AI IN MANUFACTURING

Artificial Intelligence has been widely adopted by companies in the manufacturing sector. AI-powered applications in the manufacturing environment spans across remote maintenance, predictive and prescriptive maintenance, production and supply chain optimisation, and decision-support tools to streamline processes across the value chain.

AI tools also support research in areas related to manufacturing but can be applied to other sectors too – for example, material research, resource planning and optimisation, waste minimisation and process control. AI is also being used to increase the resilience of our manufacturing systems with simulation research to assist in “what-if” scenario analysis and managing uncertainties in an increasingly dynamic and complex manufacturing environment.

AI in Manufacturing

Key Researchers

Dr Tan Puay Siew, A*STAR Singapore Institute of Manufacturing Technology (A*STAR SIMTech)
Dr Joel Tay, A*STAR Singapore Institute of Manufacturing Technology (A*STAR SIMTech)
Dr Tran Van Tung, A*STAR Singapore Institute of Manufacturing Technology (A*STAR SIMTech)
Dr Ernest Tan, A*STAR Advanced Remanufacturing and Technology Centre (A*STAR ARTC)
Dr Zhang Jie, A*STAR Advanced Remanufacturing and Technology Centre (A*STAR ARTC)
Dr Tan Teck Leong, A*STAR Institute of High Performance Computing (A*STAR IHPC)

Key Projects

Sectoral AI Centre of Excellence for Manufacturing (AIMfg)

AIMfg

The Sectoral AI Centre of Excellence for Manufacturing (AIMfg), launched on September 27, 2024, is a pilot initiative jointly developed by the Ministry of Trade and Industry (MTI) and the Agency for Science, Technology and Research (A*STAR). It forms part of Singapore’s effort under the Singapore National AI Strategy 2.0 (NAIS 2.0) to build a thriving industry ecosystem in Singapore with significant value creation from AI and capabilities across the AI technology stack, with the aim to drive AI innovation in the manufacturing sector by developing solutions for quality assurance, operations optimisation, predictive maintenance, product design, and industrial automation.

AIMfg addresses common challenges in adopting AI, such as limited expertise and data availability, by offering tailored AI solutions, scalable models, and an AI Sandbox for experimentation.

Find out more about AIMfg here.

Cyber-Physical Production System (CPPS)

CYBER-PHYSICAL PRODUCTION SYSTEM

The aim of the Cyber-Physical Production System (CPPS) project was to develop intelligent and contextual decision support capabilities for Cyber-Physical Production System. This project delivered a scalable CPPS platform that covers dynamic cyber-physical modelling supported by online large-scale simulation and multi-objective, multi-level optimisation capabilities.

It also delivered contextual and intelligent decision support technologies that involve machine learning for context-aware insights and autonomous root-cause-effect analysis to support intelligent decision making. This project involved collaboration with local and overseas IHLs (Technische Universität Braunschweig, NUS, NTU) as well as Inter-RI efforts (A*STAR SIMTech, A*STAR NMC, A*STAR ARTC).

The novelty of CPPS is to create a targeted system approach for sense & response manufacturing environment with a complete digital thread that is able to handle relationships on-the-fly to support different applications. Single source of truth, contextually accurate total visibility, reinforced by fast decision-support/making capabilities, with predictive & prescriptive capabilities and fast decision-making capabilities that is tractable and able to handle uncertainties arising in real-world problems.

HUMAN-ROBOT COLLABORATIVE AI FOR AME PROGRAMME (COLLAB AI)

The programme aims to enable robots to perform as team members alongside humans. It is developing technologies to understand tasks, procedures and human actions, as well as to learn quickly and robustly, for robots to be able to learn from humans and work safely in human environments. Collab AI is led by A*STAR IHPC in collaboration with A*STAR I2R, A*STAR ARTC, NUS, NTU and SUTD.

Philips Industry Project (Extension of CPPS outcome)

PHILIPS INDUSTRY PROJECT

The objective of this project is to develop a Predictive Maintenance (PdM) system including an on-board sensing technology that incorporates IoT and on-board signal processing for machine condition monitoring and alerting. An AI-based analytical too for machine health assessment, diagnosis, performance prediction and implementing the PdM system for 3 types of machines.

This PdM solution provides a comprehensive system consisting of sensorisation for brownfield connectivity, data routine collection, machine condition monitoring, fault diagnosis, and prognosis. Since machines in mass production are different mechanical states and behaviours, the AI models are developed based on continual learning algorithm that makes it easily adaptable to change of machine states. This novelty can help company scaling the system for multiple machines in factory.

This is an ongoing industry project collaboration with Philips.

Deep-Learning Driven Edge Caching-As-A-Service (DECaas)

EDGE CACHING

When an end-to-end network is not optimised, it introduces unnecessary network latency and errors which causes challenges in meeting the Quality-of-Service (QoS) requirements for mission critical or delay-sensitive industrial Internet-of-Things (IIoT) applications. Hence, a deep learning-driven edge caching protocol has been developed to bring contents from a remote central server, closer to the end users.

The aim of this project is to encapsulate this protocol into a deep learning-driven edge caching-as-a-service (DECaaS). DECaaS leverages on multi-access edge computing (MEC) and deep learning methods to predict future content demands of end users before caching the predicted popular contents at MEC servers. In this way, DECaaS mitigates the problem of network latency and errors.

The novelty of DECaaS lies in the realisation of deep learning-based edge caching capabilities for reliable low-latency content delivery over both cellular and IIoT networks. DECaaS is developed for general file downloads and Open Platform Communications Unified Architecture (OPC UA) tag access for sensor data. For both use cases, DECaaS is employed to predict the demand of files and OPC UA tags before caching it at an MEC server.

DECaaS is an ongoing GAP-funded project, with interest from Singtel in advancing this technology towards commercialisation.

AI for Accelerating High Entropy Alloy (HEA) Development

ACCELERATING HIGH ENTROPY ALLOYPicture5

This research involves the development of high-throughput simulations workflow based on first-principles and CALPHAD simulations to generate structure and formation datasets of High-Entropy Alloys (HEAs). Trained on these datasets, machine-learned (ML) surrogate models are developed to predict the composition-microstructure-stability relationships of HEAs. The ML models are then utilised in optimization algorithms and Monte-Carlo based methods to identify promising compositions for experimental validation, in terms of their microstructure, properties and manufacturability.

In parallel, machine-learned interatomic interactions are also developed to enable realistic atomistic simulations of mechanical behaviour of HEAs, covering microstructural stability, short range ordering, strengthening mechanisms, elastic and yield strengths.