Constant-Volume Deformation Manufacturing for Material-Efficient Shaping

2025-11-26

AI Summary

Traditional additive/subtractive manufacturing suffers from volumetric loss, geometric deviation, and discontinuous deformation when fabricating complex geometries. To address these limitations, this work proposes a volume-conserving digital mold paradigm. Our method integrates real-time volumetric consistency modeling, geometry-guided deformation prediction via a deep neural network, and point-cloud-driven error compensation, further enhanced by post-forming point-cloud analysis and elastic springback correction. The resulting adaptive deformation manufacturing pipeline enables high-fidelity, zero-waste, and reproducible plastic forming. Experimental validation across five representative geometric classes demonstrates sub-millimeter average shape fidelity (≤ 0.8 mm), material utilization exceeding 98%, and substantial improvements in manufacturing sustainability and customization capability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: 3D Computer Vision
• Intelligent Robots: Manipulation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Additive and subtractive manufacturing enable complex geometries but rely on discrete stacking or local removal, limiting continuous and controllable deformation and causing volume loss and shape deviations. We present a volumepreserving digital-mold paradigm that integrates real-time volume-consistency modeling with geometry-informed deformation prediction and an error-compensation strategy to achieve highly predictable shaping of plastic materials. By analyzing deformation patterns and error trends from post-formed point clouds, our method corrects elastic rebound and accumulation errors, maintaining volume consistency and surface continuity. Experiments on five representative geometries demonstrate that the system reproduces target shapes with high fidelity while achieving over 98% material utilization. This approach establishes a digitally driven, reproducible pathway for sustainable, zero-waste shaping of user-defined designs, bridging digital modeling, real-time sensing, and adaptive forming, and advancing next-generation sustainable and customizable manufacturing.

Problem

• Achieves high-fidelity shaping with over 98% material utilization
• Corrects elastic rebound and accumulation errors for volume consistency
• Establishes a sustainable, zero-waste digital-mold manufacturing paradigm

Innovation

• Volume-preserving digital-mold paradigm integrates real-time modeling and prediction
• Method corrects elastic rebound and accumulation errors via error-compensation strategy
• Achieves over 98% material utilization for sustainable, zero-waste shaping