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基于知识驱动的LLM决策支持系统用于激光粉末床熔融中的可解释缺陷分析和缓解指导A Knowledge-Driven LLM-Based Decision-Support System for Explainable Defect Analysis and Mitigation Guidance in Laser Powder Bed Fusion

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

激光粉末床熔融（LPBF）制造的最新进展促成了一种新型的知识驱动决策支持系统，该系统利用大型语言模型（LLMs）进行缺陷分析和缓解。该创新系统将结构化缺陷知识与LLMs的高级推理能力相结合，改善了LPBF过程中的缺陷诊断和管理。通过利用全面的知识库和多模态图像评估，该系统增强了可解释性，并为面临缺陷管理挑战的制造商提供了可操作的指导。

Recent advancements in Laser Powder Bed Fusion (LPBF) manufacturing have led to the development of a novel knowledge-driven decision-support system that leverages large language models (LLMs) for defect analysis and mitigation. This innovative system integrates structured defect knowledge with advanced reasoning capabilities of LLMs, allowing for improved diagnosis and management of defects during the LPBF process. By utilizing a comprehensive knowledge base and multimodal image assessments, the system enhances interpretability and provides actionable guidance for manufacturers facing challenges in defect management.

要点

  1. 将LLMs与结构化缺陷知识相结合显著改善了LPBF制造中的缺陷诊断。

    The integration of LLMs with structured defect knowledge significantly improves defect diagnosis in LPBF manufacturing.

  2. 该系统的多模态图像评估能力增强了可解释性，并为缺陷缓解提供了更清晰的指导。

    The system's multimodal image assessment capabilities enhance interpretability and provide clearer guidance for defect mitigation.

  3. 这一发展代表了AI在制造业应用的进步，特别是在增材制造过程中。

    This development represents a step forward in the application of AI in manufacturing, particularly in additive manufacturing processes.

为何重要

这一进展突显了AI驱动解决方案在提升制造过程中的潜力，特别是在解决缺陷管理等复杂问题时。随着各行业越来越多地采用AI技术，有效解读和处理数据的能力变得至关重要。

This advancement highlights the potential of AI-driven solutions to enhance manufacturing processes, particularly in addressing complex issues such as defect management. As industries increasingly adopt AI technologies, the ability to interpret and act on data effectively becomes crucial.

按谁的角度看

产品经理

对于产品经理而言，该系统展示了如何利用AI改善产品质量和减少缺陷，最终提升客户满意度并降低生产成本。

For product managers, this system exemplifies how AI can be leveraged to improve product quality and reduce defects, ultimately leading to better customer satisfaction and lower production costs.

开发者

开发人员和工程师可以从LLMs与结构化知识的结合中学习，为构建更有效的AI系统提供洞见，这些系统能够解读复杂数据并提供可操作的解决方案。

Developers and engineers can learn from this integration of LLMs and structured knowledge, providing insights into building more effective AI systems that can interpret complex data and offer actionable solutions.

学生与求职者

对于学生和求职者而言，这一发展强调了AI在制造业中日益重要的地位，表明AI和数据分析技能在未来就业市场中将变得越来越有价值。

For students and job-seekers, this development underscores the growing importance of AI in manufacturing, suggesting that skills in AI and data analysis will be increasingly valuable in future job markets.

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