design for manufacturability

This study addresses the quality control challenges in additive manufacturing arising from its layer-by-layer fabrication process and high degree of customization. It presents the first systematic adaptation of the Six Sigma DMAIC methodology tailored to this context. By integrating multi-source sensing and measurement data across the entire manufacturing chain—including materials, design, process parameters, and post-processing—the work employs advanced techniques such as deep learning, machine learning, design of experiments, simulation, ontological analysis, and network science to model the complex relationships among design inputs, process variations, and final part quality. The proposed optimization framework enables real-time anomaly detection and simultaneously optimizes lead time and energy consumption, thereby significantly enhancing the quality stability and process controllability of additive manufacturing systems.

This work addresses the persistent challenges in fused deposition modeling (FDM) printing—such as poor printability, insufficient mechanical strength, and complex post-processing caused by geometric defects like steep overhangs—by introducing the first end-to-end multi-agent system capable of automatically repairing original CAD models. The proposed framework integrates B-Rep parsing, graph neural network–based semantic recognition, and multimodal large language model reasoning to detect manufacturability issues and generate optimized STEP files along with detailed modification reports. By constructing face adjacency topological graphs, applying GraphSAGE for semantic labeling, leveraging Claude Sonnet for design suggestions, and validating modifications via GPT-4o’s visual reasoning, the system automates the entire pipeline from geometric analysis to natural-language design recommendations. Evaluated on a birdhouse model, it accurately identified overhang regions and effectively proposed corrective strategies such as chamfering, filleting, or part reorientation, substantially overcoming the reliance on manual intervention inherent in traditional design-for-manufacturing approaches.

To address uncontrolled geometric inaccuracy in high-deposition-rate robotic additive manufacturing (HDR-RAM) caused by open-loop operation, this paper proposes the 3D-DM² framework—the first real-time 3D deviation mapping and defect monitoring system tailored for cold-spray processes. The method integrates robot-path-synchronized online 3D scanning, lightweight point-cloud registration, incremental surface reconstruction, and layer-wise deviation segmentation to enable dynamic identification, continuous tracking, and early-stage defect detection during deposition. Experimental results demonstrate stable sub-millimeter deviation detection under high deposition rates, enabling closed-loop geometric compensation. This significantly reduces post-processing requirements and enhances dimensional consistency and process stability for complex components.

Current 3D printing defect detection methods rely heavily on expert intervention or task-specific models requiring extensive labeled data, exhibiting poor generalization across printers and firmware versions. To address this, we propose the first large language model (LLM)-based real-time monitoring and closed-loop control system for additive manufacturing. Our approach integrates multimodal perception of inter-layer images and leverages an LLM for zero-shot fault attribution, policy reasoning, and repair instruction generation—without domain-specific fine-tuning or annotated data. The system interfaces directly with printer APIs to autonomously execute corrective actions. It generalizes across heterogeneous hardware and firmware, accurately identifying common defects—including inconsistent extrusion, stringing, warping, and interlayer adhesion failure—localizing root-cause parameters (e.g., nozzle temperature, print speed, bed leveling), and dynamically adjusting them in real time. Fully automated and human-in-the-loop-free, it achieves diagnostic and corrective performance comparable to that of experienced AM engineers.

This work addresses the tight coupling between design intent and printer-specific representations in heterogeneous manufacturing, which hinders cross-platform reuse. The authors propose a novel compiler architecture that models fabrication-aware design as a staged, type-directed lowering process, decoupling source design, attribute translation, and backend compilation to enable manufacturing-agnostic expression. Introducing compiler paradigms to heterogeneous manufacturing for the first time, the approach unifies volumetric information—such as material composition, hardness, and color—through implicit geometry and typed spatial attribute fields, automatically generating voxel stacks, G-code, or slicer projects. Experiments demonstrate successful fabrication of complex objects embedding CT data, Shore hardness fields, and full-color fields on both material jetting and extrusion platforms, validating cross-process reusability. The accompanying Python toolkit is publicly released.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.