About this Research Topic
Additive manufacturing (AM) and subtractive manufacturing (SM) are two distinct yet complementary manufacturing processes that are being increasingly transformed by the integration of artificial intelligence (AI) techniques. This topic description explores the synergistic opportunities and advancements arising from the intersection of these technologies.
AM, commonly known as 3D printing, is a layer-by-layer material deposition process that enables the fabrication of complex geometries without the need for product-specific tooling. The geometric freedom inherent to AM has led to a surge in mass personalisation and customisation across a wide range of industries. Concurrently, the rapid development of AI, particularly in the areas of machine learning, has unlocked unprecedented pattern recognition and predictive capabilities. Conversely, subtractive manufacturing (SM) is a traditional manufacturing process that involves removing material from a workpiece to obtain the desired shape and dimensions. While AM offers unparalleled geometric freedom, SM excels in producing parts with high dimensional accuracy and surface quality. Integrating AI techniques with SM can greatly enhance these processes' optimisation, control, and understanding.
Integrating AI techniques with both AM and SM processes offers significant potential for optimisation, quality control, and process understanding. Machine learning algorithms can model the complex relationships between process parameters, material properties, and part performance in additive and subtractive manufacturing. This can lead to improved process-structure-property correlations, enhanced design for manufacturability, and more efficient optimisation strategies, such as generative design and topology optimisation.
Furthermore, AI-enabled in-situ monitoring and defect detection can revolutionise quality control in AM and SM, allowing for real-time adjustments and compensation of internal defects. Feedback control systems leveraging AI can further improve process stability and part consistency, paving the way for more reliable and repeatable manufacturing.
We will consider manuscripts, reviews, and mini-reviews on exploring the applicability of machine learning techniques for topics of interest including, but not limited to the following:
For Additive Manufacturing (AM):
· Process-structure-performance properties: Modeling the complex relationships between AM process parameters, material properties, and part performance using machine learning techniques.
· Design for Additive Manufacturing: Leveraging AI-driven methods like generative design and topology optimisation to enhance part design for AM.
· Simulation for Additive Manufacturing: Developing AI-powered simulation models to predict the behaviour and outcomes of AM processes accurately.
· In-situ Process Monitoring: Utilizing AI-enabled sensors and computer vision for real-time monitoring and control of AM processes.
· Internal Defect Detection: Applying machine learning algorithms for automated detection and classification of internal defects in AM parts.
· Internal Defect Compensation: Implementing AI-based feedback control systems to compensate for and mitigate internal defects during the AM process.
· Post-processing for Additive Manufacturing: Integrating AI techniques to optimise and automate post-processing operations for AM parts.
· Part Quality Metrics: Developing AI-driven methods to establish robust quality metrics and ensure consistent part performance in AM.
For Subtractive Manufacturing (SM):
· Process-structure-performance properties: Modeling the complex relationships between SM process parameters, material properties, and part performance using machine learning techniques.
· Design for Subtractive Manufacturing: Leveraging AI-driven methods to enhance part design for optimal manufacturability in SM.
· Simulation for Subtractive Manufacturing: Developing AI-powered simulation models to predict the behaviour and outcomes of SM processes accurately.
· In-situ Process Monitoring: Utilizing AI-enabled sensors and computer vision for real-time monitoring and control of SM processes.
· Tool Wear and Breakage Detection: Applying machine learning algorithms for automated detection and prediction of tool wear and breakage in SM.
· Feedforward and Feedback Control: Implementing AI-based control systems to optimise and maintain process stability in SM.
· Part Quality Metrics: Developing AI-driven methods to establish robust quality metrics and ensure consistent part performance in SM.
This research topic aims to drive advancements in intelligent, adaptive, and high-performance manufacturing solutions by exploring the applicability of machine learning techniques across AM and SM. The overarching goal of this research topic is to bring together researchers, engineers, and practitioners from diverse backgrounds to explore the synergies between AI techniques and both additive and subtractive manufacturing. Potential areas of investigation include, but are not limited to:
a. Leveraging machine learning for process-structure-performance modelling
b. Optimising design and process parameters using AI-driven techniques
c. Enhancing in-situ monitoring and real-time defect detection through AI
d. Developing AI-based feedback control systems for improved process stability
e. Exploring the integration of AI with post-processing operations
f. Investigating the applicability of AI in hybrid manufacturing systems
By fostering interdisciplinary collaboration and knowledge exchange, this research topic aims to define and advance the cutting edge of AI-driven additive and subtractive manufacturing, paving the way for a new era of intelligent, adaptive, and high-performance manufacturing solutions.
