Purpose

Inconsistencies in build quality part-to-part and build-to-build continue to be a problem in additive manufacturing (AM). The flexibility of AM often enables low-volume and custom production, making conventional methods of machine qualification and health monitoring challenging to implement. Machine health has been difficult to separate from the effects of design and process decisions, and therefore inferring machine health through part quality has been similarly complicated.

Design/methodology/approach

This conceptual paper proposes a framework for monitoring machine health by monitoring two types of witness parts, in the form of witness builds and witness artifacts, to provide sources of data for potential indicators of machine health.

Findings

The proposed conceptual framework with witness builds and witness artifacts permits the implementation into AM techniques to monitor machine health according to part quality. Subsequently, probabilistic models can be used to optimize machine costs and repairs, as opposed to statistical approaches that are less ideal for AM. Bayesian networks, hidden Markov models and Markov decision processes may be well-suited to accomplishing this task.

Originality/value

Though variations of witness builds have been created for use in AM to measure build quality and machine capabilities, the literature contains no previously proposed framework that permits the evaluation of machine health and its influence on quality through a combination of witness builds and witness artifacts, both of which can be easily added into AM production.

中文翻译：

---

增材制造中见证构建和见证工件的概念框架

目的

在增材制造 (AM) 中，零件到零件和构建到构建质量的不一致仍然是一个问题。AM 的灵活性通常支持小批量和定制生产，这使得传统的机器鉴定和健康监测方法难以实施。机器健康状况很难与设计和流程决策的影响分开，因此通过零件质量推断机器健康状况同样复杂。

设计/方法/方法

这篇概念性论文提出了一个通过监视两种类型的见证部分（以见证构建和见证工件的形式）来监控机器健康状况的框架，以为机器健康的潜在指标提供数据源。

发现

带有见证构建和见证工件的拟议概念框架允许在 AM 技术中实施，以根据零件质量监控机器健康状况。随后，概率模型可用于优化机器成本和维修，而不是对 AM 不太理想的统计方法。贝叶斯网络、隐马尔可夫模型和马尔可夫决策过程可能非常适合完成这项任务。

原创性/价值

尽管已经创建了见证构建的变体，用于在 AM 中测量构建质量和机器能力，但文献中没有包含先前提出的框架，允许通过见证构建和见证工件的组合来评估机器健康状况及其对质量的影响，两者都是其中可以很容易地添加到 AM 生产中。