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Advanced manufacturing is challenging engineering perceptions of how to innovate and compete. The need for manufacturers to rapidly respond to changing requirements and demands; obtain, store, and interpret large volumes of data and information; and positively impact society and our environment requires engineers to investigate and develop new ways of making products for flexible and competitive production. In addition to the associated operational, technological, and strategic advantages for industry, advanced manufacturing creates educational, workforce, and market opportunities. Thus, this literature review is aimed at investigating the current state and emerging trends in advanced manufacturing. Specifically, this study addresses advances in manufacturing process technologies, focusing on shaping processes (mass reducing, mass conserving, and joining) as well as non-shaping processes (heat treatment and surface finishing), and metal-based additive manufacturing. This literature review finds myriad efforts have been undertaken by researchers in industry, academia, and government labs from around the world, which have supported the development and implementation of new process technologies to improve manufacturing systems extending from unit process and shop floor operations to facility and supply chain management activities. However, as evidenced by recent and emerging global challenges in energy, critical materials, and health care, the manufacturing industry must continue the innovative development of advanced materials, manufacturing processes, and systems that ensure cost-efficient, rapidly flexible, high-quality, and responsible production of goods and services. [ABSTRACT FROM AUTHOR]

Advanced manufacturing is challenging engineering perceptions of how to innovate and compete. The need for manufacturers to rapidly respond to changing requirements and demands; obtain, store, and interpret large volumes of data and information; and positively impact society and our environment requires engineers to investigate and develop new ways of making products for flexible and competitive production. In addition to the associated operational, technological, and strategic advantages for industry, advanced manufacturing creates educational, workforce, and market opportunities. Thus, this literature review aims to investigate the current state and emerging trends in advanced manufacturing. Specifically, this study addresses advances in manufacturing from manufacturing systems perspective, concentrating on emerging trends in process sensing and monitoring, equipment control and automation, machine tools, sustainable manufacturing, and green supply chain management. This review finds myriad efforts have been undertaken by researchers in industry, academia, and government labs from around the world, which have supported the development and implementation of new process technologies to improve manufacturing systems extending from unit process and shop floor operations to facility and supply chain management activities. However, emerging global challenges remain in various domains including energy (e.g., resource scarcities and global warming), critical materials vulnerable to supply disruptions due to crisis and rapid changes in demand, and services (e.g., healthcare supply chains during COVID-19 pandemic). Thus, manufacturing industry must continue the innovative development of advanced materials, manufacturing processes, and systems that ensure cost efficient, rapidly flexible, high quality, and responsible production of goods and services. [ABSTRACT FROM AUTHOR]

Power metallurgy hot isostatic pressing (PM-HIP), as a versatile manufacturing process, can produce net-shape or near-net-shape components with complicated geometries from materials that are not easily cast, deformed, or welded. In PM-HIP, capsule (or die) filling is a critical step to get dimensionally and microstructurally sound outputs. Particularly, capsule filling controls the initial relative density (homogeneity) of the PM-HIP compact. In this study, the pre-consolidation capsule filling process is simulated by the discrete element method (DEM), to capture the impact of vibration parameters, including the vibration frequency, amplitude, duration, and direction, on the initial relative density (RD). The output of the DEM model was imported into a user subroutine-based finite element of PM-HIP containing a combined constitutive model of compressive and consolidative mechanical behavior of powder. The simulation model was used to quantitatively study the relationships between the vibration parameters and the initial RD of the product. The ultimate results of this work have shown an optimal vibration frequency for maximizing the initial density. The study also quantitatively tested the relationship between the other vibration parameters and the initial relative density and provided the analysis. [ABSTRACT FROM AUTHOR]

The incremental sheet forming process has gained interest from industrial and research institutions due to its intrinsic advantages, such as process flexibility and a low unit processing cost. However, achieving vertical features in the process is not possible in a single pass due to thinning and tearing of the sheet at the transition zone between vertical and horizontal features, e.g., the wall and base of a part. This limitation has been overcome by using multistage single point incremental forming (MSPIF), which introduces several intermediate tool-pass stages during the process. MSPIF has been shown to mitigate sheet thinning even when the maximum wall angle is reached. Prior research has reported promising results for aluminum alloys, low- and medium-carbon steel alloys, and formable stainless steels. There is a paucity of reported research that explores the suitability of MSPIF for less formable metals such as high-carbon steels, alloy steels, and titanium alloys. Work herein reports a series of experimental studies examining the feasibility of MSPIF for forming high-carbon steel sheets to achieve steep wall angles (70°). The effect of varying tool step down, tool path, and MSPIF strategies on process performance is studied. Selected process performance measures include surface roughness and geometrical conformance. Only one of the six strategies explored reached the maximum slope angle required (70°) for both spiral and contoured tool path movement techniques and for both tool vertical step-down values. While increasing vertical step-down from 0.5 mm/rev to 1.5 mm/rev increases surface roughness, it is found that varying tool path movement does not have a measurable effect. For the geometrical conformance, the overall profile of the formed part is the closest to the nominal profile when vertical stepdown is 1.5 mm/rev and contoured tool path movements are used. [ABSTRACT FROM AUTHOR]

Power metallurgy hot isostatic pressing (PM-HIP) is a manufacturing technique capable of producing net shape or near-net shape components with complicated geometries from materials that are difficult to melt and cast, mechanically deform or weld. However, the process and soundness of the outcome are extremely sensitive to the geometric design of the capsule (also known as the die or can) that is used in the process. The capsule design for each new component involves several trial–error iterations to achieve the desired geometry and shape of the component. For each iteration, costly HIP experiments need to be conducted and new capsules need be manufactured with small modifications. In this study, a robust finite element analysis (FEA) model of the HIP process is developed, then wrapped in a multi-objective genetic algorithm (MOGA) optimization framework to obtain the optimal pre-HIP capsule design, which yields the desired post-HIP component geometry in one HIP run. The FEA-based optimization algorithm is validated by HIP experiments, showing excellent agreement between the experiment and the model. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic model based approach for machinery condition prognosis based on particle filter by integrating physical knowledge with in-process measurements into a state space framework to account for uncertainty and nonlinearity in machinery degradation process. One limitation of conventional particle filter is that condition prognosis is performed based on the model with predetermined parameters obtained from simulation studies or lab-controlled tests. Due to the stochastic nature of machinery defect propagation under varying operating conditions, model parameters may vary in practice which causes prediction errors. To address it, an integrated state prediction and parameter estimation framework based on particle filter and expectation-maximization algorithm is formulated and investigated. The model parameters are adaptively estimated based on expectation-maximization algorithm utilizing hidden degradation state and available in-process measurements. Particle filter is then performed on the identified model with estimated parameters following Bayesian inference scheme to improve the robustness and accuracy of machinery condition prognosis. The effectiveness of the developed method is demonstrated through a simulation study and an experimental run-to-failure bearing test in a wind turbine. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the feasibility, acceptability, and validity of a radio‐frequency identification (RFID)‐based system to measure gait speed in a clinical setting as a first step to using unobtrusive gait speed assessment in routine clinical care. Design: Feasibility study comparing gait speed assessed using an RFID‐based system with gait speed assessed using handheld stopwatch, the criterion standard. Setting: Outpatient geriatrics clinic at a Connecticut‐based academic medical center. Participants: Clinic attendees who could walk independently with or without an assistive device (N=50) and healthcare providers (N=9). Measurements: Gait speed was measured in twice using 2 methods each time before participants entered an examination room. Participants walked at their usual pace while gait speed was recorded simultaneously using the RFID‐based system and a handheld stopwatch operated by a trained study investigator. After 2 trials, participants completed a brief survey regarding their experience. At the end of the study period, clinic healthcare providers completed a separate survey. Results: Test‐retest reliability of the RFID‐based system was high (intraclass correlation coefficient = 0.953). The mean difference ± standard deviation in gait speed between the RFID‐based system and the stopwatch was –0.003±0.035 m/s (p=.53) and did not differ significantly according to age, sex, or use of an assistive walking aid. Acceptability of the device was high, and 8 of 9 providers indicated that measuring gait speed using the RFID‐based system should be a part of routine clinical care. Conclusion: RFID technology may offer a practical means of overcoming barriers to routine measurement of gait speed in real‐world outpatient clinical settings. [ABSTRACT FROM AUTHOR]

Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs) to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis. [ABSTRACT FROM AUTHOR]

This paper presents the design, characterization, and experimental evaluation of a wireless capacitive pressure sensor embedded within a rotating workroll of a microrolling machine for monitoring the production of micrometer-scaled texture on thin metallic workpiece. The sensor converts the spatial and temporal variations of pressure across the interface between the roll and workpiece into capacitance values, thereby experimentally establishing a quantitative correlation between the online measurement and the geometric features being formed on the workpiece, in real time. Analytical and numerical models have been developed to guide the design of the sensor to maximize the capacitance output while satisfying the space constraint. A sensitivity matrix linking the measured capacitance with pressure distribution has been established through numerical analysis. Simulation and experiments confirmed the effectiveness of the embedded sensor in enabling intelligent microrolling. [ABSTRACT FROM PUBLISHER]

A multivariate sensor is described for intelligent polymer processing that incorporates a piezoelectric ring to acquire melt pressure as well as a thermopile to acquire melt temperature and mold temperature. The mechatronic system analyzes the process states according to mechanistic relations to estimate the melt velocity and melt viscosity. Validation experiments are implemented to characterize the sensor's performance against an array of commercial sensors. Models of product quality with the described sensor far outperform those based on data from commercial sensors. While system identification of the transient thermopile voltage indicates an underdamped response that limited the model fidelity, the existing capability suggests a new standard for mold design that incorporates multivariate sensors into the runner system to provide a consistent set of physical states for process tuning irrespective of molding machine manufacturer and model. [ABSTRACT FROM PUBLISHER]

Improving process observability is of high relevancy to improved manufacturing process control. This paper describes a novel measurement technique using acoustic waves for the transmission of multiple physical parameters from within the cavity of an injection mold to a receiver outside: temperature, pressure, velocity, and viscosity of polymer melt. Such a technique is a key to improved monitoring and control of plastic injection molding. A coded-acoustic wave modulation scheme enables multiparameter transmission through an acoustic transmitter with variable gains. This enables selective resonant frequencies that provide the carriers for the individual parameters to be transmitted, while suppressing noise induced in the modulation process. The presented acoustic wireless sensing method is applicable to a wide range of process monitoring scenarios. [ABSTRACT FROM PUBLISHER]

This paper presents a new sensing method to improve the efficiency of electrical capacitance tomography. Instead of applying one excitation signal to one electrode at a time, the multiple excitation capacitance polling (MECaP) method progressively applies an increasing number of multiple excitations to multiple electrodes and simultaneously measures the capacitance values, thereby significantly increasing the image scanning speed. The performance of a MECaP-based sensor system is numerically simulated and analyzed using the finite element method. Experimental evaluation of the numerical results demonstrates the effectiveness and efficiency of the new sensing technique and its applicability to a broad range of commercial and industrial applications where permittivity determination through capacitance measurement provides an effective means for noninvasive dynamic processes monitoring in an enclosed environment. [ABSTRACT FROM AUTHOR]