AM, commonly known as 3D printing, is a layer-by-layer material deposition process that enables the fabrication of complex geometries without the need for product-specific tooling. The geometric freedom inherent to AM has led to a surge in mass personalisation and customisation across a wide range of industries. Concurrently, the rapid development of AI, particularly in the areas of machine learning, has unlocked unprecedented pattern recognition and predictive capabilities. Conversely, subtractive manufacturing (SM) is a traditional manufacturing process that involves removing material from a workpiece to obtain the desired shape and dimensions. While AM offers unparalleled geometric freedom, SM excels in producing parts with high dimensional accuracy and surface quality. Integrating AI techniques with SM can greatly enhance these processes' optimisation, control, and understanding.
Integrating AI techniques with both AM and SM processes offers significant potential for optimisation, quality control, and process understanding. Machine learning algorithms can model the complex relationships between process parameters, material properties, and part performance in additive and subtractive manufacturing. This can lead to improved process-structure-property correlations, enhanced design for manufacturability, and more efficient optimisation strategies, such as generative design and topology optimisation.
Furthermore, AI-enabled in-situ monitoring and defect detection can revolutionise quality control in AM and SM, allowing for real-time adjustments and compensation of internal defects. Feedback control systems leveraging AI can further improve process stability and part consistency, paving the way for more reliable and repeatable manufacturing.
We will consider manuscripts, reviews, and mini-reviews on exploring the applicability of machine learning techniques for topics of interest including, but not limited to the following:
For Additive Manufacturing (AM):
· Process-structure-performance properties: Modeling the complex relationships between AM process parameters, material properties, and part performance using machine learning techniques.
· Design for Additive Manufacturing: Leveraging AI-driven methods like generative design and topology optimisation to enhance part design for AM.
· Simulation for Additive Manufacturing: Developing AI-powered simulation models to predict the behaviour and outcomes of AM processes accurately.
· In-situ Process Monitoring: Utilizing AI-enabled sensors and computer vision for real-time monitoring and control of AM processes.
· Internal Defect Detection: Applying machine learning algorithms for automated detection and classification of internal defects in AM parts.
· Internal Defect Compensation: Implementing AI-based feedback control systems to compensate for and mitigate internal defects during the AM process.
· Post-processing for Additive Manufacturing: Integrating AI techniques to optimise and automate post-processing operations for AM parts.
· Part Quality Metrics: Developing AI-driven methods to establish robust quality metrics and ensure consistent part performance in AM.
For Subtractive Manufacturing (SM):
· Process-structure-performance properties: Modeling the complex relationships between SM process parameters, material properties, and part performance using machine learning techniques.
· Design for Subtractive Manufacturing: Leveraging AI-driven methods to enhance part design for optimal manufacturability in SM.
· Simulation for Subtractive Manufacturing: Developing AI-powered simulation models to predict the behaviour and outcomes of SM processes accurately.
· In-situ Process Monitoring: Utilizing AI-enabled sensors and computer vision for real-time monitoring and control of SM processes.
· Tool Wear and Breakage Detection: Applying machine learning algorithms for automated detection and prediction of tool wear and breakage in SM.
· Feedforward and Feedback Control: Implementing AI-based control systems to optimise and maintain process stability in SM.
· Part Quality Metrics: Developing AI-driven methods to establish robust quality metrics and ensure consistent part performance in SM.
This research topic aims to drive advancements in intelligent, adaptive, and high-performance manufacturing solutions by exploring the applicability of machine learning techniques across AM and SM. The overarching goal of this research topic is to bring together researchers, engineers, and practitioners from diverse backgrounds to explore the synergies between AI techniques and both additive and subtractive manufacturing. Potential areas of investigation include, but are not limited to:
a. Leveraging machine learning for process-structure-performance modelling
b. Optimising design and process parameters using AI-driven techniques
c. Enhancing in-situ monitoring and real-time defect detection through AI
d. Developing AI-based feedback control systems for improved process stability
e. Exploring the integration of AI with post-processing operations
f. Investigating the applicability of AI in hybrid manufacturing systems
By fostering interdisciplinary collaboration and knowledge exchange, this research topic aims to define and advance the cutting edge of AI-driven additive and subtractive manufacturing, paving the way for a new era of intelligent, adaptive, and high-performance manufacturing solutions.
Keywords: machine learning, feedforward control, feedback control, quality control, process-structure-performance, quality metrics, design for additive manufacturing, optimization for additive manufacturing, simulation for additive manufacturing, internal defects, sustainability, Subtractive Manufacturing, Additive manufacturing, Artificial Intelligence, Process-Structure-Property Modeling, Generative Design, Simulation, In-situ Process Monitoring, Computer vision, Intelligent Manufacturing, Hybrid Manufacturing, Tool Wear
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
