical behavior of additively manufactured parts

Abstract: Although applications of additive manufacturing (AM) have been significantly increased in recent years, its broad application in several industries is still under progress. AM also known as three-dimensional (3D) printing is layer by layer manufacturing process which can be used for fabrication of geometrically complex customized functional end-use products. Since AM processing parameters have significant effects on the performance of the printed parts, it is necessary to tune these parameters which is a difficult task. Today, different artificial intelligence techniques have been utilized to optimize AM parameters and predict mechanical behavior of 3D-printed components. In the present study, applications of machine learning (ML) in prediction of structural performance and fracture of additively manufactured components has been presented. This study first outlines an overview of ML and then summarizes its applications in AM. The main part of this review, focuses on applications of ML in prediction of mechanical behavior and fracture of 3D-printed parts. To this aim, previous research works which investigated application of ML in characterization of polymeric and metallic 3D-printed parts have been reviewed and discussed. Moreover, the review and analysis indicate limitations, challenges, and perspectives for industrial applications of ML in the field of AM. Considering advantages of ML increase in applications of ML in optimization of 3D printing parameters, prediction of mechanical performance, and evaluation of 3D-printed products is expected.