Industrial artificial intelligence (IAI) and other analysis tools with obfuscated internal processes are growing in capability and ubiquity within industrial settings. Decision-makers share their concern regarding the objective evaluation of such tools and their impacts at the system level, facility level, and beyond. One application where this style of tool is making a significant impact is in Condition Monitoring Systems (CMSs). This paper addresses the need to evaluate CMSs, a collection of software and devices that alert users to changing conditions within assets or systems of a facility. The presented evaluation procedure uses CMSs as a case study for a broader philosophy evaluating the impacts of IAI tools. CMSs can provide value to a system by forewarning faults, defects, or other unwanted events. However, evaluating CMS value through scenarios that did not occur is rarely easy or intuitive. Further complicating this evaluation are the ongoing investment costs and risks posed by the CMS from imperfect monitoring. To overcome this, an industrial facility needs to regularly and objectively review CMS impacts to justify investments and maintain competitive advantage. This paper's procedure assesses the suitability of a CMS for a system in terms of risk and investment analysis. This risk-based approach uses the changes in the likelihood of good and bad events to quantify CMS value without making any one-time point-wise estimates. Fictional case studies presented in this paper illustrate the procedure and demonstrate its usefulness and validity.

Manufacturing processes have become increasingly sophisticated leading to greater usage of robotics. Sustaining successful manufacturing robotic operations requires a strategic maintenance program. Without careful planning, maintenance can be very costly. To reduce maintenance costs, manufacturers are exploring how they can assess the health of their robot workcell operations to enhance their maintenance strategies. Effective health assessment relies upon capturing appropriate data and generating intelligence from the workcell. Multiple data streams relevant to a robot workcell may be available including robot controller data, a supervisory programmable logic controller data, maintenance logs, process and part quality data, and equipment and process fault and failure data. These data streams can be extremely informative, yet the massive volume and complexity of this data can be overwhelming, confusing, and sometimes paralyzing. Researchers at the National Institute of Standards and Technology have developed a test method and companion sensor to assess the health of robot workcells which will yield an additional and unique data stream. The intent is that this data stream can either serve as a surrogate for larger data volumes to reduce the data collection and analysis burden on the manufacturer, or add more intelligence to assessing robot workcell health. This article presents the most recent effort focused on verifying the companion sensor. Results of the verification test process are discussed along with preliminary results of the sensor's performance during verification testing. Lessons learned indicate that the test process can be an effective means of quantifying the sensor's measurement capability particularly after test process anomalies are addressed in future efforts.

Distributed production paradigms have grown in discrete manufacturing as discrete products are increasingly made by global, distributed networks. Challenges faced by discrete manufacturing, such as increased globalization, market volatility, workforce shortages, and mass personalization have necessitated scalable solutions that improve the agility of production systems. These challenges have driven the need for better collaboration and coordination in production via improved integration of production systems across the product lifecycle. This paper describes key industry use cases to motivate the research and development needed for distributed production in discrete manufacturing. The technological challenges that have hindered distributed production in discrete manufacturing are presented as is a state-of-the-art review of the standards and technologies that have been developed to overcome these challenges. Based on this review, future research directions are described to address the needs of industry and achieve the goals of distributed production in discrete manufacturing.

X-ray computed tomography (XCT) is a promising nondestructive evaluation technique for additive manufacturing (AM) parts with complex shapes. Industrial XCT scanning is a relatively new development, and XCT has several acquisition parameters that a user can change for a scan whose effects are not fully understood. An artifact incorporating simulated defects of different sizes was produced using laser powder bed fusion (LPBF) AM. The influence of six XCT acquisition parameters was investigated experimentally based on a fractional factorial designed experiment. Twenty experimental runs were performed. The noise level of the XCT images was affected by the acquisition parameters, and the importance of the acquisition parameters was ranked. The measurement results were further analyzed to understand the probability of detection (POD) of the simulated defects. The POD determination process is detailed, including estimation of the POD confidence limit curve using a bootstrap method. The results are interpreted in the context of the AM process and XCT acquisition parameters.

Recent efforts in Smart Manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications and computational platforms, along with statistical methods to collect and analyze real-time performance data. However, how do you effectively select where and when to implement these technology solutions within manufacturing operations? Furthermore, how do you account for the human-driven activities in manufacturing when inserting new technologies? Due to a reliance on human problem solving skills, today's maintenance operations are largely manual processes without wide-spread automation. The current state-of-the-art maintenance management systems and out-of-the-box solutions do not directly provide necessary synergy between human and technology, and many paradigms ultimately keep the human and digital knowledge systems separate. Decision makers are using one or the other on a case-by-case basis, causing both human and machine to cannibalize each other's function, leaving both disadvantaged despite ultimately having common goals. A new paradigm can be achieved through a hybridized systems approach - where human intelligence is effectively augmented with sensing technology and decision support tools, including analytics, diagnostics, or prognostic tools. While these tools promise more efficient, cost-effective maintenance decisions, and improved system productivity, their use is hindered when it is unclear what core organizational or cultural problems they are being implemented to solve. To explicitly frame our discussion about implementation of new technologies in maintenance management around these problems, we adopt well established error mitigation frameworks from human factors experts - who have promoted human-systems integration for decades - to maintenance in manufacturing. Our resulting tiered mitigation strategy guides where and how to insert SM technologies into a human-dominated maintenance management process.

Injection molding of plastic optical lenses prevails over many other techniques in both efficiency and cost, however polymer shrinkage during cooling, high level of uneven residual stresses and refractive index variations have limited its potential use for high precision lenses fabrication. In this research, we adopted a newly-developed strong graphene network to both plain and convex fused silica mold surfaces and proposed a novel injection molding of plano-concave lenses with graphene coated fused silica molds. The unique combination of the graphene coating and fused silica substrate maximize the mechanical properties of the mold and coating materials, namely high hardness, low surface friction, and high heat preservation effect during cooling since fused silica has low thermal conductivity. This advanced injection molding process was implemented in molding of plano-concave lenses resulting in reduced polymer shrinkage. In addition, internal residual stresses, and refractive index variations were also analyzed and discussed in detail. Meanwhile, as a comparison of conventional injection mold material, aluminum mold inserts with the same shape and size were also diamond machined and then employed to mold the same plano-concave lenses. Finally, a simulation model using Moldex3D was utilized to interpret stress distributions of both graphene and aluminum molds and then validated by experiments. The comparison between graphene and aluminum molds reveals that the novel injection molding with carbide-bonded graphene coated fused silica mold inserts is capable of molding high quality optical lenses with much less shrinkage and residual stresses, but more uniform refractive index distribution.

The production stage of a product's life cycle can significantly contribute to its overall environmental impact. Estimates of environmental impact for a product are typically produced using Life Cycle Assessment (LCA) methods. These methods rely on Life Cycle Inventory (LCI) data containing impact estimates of manufacturing processes and other operations that contribute to a product's creation. The accuracy of LCI data is critical for quality assessments; however, this data is often insufficient in the types and varieties of manufacturing processes covered and is often only a coarse estimate of actual impacts. At the same time, much manufacturing research focuses on how to model, measure, assess, and reduce the environmental impacts of manufacturing processes. Recent standards emerging from ASTM International define a structured format for presenting these studies in a reusable way. In this paper, we investigate the potential for using the ASTM E3012-16 format to generate LCI datasets suitable to perform LCA by mapping from the ASTM standard into the widely-adopted ecoSpold2 format. A process is presented for generating LCI datasets from ASTM models, and overlaps and gaps between the two standards are identified.

The manufacturing industry is evolving and starting to use 3D models as the central knowledge artifact for product data and product definition, or what is known as Model-based Definition (MBD). The Model-based Enterprise (MBE) uses MBD as a way to transition away from using traditional paper-based drawings and documentation. As MBD grows in popularity, it is imperative to understand what information is needed in the transition from drawings to models so that models represent all the relevant information needed for processes to continue efficiently. Finding this information can help define what data is common amongst different models in different stages of the lifecycle, which could help establish a Common Information Model. The Common Information Model is a source that contains common information from domain specific elements amongst different aspects of the lifecycle. To help establish this Common Information Model, information about how models are used in industry within different workflows needs to be understood. To retrieve this information, a survey mechanism was administered to industry professionals from various sectors. Based on the results of the survey a Common Information Model could not be established. However, the results gave great insight that will help in further investigation of the Common Information Model.

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.

Powder bed metal additive manufacturing (AM) utilizes a high-energy heat source scanning at the surface of a powder layer in a predefined area to be melted and solidified to fabricate parts layer by layer. It is known that powder bed metal AM is primarily a thermal process, and further, heat conduction is the dominant heat transfer mode in the process. Hence, understanding the powder bed thermal conductivity is crucial to process temperature predictions, because powder thermal conductivity could be substantially different from its solid counterpart. On the other hand, measuring the powder thermal conductivity is a challenging task. The objective of this study is to investigate the powder thermal conductivity using a method that combines a thermal diffusivity measurement technique and a numerical heat transfer model. In the experimental aspect, disk-shaped samples, with powder inside, made by a laser powder bed fusion (LPBF) system, are measured using a laser flash system to obtain the thermal diffusivity and the normalized temperature history during testing. In parallel, a finite element (FE) model is developed to simulate the transient heat transfer of the laser flash process. The numerical model was first validated using reference material testing. Then, the model is extended to incorporate powder enclosed in an LPBF sample with thermal properties to be determined using an inverse method to approximate the simulation results to the thermal data from the experiments. In order to include the powder particles' contribution in the measurement, an improved model geometry, which improves the contact condition between powder particles and the sample solid shell, has been tested. A multipoint optimization inverse heat transfer method is used to calculate the powder thermal conductivity. From this study, the thermal conductivity of a nickel alloy 625 powder in powder bed conditions is estimated to be 1.01 W/m K at 500°C. [DOI: 10.1115/1.4040877].

Design for Environment (DfE) principles are helpful for integrating manufacturing-specific environmental sustainability considerations into product and process design. However, such principles are often overly general, static, and disconnected from production contexts. This paper proposes a visual analytics-based framework for generating DfE principles that are contextualized to specific production setups. These principles are generated through interactive visual exploration of design and process parameters as well as manufacturing process performance metrics corresponding to the production setup. We also develop a formal schema for aiding storage, updating, and reuse of the generated DfE principles. In this schema, each DfE principle is associated with corresponding product lifecycle data and the evidence that led to the generation of that principle. We demonstrate the proposed visual analytics framework using data from an industry-led experiment that compared dry ice based and oil based milling for a specific production setup.

This paper presents a comprehensive review on the sources of model inaccuracy and parameter uncertainty in metal laser powder bed fusion (L-PBF) process. Metal additive manufacturing (AM) involves multiple physical phenomena and parameters that potentially affect the quality of the final part. To capture the dynamics and complexity of heat and phase transformations that exist in the metal L-PBF process, computational models and simulations ranging from low to high fidelity have been developed. Since it is difficult to incorporate all the physical phenomena encountered in the L-PBF process, computational models rely on assumptions that may neglect or simplify some physics of the process. Modeling assumptions and uncertainty play significant role in the predictive accuracy of such L-PBF models. In this study, sources of modeling inaccuracy at different stages of the process from powder bed formation to melting and solidification are reviewed. The sources of parameter uncertainty related to material properties and process parameters are also reviewed. The aim of this review is to support the development of an approach to quantify these sources of uncertainty in L-PBF models in the future. The quantification of uncertainty sources is necessary for understanding the tradeoffs in model fidelity and guiding the selection of a model suitable for its intended purpose.