A standards-based approach for linking as-planned to as-fabricated product data.

The digital thread links disparate systems across the product lifecycle to support data curation and information cultivation and enable data-driven applications, e.g., digital twin. Realizing the digital thread requires the integration of semantically-rich, open standards to facilitate the dynamic creation of context based on multiple viewpoints. This research develops such an approach to link as-planned (ISO 6983) to as-fabricated (MTConnect) product data using dynamic time warping. Applying this approach to a production part enabled the designer to make a more optimal decision from the perspective of the product lifecycle that would have otherwise been challenging to identify.

Contextualising manufacturing data for lifecycle decision-making.

Recent advances enable data from manufacturing systems to be captured and contextualised relative to other phases of the product lifecycle, a necessary step toward understanding system behaviour and satisfying traceability requirements. Significant challenges remain for integrating information across the lifecycle and enabling efficient decision-making. In this paper, we explore opportunities for mapping standard data representations, such as the Standard for the Exchange of Product Data (STEP), MTConnect, and the Quality Information Framework (QIF) to integrate information silos existing across the lifecycle. To demonstrate this vision, we describe a reference implementation with a contract manufacturer in the National Institute of Standards and Technology (NIST) Smart Manufacturing Systems Test Bed. Using this implementation, we explore how knowledge generated from manufacturing can support lifecycle decision-making. As a case study, we then present an interactive prototype correlating the test bed's data based on the context that must be provided for a specific decision-making viewpoint.

Towards a generalized energy prediction model for machine tools.

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Towards a Lifecycle Information Framework and Technology in Manufacturing.

Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, industry has been challenged by the fact that the context in which data is used varies based on the function / role in the product lifecycle that is interacting with the data. Holistically, the data across the product lifecycle must be considered an unstructured data-set because multiple data repositories and domain-specific schema exist in each phase of the lifecycle. This paper explores a concept called the Lifecycle Information Framework and Technology (LIFT). LIFT is a conceptual framework for lifecycle information management and the integration of emerging and existing technologies, which together form the basis of a research agenda for dynamic information modeling in support of digital-data curation and reuse in manufacturing. This paper provides a discussion of the existing technologies and activities that the LIFT concept leverages. Also, the paper describes the motivation for applying such work to the domain of manufacturing. Then, the LIFT concept is discussed in detail, while underlying technologies are further examined and a use case is detailed. Lastly, potential impacts are explored.

Framework for a Digital Twin in Manufacturing: Scope and Requirements.

Digital twin has the potential to be an important concept for achieving smart manufacturing. However, there remains a lot of confusion about the concept and how it can be implemented in real manufacturing systems, especially among small-to-medium-sized enterprises. This paper synthesizes the different perspectives that have been reported on the digital twin to identify the key characteristics that must be understood when developing a digital twin for a specific use case. Example applications are provided and the need for a standardized framework, such as the one under development as ISO 23247 (Digital Twin Manufacturing Framework), is motivated. This framework can enable context-dependent implementations and promote composability and reusability of digital twin components.

A review of diagnostic and prognostic capabilities and best practices for manufacturing.

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

Reference architecture to integrate heterogeneous manufacturing systems for the digital thread.

The increasing growth of digital technologies in manufacturing has provided industry with opportunities to improve its productivity and operations. One such opportunity is the digital thread, which links product lifecycle systems so that shared data may be used to improve design and manufacturing processes. The development of the digital thread has been challenged by the inherent difficulty of aggregating and applying context to data from heterogeneous systems across the product lifecycle. This paper presents a reference four-tiered architecture designed to manage the data generated by manufacturing systems for the digital thread. The architecture provides segregated access to internal and external clients, which protects intellectual property and other sensitive information, and enables the fusion of manufacturing and other product lifecycle data. We have implemented the architecture with a contract manufacturer and used it to generate knowledge and identify performance improvement opportunities that would otherwise be unobservable to a manufacturing decision maker.

A Classification Scheme for Smart Manufacturing Systems' Performance Metrics.

This paper proposes a classification scheme for performance metrics for smart manufacturing systems. The discussion focuses on three such metrics: agility, asset utilization, and sustainability. For each of these metrics, we discuss classification themes, which we then use to develop a generalized classification scheme. In addition to the themes, we discuss a conceptual model that may form the basis for the information necessary for performance evaluations. Finally, we present future challenges in developing robust, performance-measurement systems for real-time, data-intensive enterprises.

The Current State of Sensing, Health Management, and Control for Small-To-Medium-Sized Manufacturers.

The development of digital technologies for manufacturing has been challenged by the difficulty of navigating the breadth of new technologies available to industry. This difficulty is compounded by technologies developed without a good understanding of the capabilities and limitations of the manufacturing environment, especially within small-to-medium enterprises (SMEs). This paper describes industrial case studies conducted to identify the needs, priorities, and constraints of manufacturing SMEs in the areas of performance measurement, condition monitoring, diagnosis, and prognosis. These case studies focused on contract and original equipment manufacturers with less than 500 employees from several industrial sectors. Solution and equipment providers and National Institute of Standards and Technology (NIST) Hollings Manufacturing Extension Partnership (MEP) centers were also included. Each case study involved discussions with key shop-floor personnel as well as site visits with some participants. The case studies highlight SME's strong need for access to appropriate data to better understand and plan manufacturing operations. They also help define industrially-relevant use cases in several areas of manufacturing operations, including scheduling support, maintenance planning, resource budgeting, and workforce augmentation.

ENABLING SMART MANUFACTURING TECHNOLOGIES FOR DECISION-MAKING SUPPORT.

Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation needed to continue adoption and use of smart manufacturing technologies.

Enabling Smart Manufacturing Research and Development using a Product Lifecycle Test Bed.

Smart manufacturing technologies require a cyber-physical infrastructure to collect and analyze data and information across the manufacturing enterprise. This paper describes a concept for a product lifecycle test bed built on a cyber-physical infrastructure that enables smart manufacturing research and development. The test bed consists of a Computer-Aided Technologies (CAx) Lab and a Manufacturing Lab that interface through the product model creating a "digital thread" of information across the product lifecycle. The proposed structure and architecture of the test bed is presented, which highlights the challenges and requirements of implementing a cyber-physical infrastructure for manufacturing. The novel integration of systems across the product lifecycle also helps identify the technologies and standards needed to enable interoperability between design, fabrication, and inspection. Potential research opportunities enabled by the test bed are also discussed, such as providing publicly accessible CAx and manufacturing reference data, virtual factory data, and a representative industrial environment for creating, prototyping, and validating smart manufacturing technologies.

IDENTIFYING PERFORMANCE ASSURANCE CHALLENGES FOR SMART MANUFACTURING.

Smart manufacturing has the potential to address many of the challenges faced by industry. However, the manufacturing community often needs assistance to leverage available technologies to improve their systems. To assure the performance of these technologies, this paper proposes a shared knowledge base that collects problem areas, solutions, and best practices for manufacturing technology. An Implementation Risk Assessment Framework (IRAF) is also described to identify the primary weaknesses of technologies in specific manufacturing contexts. Such approaches have the potential to stimulate new ideas and drive standardization activities critical to scale up and deploy smart manufacturing technologies successfully and quickly.

Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop.

The National Institute of Standards and Technology (NIST) hosted the Roadmapping Workshop - Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) in Fall 2014 to discuss the needs and priorities of stakeholders in the PHM4SMS technology area. The workshop brought together over 70 members of the PHM community. The attendees included representatives from small, medium, and large manufacturers; technology developers and integrators; academic researchers; government organizations; trade associations; and standards bodies. The attendees discussed the current and anticipated measurement science challenges to advance PHM methods and techniques for smart manufacturing systems; the associated research and development needed to implement condition monitoring, diagnostic, and prognostic technologies within manufacturing environments; and the priorities to meet the needs of PHM in manufacturing. This paper will summarize the key findings of this workshop, and present some of the critical measurement science challenges and corresponding roadmaps, i.e., suggested courses of action, to advance PHM for manufacturing. Milestones and targeted capabilities will be presented for each roadmap across three areas: PHM Manufacturing Process Techniques; PHM Performance Assessment; and PHM Infrastructure - Hardware, Software, and Integration. An analysis of these roadmaps and crosscutting themes seen across the breakout sessions is also discussed.