Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures

Cathal Hoare; Stephen Ryan

doi:10.1145/3786179.3788324

Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

While offering significant advantages, Metal Additive Manufacturing (MAM) faces persistent challenges in process stability and reproducibility. Data-driven models using in-situ sensor data enable real-Time quality control and defect mitigation but are difficult to deploy for resource-constrained manufacturers. This work presents a Digital Shadow-informed Decision Support System (DSS) designed to integrate machine learning (ML) capabilities through a Service-Oriented Architecture (SOA) and Low-Code/No-Code (LCNC) development methodologies. The approach facilitates rapid creation and deployment of modular analysis services for MAM, allowing small and medium enterprises (SMEs) to implement adaptive, data-driven diagnostic and decision-support tools for reliable MAM based production.

Original language	English
Title of host publication	Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026
Publisher	Association for Computing Machinery, Inc
Pages	23-26
Number of pages	4
ISBN (Electronic)	9798400724107
DOIs	https://doi.org/10.1145/3786179.3788324
Publication status	Published - 7 May 2026
Event	2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026 - Rio de Janeiro, Brazil Duration: 12 Apr 2026 → 18 Apr 2026

Publication series

Name	Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026

Conference

Conference	2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026
Country/Territory	Brazil
City	Rio de Janeiro
Period	12/04/26 → 18/04/26

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1145/3786179.3788324

Cite this

Hoare, C., & Ryan, S. (2026). Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures. In Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026 (pp. 23-26). (Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026). Association for Computing Machinery, Inc. https://doi.org/10.1145/3786179.3788324

Hoare, Cathal ; Ryan, Stephen. / Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures. Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026. Association for Computing Machinery, Inc, 2026. pp. 23-26 (Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026).

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title = "Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures",

abstract = "While offering significant advantages, Metal Additive Manufacturing (MAM) faces persistent challenges in process stability and reproducibility. Data-driven models using in-situ sensor data enable real-Time quality control and defect mitigation but are difficult to deploy for resource-constrained manufacturers. This work presents a Digital Shadow-informed Decision Support System (DSS) designed to integrate machine learning (ML) capabilities through a Service-Oriented Architecture (SOA) and Low-Code/No-Code (LCNC) development methodologies. The approach facilitates rapid creation and deployment of modular analysis services for MAM, allowing small and medium enterprises (SMEs) to implement adaptive, data-driven diagnostic and decision-support tools for reliable MAM based production.",

author = "Cathal Hoare and Stephen Ryan",

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Hoare, C & Ryan, S 2026, Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures. in Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026. Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026, Association for Computing Machinery, Inc, pp. 23-26, 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026, Rio de Janeiro, Brazil, 12/04/26. https://doi.org/10.1145/3786179.3788324

Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures. / Hoare, Cathal ; Ryan, Stephen.
Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026. Association for Computing Machinery, Inc, 2026. p. 23-26 (Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - While offering significant advantages, Metal Additive Manufacturing (MAM) faces persistent challenges in process stability and reproducibility. Data-driven models using in-situ sensor data enable real-Time quality control and defect mitigation but are difficult to deploy for resource-constrained manufacturers. This work presents a Digital Shadow-informed Decision Support System (DSS) designed to integrate machine learning (ML) capabilities through a Service-Oriented Architecture (SOA) and Low-Code/No-Code (LCNC) development methodologies. The approach facilitates rapid creation and deployment of modular analysis services for MAM, allowing small and medium enterprises (SMEs) to implement adaptive, data-driven diagnostic and decision-support tools for reliable MAM based production.

AB - While offering significant advantages, Metal Additive Manufacturing (MAM) faces persistent challenges in process stability and reproducibility. Data-driven models using in-situ sensor data enable real-Time quality control and defect mitigation but are difficult to deploy for resource-constrained manufacturers. This work presents a Digital Shadow-informed Decision Support System (DSS) designed to integrate machine learning (ML) capabilities through a Service-Oriented Architecture (SOA) and Low-Code/No-Code (LCNC) development methodologies. The approach facilitates rapid creation and deployment of modular analysis services for MAM, allowing small and medium enterprises (SMEs) to implement adaptive, data-driven diagnostic and decision-support tools for reliable MAM based production.

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M3 - Conference contribution

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Hoare C , Ryan S. Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures. In Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026. Association for Computing Machinery, Inc. 2026. p. 23-26. (Proceedings 2026 2nd International Workshop on Low-Code Development and Software Architecture, LArc 2026). doi: 10.1145/3786179.3788324

Integrating Machine Learning into Additive Manufacturing through Service-Oriented and Low-Code Architectures

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