Deviations from the approved design or processes during mass production can lead to unforeseen risks. However, these changes are sometimes necessary due to changes in the product design characteristics or an adaptation in the manufacturing process. A major challenge is to identify these risks early in the workflow so that failures leading to warranty claims can be avoided. We developed Fountain as a contextual assistant integrated in the deviation management workflow that helps in identifying the risks based on the description of the existing design and process criteria and the proposed deviation. In the manufacturing context, it is important that the assistant provides recommendations that are explainable and consistent. We achieve this through a combination of the following two components 1) language models finetuned for domain specific semantic similarity and, 2) knowledge representation in the form of a property graph derived from the bill of materials, Failure Modes and Effect Analysis (FMEA) and prior failures reported by customers. Here, we present the nuances of selecting and adapting pretrained language models for an engineering domain, continuous model updates based on user interaction with the contextual assistant and creating the causal chain for explainable recommendations based on the knowledge representation. Additionally, we demonstrate that the model adaptation is feasible using moderate computational infrastructure already available to most engineering teams in manufacturing organizations and inference can be performed on standard CPU only instances for integration with existing applications making these methods easily deployable.

翻译：批量生产中偏离批准的设计或工艺流程可能引发不可预见的风险。然而，因产品设计特性变更或制造工艺调整，此类变更加之必要。核心挑战在于工作流中尽早识别这些风险，以避免导致保修索赔的故障。我们开发了Fountain作为集成于变更管理流程的情境助手，基于现有设计准则、工艺准则及拟议变更的描述辅助风险识别。在制造场景中，助手需提供可解释且一致的建议。我们通过以下两个组件的融合实现这一目标：1) 针对领域语义相似度微调的语言模型；2) 知识表征——由物料清单、失效模式与影响分析（FMEA）及客户既往故障报告导出的属性图。本文阐述了为工程领域选择并适配预训练语言模型的要义，基于用户与情境助手交互的持续模型更新策略，以及基于知识表征构建可解释建议因果链的方法。此外，我们验证了该模型适配方法可在制造企业工程团队常用的中等计算基础设施上实现，且推理过程仅需标准CPU实例即可完成，便于与现有应用集成，使这些方法易于部署。