Industrial Artificial Intelligence (IAI), or Artificial Intelligence applied to industry applications, is defined by its requirement to fulfill an explicit need of a system, while both utilizing and being bounded by the limitations and capabilities of that system. Performance and evaluations of an IAI have no meaning outside the context of its impact on a system and users.
The Industrial AI Management and Metrology (IAIMM) project develops and deploys measurement science to advance adoption and management of Artificial Intelligence (AI) systems in industrial environments to improve the productivity, resiliency, security, and the sustainability of manufacturing operations and supply chains. The educational barrier to entry of many IAI systems paired with a lack of standard evaluation tools and management methods has led to hesitation, mistrust, and misapplication of IAI systems in manufacturing enterprises. Industry requires trusted tools that can be used to readily assess the value of an IAI system within its application domain and enterprise. This is a particular issue to Subject Matter Experts (SMEs) who do not have resources necessary to independently develop these assessments.
Our project objective is to:
Industrial AI uses Physics, Data Insights, and Human Observations + Intuition to Create Actionable Intelligence for Informed Decision Support
IAIMM has identified IAI systems for decision making, planning, and control in manufacturing as a prime candidate for better Standard Operating Procedures (SOPs) centered on both use and evaluation. The specific use case of multi-stage manufacturing presents a broad scope of application to accentuate and explore various processes, policies, and pain points within the US manufacturing industry. Many of these challenges center around simulation, collection, and interchange of related connected and disparate data from both equipment and operators.
Exploring and enhancing IAI use in connected multi-stage process use cases can be a direct path to improve the productivity, resiliency, security, and sustainability of manufacturing operations and enterprises across the supply chain. Special interest will be placed upon IAI connected to smart manufacturing processes using Industrial Internet of Things (IIOT) for information collection and communication.
Three Major Efforts
Purpose and Goals
This project seeks to provide standard best practice tools and procedures for evaluating, using, and setting expectations for IAI systems to foster appropriate levels of trust in IAI systems. This work entails exploration of data used by IAI, the methods of application, and the relevant domains and stakeholders affected by IAI use. We seek to lower barriers of understanding and interrogation such that users and decision makers of all levels can determine the potential risks and likely impacts of an IAI tool or solution. Identified metrics and methods of evaluation will provide decision makers clear justification for investing in and maintaining IAI tools. Evaluations will focus on systems through enterprise-level impacts that can be intuitively translated to both floor-level operators and management decision makers.
As the manufacturing industry produces increasing volumes of diverse data, stakeholders need robust evaluation methods to assess the impacts of IAIs and their ability to realize value from this data. The diverse breadth of manufacturing process information includes but is not limited to: equipment data, design data, execution data, part quality data, systems interaction data, human generated feedback data, process performance data, and more.
Advanced manufacturing seeks to close the gap between human operators and IAIs that feed off the growing connectivity provided by IIoT technologies. Clear communication of IAI performance expectations as well as of internal logic and reasoning of these systems to human users is key for building appropriate levels of trust. The ability of systems to augment and enhance human performance is directly related to the system’s ability to communicate to the user. Additionally, the value that system provides must be effectively measured and communicated to decision makers. Towards this end, this research seeks identification and development of simulation environments and evaluation tools suitable to IAI and manufacturing specifically.
IAIMM will develop test methods, standards, toolkits, models, datasets, industry pilots, and build communities of interest to advance manufacturing management of IAI systems with a focus on data use, interpretability, interoperability, and system level impact. IAIMM outputs will lower the barrier to incorporate new technologies and analysis methods into existing operations. The outcomes of the IAIMM program will enable trusted, understandable, and reproducible analysis workflows across engineered products, manufacturing processes, production systems, enterprises, and supply chains to improve decision making.
Relevant Publications: