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The number of published manufacturing science digital articles available from scientifc journals and the broader web have exponentially increased every year since the 1990s. To assimilate all of this knowledge by a novice engineer or an experienced researcher, requires signifcant synthesis of the existing knowledge space contained within published material, to fnd answers to basic and complex queries. Algorithmic approaches through machine learning and specifcally Natural Language Processing (NLP) on a domain specifc area such as manufacturing, is lacking. One of the signifcant challenges to analyzing manufacturing vocabulary is the lack of a named entity recognition model that enables algorithms to classify the manufacturing corpus of words under various manufacturing semantic categories. This work presents a supervised machine learning approach to categorize unstructured text from 500K+manufacturing science related scientifc abstracts and labelling them under various manufacturing topic categories. A neural network model using a bidirectional long-short term memory, plus a conditional random feld (BiLSTM+CRF) is trained to extract information from manufacturing science abstracts. Our classifer achieves an overall accuracy (f1-score) of 88%, which is quite near to the state-of-the-art performance. Two use case examples are presented that demonstrate the value of the developed NER model as a Technical Language Processing (TLP) workfow on manufacturing science documents. The long term goal is to extract valuable knowledge regarding the connections and relationships between key manufacturing concepts/entities available within millions of manufacturing documents into a structured labeled-property graph data structure that allow for programmatic query and retrieval. 

Suppliers registered within a manufacturing-as-a-service (MaaS) marketplace require near real time decision making to accept or reject orders received on the platform. Myopic decision-making such as a first come, first serve method in this dynamic and stochastic environment can lead to suboptimal revenue generation. In this paper, this sequential decision making problem is formulated as a Markov Decision Process and solved using deep reinforcement learning (DRL). Empirical simulations demonstrate that DRL has considerably better performance compared to four baselines. This early work demonstrates a learning approach for near real-time decision making for suppliers participating in a MaaS marketplace. 

Uncertainty in manufacturing networks has created barriers to closing the gap between design enterprises and the American industrial base. Uncertainty arises from the lack of transparent access to manufacturer capabilities, the inability to auto-discover service providers who are best capable for a given job request, and the dependence on human word-of-mouth trust network relationships that exist in the manufacturing supply chain. This uncertainty slows down the pace of product development lifecycles from a viewpoint of inefficient forms of supplier assessment, vetting, selection, and compliance, leading to a trust tax tacked onto the final price of products. In times of global crisis such as the coronavirus disease pandemic, this uncertainty also leads to inefficient forms of gathering information on manufacturing capability, available capacity, and registered licenses and assessing compliance. This technical note outlines solution pathways that can help ease the search and discovery process of connecting clients and manufacturing service providers through digitally enabled technologies.