Journal of advanced manufacturing and processing最新文献

{"title":"Engineering a Ceramic Piston Pump to Minimize Particle Formation for a Therapeutic Immunoglobulin: a combined factorial and modelling approach","authors":"Kirk Roffi, I. Sebastião, Alexandre Morel","doi":"10.1002/amp2.10142","DOIUrl":"https://doi.org/10.1002/amp2.10142","url":null,"abstract":"","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47911423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simulation-based integrated virtual testbed for dynamic optimization in smart manufacturing systems","authors":"Yuting Sun,&nbsp;Jiachen Tu,&nbsp;Mikhail Bragin,&nbsp;Liang Zhang","doi":"10.1002/amp2.10141","DOIUrl":"10.1002/amp2.10141","url":null,"abstract":"<p>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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45277476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization for cost-efficient and resilient machining under tool wear","authors":"James P. Wilson,&nbsp;Zongyuan Shen,&nbsp;Utsav Awasthi,&nbsp;George M. Bollas,&nbsp;Shalabh Gupta","doi":"10.1002/amp2.10140","DOIUrl":"10.1002/amp2.10140","url":null,"abstract":"<p>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 <math>\u0000 <mrow>\u0000 <mn>15</mn>\u0000 <mo>%</mo>\u0000 </mrow></math> as compared to the default control scheme.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49112394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Additively manufactured cryogenic microchannel distillation device for air separation","authors":"Danny R. Bottenus,&nbsp;Paul H. Humble,&nbsp;Russell Burnett,&nbsp;Warren Harper,&nbsp;Tim Veldman,&nbsp;Michael R. Powell,&nbsp;John A. Barclay,&nbsp;James Ely","doi":"10.1002/amp2.10139","DOIUrl":"10.1002/amp2.10139","url":null,"abstract":"<p>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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44893834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a supervised machine-learning model capable of distinguishing fiber orientation of polymer composite samples nondestructively tested using active ultrasonics","authors":"Austin D. Bedrosian,&nbsp;Michael R. Thompson,&nbsp;Andrew Hrymak,&nbsp;Gisela Lanza","doi":"10.1002/amp2.10138","DOIUrl":"10.1002/amp2.10138","url":null,"abstract":"<p>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.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43286743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln","authors":"Asem M. Ali,&nbsp;Juan David Tabares,&nbsp;Mark W. McGinley","doi":"10.1002/amp2.10137","DOIUrl":"10.1002/amp2.10137","url":null,"abstract":"<p>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","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46174051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling energy-efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno-economic analysis and advanced process modeling","authors":"Chaitanya Sampat,&nbsp;Lalith Kotamarthy,&nbsp;Pooja Bhalode,&nbsp;Yingjie Chen,&nbsp;Ashley Dan,&nbsp;Sania Parvani,&nbsp;Zeal Dholakia,&nbsp;Ravendra Singh,&nbsp;Benjamin J. Glasser,&nbsp;Marianthi Ierapetritou,&nbsp;Rohit Ramachandran","doi":"10.1002/amp2.10136","DOIUrl":"10.1002/amp2.10136","url":null,"abstract":"<p>The global pharmaceutical industry is a trillion-dollar market. However, the pharmaceutical sector often lags in manufacturing innovation and automation which limits its potential to maximize energy efficiency. The integration of techno-economic analysis (TEA) with advanced process models as part of an overarching smart manufacturing platform, can help industries create business models, which can be adapted for manufacturing to reduce energy consumption and operating costs while ensuring product quality which can further enable a more sustainable process operation. In this study, a rational design of experiment on three unit-operations (wet granulation, drying, and milling) was performed on a batch (case 1) and continuous (case 2) pharmaceutical process to obtain experimental data. Process models for predicting product quality and energy efficiency of each of the three-unit operations were developed. The experimental data were used to validate the models and good agreement was observed. The energy consumption of each unit operation was calculated using statistical models relating the power consumption and the process parameters. The developed process models and energy models were further integrated into a TEA framework, which quantified the energy and monetary cost of manufacturing for both batch and continuous manufacturing cases. With this integrated framework, energy costs savings of ~33% was obtained in the continuous manufacturing process (case 2) over the batch process (case 1).</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42556521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When is “Net Zero net zero?”","authors":"Matthew J. Realff","doi":"10.1002/amp2.10135","DOIUrl":"10.1002/amp2.10135","url":null,"abstract":"","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47030904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental validation of multiphysics model simulations of the thermal response of a cement clinker rotary kiln at laboratory scale","authors":"Juan David Tabares,&nbsp;William M. McGinley,&nbsp;Thad L. Druffel,&nbsp;Bhagyashri Aditya Bhagwat","doi":"10.1002/amp2.10134","DOIUrl":"10.1002/amp2.10134","url":null,"abstract":"<p>An increasing demand for buildings, transportation systems and civil infrastructure development has driven expansion of cement consumption world-wide, producing a significant increase in related global energy demand. With approximately 7% of the world-wide industrial energy consumption (10.7 exajoules [EJ]), the cement industry is the third most energy intensive industrial processes and a key component for concrete, the most consumed composite material in the global construction industry. In cement manufacturing, the cement kiln accounts for most of the energy consumption in the production process. As the heart of a cement plant, the cement kiln is where the kiln feed primarily containing calcium oxide (CaO), silica (SiO₂), alumina (Al₂O₃), and iron (Fe₂O₃) are thermally and chemically transformed into clinker minerals. The presented work developed a multiphysics model, designed and built a laboratory-scale rotary cement clinker kiln, and produced cement clinker at laboratory-scale. The model was developed to study the interaction between the various thermal, fluid dynamic and chemical interactions involved in the sintering process used to form Portland cement clinker in an effort to reduce energy use. The analytical model was validated through experimental testing using a unique laboratory-scale rotary cement kiln developed during the investigation. Also demonstrated was the feasibility of producing clinker at laboratory scale. This modeling and lab scale tests were designed to better understand the clinker sintering process so that operational and quality decisions can be made to optimize energy consumption without compromising cement clinker quality. The computational fluid dynamics modeling was developed in COMSOL Multiphysics 6.0. The characteristics of the combustion fluid flow, concentration of species, temperature and heat transfer were studied for a turbulent flow of methane (CH₄) gas and oxygen (O₂). Theory suggests that heat transfer impacts the cement production process but the multiphysics model more accurately describes the convection, conduction, and radiant heat transfer in the kilning process and thus allows for a better understanding of the energy exchange driving the chemical reactions that produce Portland cement. Clinker minerals were formed because of appropriate burning conditions implemented during experimental model validation.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43269585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Getting to net zero through extended producer responsibility","authors":"P. Hugh Helferty","doi":"10.1002/amp2.10132","DOIUrl":"10.1002/amp2.10132","url":null,"abstract":"<p>The application of Extended Producer Responsibility, including for greenhouse gases, to manufacturing broadly would go a long way toward enabling society to meet its net zero goals. Within the oil and gas industry, this could be achieved by phasing-in a Carbon Takeback Obligation. American leadership in applying Extended Producer Responsibility to include greenhouse gases could both reduce U.S. emissions and help drive other countries to do so.</p>","PeriodicalId":87290,"journal":{"name":"Journal of advanced manufacturing and processing","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aiche.onlinelibrary.wiley.com/doi/epdf/10.1002/amp2.10132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48749734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}