Additive manufacturing, as the first fully digital manufacturing process, is critically dependent on data, including the input materials, the process parameters guiding the three-dimensional printing process execution, any postprocessing steps, and any inspections performed on the printed coupons and/or parts, to name just a few examples. This article presents the standards to enable findable, accessible, interoperable, and reusable (FAIR) data. It then discusses three main types of data models that are used to capture different levels of detail and granularity of data: conceptual, logical, and physical. Different approaches and techniques with their own strengths and weaknesses are developed to model data. Four of the major types of data models include hierarchical, relational, object-oriented, and network/graph-based. The article also presents the evolution of data management approaches. It then describes the characteristics of effective logical data models.

This article details findability, accessibility, interoperability, and reusability (FAIR) additive manufacturing data management principles and examines related motivations, benefits, and challenges. It explains opportunities to advance the state of the AM community efforts in fostering FAIR data management practices/principles and outlines the consequence of such efforts on technology maturation and industrialization for AM technologies.

This article focuses on the aspects associated with inspection related to pressure vessels and pipework. These aspects include inspection policy, inspection planning and procedures, inspection strategy, inspection methodology, preparation for inspection, invasive inspection, internal visual inspection, and non-invasive inspection. Inspection execution, risk-based inspection, competence assurance of inspection personnel, inspection coverage, inspection periodicity, inspection anomaly criteria, assessment of fitness, and reporting requirements, are also discussed. The article addresses the data acquisition, reporting and trending, and review and audit for the inspection. It reviews inspection techniques, including visual inspection, ultrasonic inspection, and radiographic inspection.

This article focuses on the methods that are being developed for detecting and monitoring corrosion: electrochemical methods, electromagnetic or sound wave methods, fiber-optic technology, fluorescence methods, and the Diffracto Sight method. It reviews the importance of data management and the Corrosion Expert System. It concludes with information on the simulation and modeling for incorporating the mechanisms of corrosion prevention into military hardware systems design and operation.

To improve the performance of additive manufacturing (AM) systems, a critical need exists for collaborative generation, acquisition, curation, and analysis of digital AM data across the product life cycle. This article discusses the history, development, and potential benefits of the AM Common Data Dictionary (AM-CDD), along with future directions.

Additive manufacturing (AM) creates parts layer by layer directly from three-dimensional computer-aided design data. This article discusses systematic ways to address the challenges in AM data integration by exploring various AM-specific data-integration scenarios that can improve the current AM ecosystem. Representative AM data sources are also described. A reference framework that captures the heterogenous AM data sources and existing data-integration mechanisms are used. General data-integration practices—based on existing manufacturing data and lab information system integration experiences—are recommended to automate AM data flow, operations, and development. Lastly, the article discusses the seven steps in the big-data-integration workflow.

In situ process monitoring includes any technologies that monitor or inspect during an additive manufacturing (AM) process. This article presents the types, process considerations, and challenges of in situ monitoring technologies that can be implemented during an AM process. The types include system health monitoring, melt pool monitoring, and layer monitoring. The article discusses data analysis, and provides an overview of the integration of sensors into AM machines.

The qualification of additive manufacturing (AM) processes and the certification of AM parts is recognized as a significant impediment to the rapid, low-cost deployment of AM manufacturing. The challenges are multifaceted; however, it is an attempt to apply conventional qualification approaches to an inherently different process that has caused the most difficulty. This article examines the conventional qualification methodology and explores how the unique characteristics of AM pose a set of qualification challenges. The extant approach to the qualification of AM processes is described, followed by a discussion on a possible future state.

External corrosion direct assessment (ECDA) is a structured process intended for use by pipeline operators to assess and manage the impact of external corrosion on the integrity of underground pipelines. This article focuses on four steps of ECDA, namely, preassessment, indirect examinations, direct examination, and post assessment. The ECDA tool selection matrix used to determine the tool choices is also presented.

This article focuses on the environmentally assisted cracking (EAC) of structural materials in boiling water reactors (BWRs), reactor pressure vessels, core internals, and ancillary piping. It discusses the effects of water chemistry on materials degradation, mitigation approaches, and their impact on aging management programs. The article reviews the effects of materials, environment, and stress factors on the cracking susceptibility of ferritic and austenitic structural alloys in BWRs. It describes the methods, such as data-based life-prediction approaches and mechanisms-informed life-prediction approaches, for predicting cracking kinetics in BWRs. The article provides information on several EAC mitigation techniques for BWR components, namely material solutions, stress solutions, and environmental solutions.

This article presents the analytics challenges in additive manufacturing. It discusses the types and applications of data analytics. Data analytics can be classified into four types: descriptive, diagnostic, predictive, and prescriptive. The diverse applications of data analytics and machine learning include design, process-structure-properties (PSP) relationships, and process monitoring and quality control. The article also presents tools used for data analytics.

The purpose of accreditation is to evaluate and assure high-quality results from suppliers of products or services without incurring the costs associated with auditing each supplier. This article describes laboratory accreditation based on the general requirements of International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) Guide 25, "General Requirements for the Competence of Calibration and Testing Laboratories." The Guide 25 is a balanced standard that addresses quality system requirements of ISO 9000 and the technical requirements needed to perform testing or calibration. The article describes an accreditation process that would enhance the international acceptance of test data for mechanical testing laboratories.

This article first describes a typical additive manufacturing (AM) process chain, which involves the transaction of digital information to manufacture physical products. The digitized nature of AM exposes the technology to increased vulnerabilities, posing a hurdle to its mass adoption. The article presents motivation for using blockchain, which is a decentralized, immutable ledger that is shared on a peer-to-peer network. The article presents the advantages of blockchain integration to AM supply chains. These involve aspects of data security, supply chain and logistics, finance, value creation, and scope expansion. The article also presents the opportunities and challenges of blockchain technology.

This article introduces the concept of condition monitoring (CM) and summarizes various techniques used for CM across the industrial sectors. The techniques include visual inspection, performance monitoring, vibration condition monitoring, vibration condition monitoring, lubricant oil analysis, acoustic emission testing, temperature monitoring, motor current signature analysis, and ultrasound emission. The article describes the evolution of condition-based maintenance in CM. It also describes the basics of integrated vehicle health management, a capability that enables a number of maintenance philosophies. The article concludes with a discussion on various condition monitoring in industrial sectors, including condition-monitoring techniques in nuclear power plants, road condition monitoring, and condition monitoring in wind turbines.

The objective of dimensional management is to create a design and process that absorbs as much variation as possible without affecting the function of the product. This article describes the steps followed by the dimensional management process. These include defining product dimensional requirements, determining process and product requirements, ensuring accurate documentation, developing a measurement plan that validates product requirements, establishing manufacturing capabilities versus design intent, and establishing production-to-design feedback loop. The article discusses the simulation model in terms of a functional feature product model, component part variation, assembly method variation, measurement schemes, and assembly sequences.

This article presents an approach to manage the uncertainty present in materials design. It describes inductive and deductive approaches to deal with uncertainty. The article focuses on providing an understanding of the opportunities for managing uncertainty and the decisions that influence the accuracy of the results. A design of experiments (DOE) represents a sequence of experiments to be performed, expressed in terms of factors set at specified levels. The article discusses the two types of DOEs: the full factorial design and the fractional factorial design. It explains the factors to be considered when selecting a procedure for propagating uncertainty. The article lists the categories of the popular types of uncertainty propagation methods, including simulation-based methods, local expansion methods, and numerical integration-based methods.