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Smart and Sustainable Manufacturing Systems Cover Image for Volume 8, Issue 1
Current Issue
Volume 8,
Issue 1,
2024
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About this Journal

SSMS fosters transdisciplinary research that crosses the boundaries of information science, systems engineering and engineering design, manufacturing, and product life cycle with a focus on how to make manufacturing systems smarter and sustainable. Read more...

Editors-in-Chief

Dr. Sudarsan Rachuri
Department of Energy

Dr. Yinlun Huang, Ph.D.
Wayne State University

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Informing Circularity-Focused Design Optimization Using Parametric Life-Cycle Engineering Simulation Models
A Digital Twin for a Real-Time Hardened Case Prediction in Induction Hardening Applications
Enhancing Online Job Shop Scheduling Efficiency: Simulation-Based Optimization Incorporating Human Productivity Factors and TOPSIS Ranking
Optimizing Stereolithography Printing Parameters for Enhanced Microfluidic Chip Quality
Hybrid Analytics in Smart Manufacturing Systems—Addressing the Manufacturing Data Challenge
A Graphical Representation of Sensor Mapping for Machine Tool Fault Monitoring and Prognostics for Smart Manufacturing
Realization of System Robustness by Clustering to Predict New Product Performance Levels
Analyzing Security Risks in Cyber-Physical Manufacturing Systems with Actor–Network Theory
Team–Based Learning of Sustainability: Incorporation of Sustainability Concept and Assessment into Chemical Engineering Senior Design Course
Developing the Workforce for Next-Generation Smart Manufacturing Systems: A Multidisciplinary Research Team Approach
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
Guidance on the Use of Existing ASTM Polymer Testing Standards for ABS Parts Fabricated Using FFF
Defect Detection and Monitoring in Metal Additive Manufactured Parts through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals
In Situ Monitoring of Thin-Wall Build Quality in Laser Powder Bed Fusion Using Deep Learning
Visualization and Explainable Machine Learning for Efficient Manufacturing and System Operations
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