  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.
