Machine Learning (ML) provides an avenue to gain this insight by 1) learning fundamental knowledge about AM processes and 2) identifying predictive and actionable recommendations to optimize part quality and process design. This report presents a literature review of ML applications in AM. The review identifies areas in the
In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various
This paper focuses on recent advances in algorithm-based methods for additive manufacturing processes, especially machine learning approaches. Three main additive manufacturing stages are explored and discussed including geometrical design, process parameter configuration, and in situ anomaly detection. Discussions on current
3 machine learning technique categories and tasks in polymer additive manufacturing ML is a sub-field of artificial intelligence (AI) that employs a data-based modeling approach to uncover patterns within a dataset. 97 By extracting these patterns, ML algorithms can make predictions for previously unseen cases.
Additive manufacturing (AM) is poised to bring a revolution due to its unique production paradigm. It offers the prospect of mass customization, flexible
The last few decades have witnessed concurrent progress in deep learning, machine learning, and additive manufacturing. Notably, applications of additive
In this paper, several algorithms, with a focus on machine learning methods, are reviewed and explored to systematically tackle the three main stages of the additive
Possible routes to intensify catalytic reactors: Multiscale Modeling, Machine Learning and Additive Manufacturing. Multiscale Modeling enables designing, investigating and analyzing catalytic reactors by accounting for the governing phenomena at each scale. This is achieved by using a first-principles approach at each scale revealing
This paper focuses on recent advances in algorithm-based methods for additive manufacturing processes, especially machine learning approaches. Three main additive manufacturing stages are explored
1.1.3 Types of machine learning models. Machine learning models can be divided into four main categories: Supervised machine learning whereby the output
Machine learning in design for additive manufacturing. DfAM significantly differs from the design principles commonly practised in conventional
For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image
This review article demonstrated that the application of machine learning to additive manufacturing of titanium alloys is an extremely promising field of study, with the concepts potentially providing a robust framework for designing and manufacturing titanium-based alloys with favorable characteristics. Also, this review of state-of-the-art
Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit. Manuf. 25, 151–165 (2019).
1. Introduction. Additive manufacturing (AM), as opposed to traditional subtractive manufacturing technologies, is a promising digital approach for the modern industrial paradigm that has gained widespread interest all over the world [1], [2], [3], [4] fabricating objects layer by layer from three-dimensional (3D) computer-aided design
Moreover, additive manufacturing processes require knowledge about how process parameters affect material properties and melt pool dynamics to produce defect-free parts. Thus, creating machine learning models in additive manufacturing is difficult, due to the lack of sufficient data and the complexity of MAM processes [3].
This has drawn increasing attention towards the use of machine learning in the manufacturing industry, particularly in the field of additive manufacturing (Jiang et al. 2020; Qin et al. 2022 ). The rapid progress of machine learning in additive manufacturing has brought us to this special issue, where we hope to bring together
3.3 Machine Learning (ML) in Additive Manufacturing Additive Manufacturing is becoming increasingly data-intensive while generating a growing amount of newly accessible data. The availability of AM data affords Design for Additive Manufacturing (DfAM) a new chance to develop AM design guidelines with a deeper
Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies
In this paper, several algorithms, with a focus on machine learning methods, are reviewed and explored to systematically tackle the three main stages of the
Machine learning is now a hot technology that has been used in medical diagnosis, image processing, prediction, classification, learning association, regression, etc. Currently, focuses are increasingly given to using machine learning in the manufacturing industry, including additive manufacturing.
In situations requiring high levels of customization and limited production volumes, additive manufacturing (AM) is a frequently utilized technique with several benefits. To properly configure all the parameters required to produce final goods of the utmost quality, AM calls for qualified designers and experienced operators. This research
Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with
In this review, some of the latest applicable methods of machine learning (ML) in additive manufacturing (AM) have been presented and the classification of the most common ML techniques and designs for AM have been evaluated. Generally, AM methods are capable of creating complex designs and have shown great efficiency in the customization of
Machine learning approaches seem to be a promising solution to tackle the challenges in the additive manufacturing field. This paper employs a systematic literature review by employing natural language processing and text mining techniques to analyze the recent advancement in the application of machine learning in porosity detection and
Machine learning (ML), a subset of artificial intelligence (AI), has increasingly become popular in additive manufacturing (AM) research. Additive manufacturing, also known as 3D printing or rapid prototyping (RP), is defined as a group of layer-upon-layer fabrication processes controlled by a computer-aided design (CAD) model [1, 2].
Machine learning plays a significant role in additive manufacturing, where it finds numerous applications. The integration of AI in additive manufacturing has the potential to revolutionize the
Machine Learning (ML) has been introduced to various Additive Manufacturing (AM) fields due to its functional ability to recognize complex process-structure–property (PSP) relationships. Yet, it has only been heavily employed in applications of DED very recently.
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 recent years, machine learning
Abstract: Quality of electronic products fabricated with additive manufacturing (AM) techniques such as 3D inkjet printing can be assured by adopting pro-active predictive models for process condition monitoring instead of using conventional post-manufacture assessment techniques. This paper details a model-based approach, and associated
Machine learning (ML), a subset of artificial intelligence (AI), has increasingly become popular in additive manufacturing (AM) research. Additive manufacturing, also known as 3D printing or rapid prototyping (RP), is defined as a group of layer-upon-layer fabrication processes controlled by a computer-aided design (CAD) model [1, 2].
In this review article, the latest applications of machine learning (ML) in the additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly