Journal of advanced manufacturing and processing最新文献

CO₂ methanation, which converts CO₂ and hydrogen into methane as fuel, is one of the promising candidates for the development of CO₂ utilization technologies. Recently, a highly active catalyst made of Ni/ZrO₂ for methanation has been developed, and is currently investigated as a potential use in a high-performance reactor. However, design of reactor must be carried out carefully, since this reaction is highly exothermic, which may cause reactor runaway and deterioration of catalysts. For this problem, a mathematical model that can predict the behavior inside the reactor is necessary. In this work, we consider the methanation reaction of CO₂ in a reactor model and estimate the kinetic parameters in the reaction rate model from experimental data. In the parameter estimation using literature values and Tikhonov regularization, eight kinetic parameters in the rate equations were identified from 64 data points with a wide range of conditions. We confirm that molar fractions at the reactor exit predicted by this reactor model are in good agreement with the experimental results. Furthermore, the developed model was validated to predict the compositions and temperature that were not used in the estimation. We expect the developed model will be a powerful tool for the reactor design.

Operating windows of conventional tray columns may not be large enough to ensure a sufficient separation with the current or future challenges of a volatile energy and raw material supply. Therefore, an innovative, segmented separation tray has been developed, which enlarges the operation window and thus leads to a higher flexibility, managing the challenges of higher volatility in downstream processes. In this contribution, a hydrodynamic characterization and the resulting lower operation limits of this segmented tray are presented. Furthermore, an approach to obtain shorter start-up times for the column and a resulting faster response time to changes in the process are presented. The feasibility of this type of operation is evaluated by the investigation of the stability of the tray under new operation conditions.

Estimation of tool wear in precision machining is vital in the traditional subtractive machining industry to reduce processing cost, improve manufacturing efficiency and product quality. In this vein, fusion of time and frequency-domain features of commonly sensed signals can provide an early indication of tool wear and improve its prediction accuracy for prognostics and health management. This paper presents a data-driven methodology and a complete tool chain for the inference of precision machining tool wear from fused machine measurements, such as cutting force, power, audio and vibration signals, and quantify the usefulness of each measurement. Indicators of tool wear are extracted from time-domain signal statistics, frequency-domain analysis, and time-frequency domain analysis. Correlation coefficients between the extracted features (indicators) and the tool wear are used to select the most informative features. Principal Component Analysis and Partial Least-Squares are used to reduce the dimensionality of the feature space. Regression models, including linear regression, support vector regression, Decision tree regression, neural network regression and Gaussian process regression, are used to predict the tool wear using data from a Haas milling machine performing spiral boss face milling. The performance of the regression models based on subsets of sensors validates the preliminary estimates about the saliency of the sensors. The experimental results show that the proposed methods can predict the machine tool wear precisely, with readily available sensor measurements. Neural network and Gaussian process regression were able to achieve good estimates of tool wear at different machine operating conditions. The most informative signal in predicting tool wear was shown to be the vibration signal. Time-frequency domain features were the most informative features among the combination of features of three domains. In addition, using partial least squares components extracted from the original features of signals led to higher prediction accuracy.

During fill-finish manufacturing, protein-pump surface interactions can induce subvisible particle (SVP) formation which poses a risk to drug product quality and patient safety. Despite this risk, there have been no concerted efforts to understand the effects of piston pump design on SVP formation. We've systematically varied the design of the piston-cylinder interface to minimize SVP formation for a therapeutic immunoglobulin. The clearance factor, surface roughness factor, and their combined interaction significantly affected particle concentrations, quantitated by light obscuration and microflow imaging. Optimized pump designs reduced particle levels by 1–2 orders of magnitude compared to the off-the-shelf equipment. At the piston surface, scanning electron microscopy revealed evidence of protein film abrasion, a process which ejects SVPs from the piston-cylinder interface as wear debris. Computational fluid dynamics and quartz crystal microbalance were applied to simulate fluid flow and protein adsorption phenomena in the pump respectively. The risk of protein film abrasion was modeled along a hypothetical Stribeck curve, thereby interconnecting design parameters, lubrication conditions, and SVP formation. Our findings support implementation of a modular pump platform with interchangeable pistons; this approach would enable the pump design to be customized based on each protein's propensity to form SVPs. This flexible approach can benefit pharmaceutical manufacturers and patients alike by accelerating tech transfer and improving process control.

In a manufacturing system, production control-related decision-making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation-based virtual testbed that implements dynamic optimization, automatic information exchange, and decision-making from the process-level, system-level, and factory-level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python-based numerical computation program, discrete-event simulation software (Simul8), and an optimization solver (CPLEX) via a third-party master program. The application of this simulation-based virtual testbed is illustrated by a case study in a machining shop.

With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost-efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi-objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi-objective formulation. The optimization framework is tested on a high-fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to $��15�\%�$ as compared to the default control scheme.

The efficiency of air separation is tested using three different small-scale cryogenic distillation columns. The performance of a random packed column is compared to the performance of two microchannel distillation (MCD) columns that use thin wicking structures and gas flow channels to achieve process intensification. The MCD columns tested include a plate-type layered column and an additively manufactured porous honeycomb (AMPH) column. For columns with 25.4 cm of active height and run under similar conditions, the packed, plate-type layering, and AMPH columns achieved approximate height equivalent of a theoretical plate (HETP) values of 5.5, 3.7, and 3.2 cm for nitrogen, and 5.9, 4.9, and 3.3 cm for argon. The AMPH column can produce up to 0.4 SLM of more than 90% purity oxygen with 12 W of cooling lift. These results demonstrate the feasibility of using additive manufacturing to construct MCD devices and pave a way for constructing novel MCD designs.

This study evaluated the paired performance of different signal processing techniques and supervised learning models being capable of identifying subtle differences in otherwise similar acoustic signals related to detecting the fiber orientation of a polymer composite. Projection of Latent Structures models demonstrated poor predictive capabilities of the composite structure based on spectral analysis of the acoustic signal. AI based models showed great improvements to the capabilities, with artificial neural network modeling exceeding Convolutional Neural Networks for correct classification accuracies. The continuous wavelet transfer highlighted the greatest degree of differences in the signal response compared with fast Fourier Transformation or short time Fourier transformation. The use of regression-based predictions over classification-based was found to greatly improve the predictive capabilities of the models, especially when multiple fiber orientations were present in a sample. A time-based analysis of spectral data showed the frequencies of the signal changed based on the orientation of the fibers. The acoustic signals for the samples with multiple fiber orientations contained individual artifacts representing components of each individual orientation. Use of the frequency domain was shown as capable of observing the targeted fiber information within the bulk material in real-time. This work shows great promise for composite material predictions using active ultrasonics, with the potential to be implemented into in-line systems.

Cement manufacturing is energy-intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry-based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in-light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel-type-independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data-based simulations were conducted to quantitatively evaluate the proposed model and the 5-fold cross-validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical oper