Obtaining the requisite properties in alloys is crucial problem in the production of aluminium components, requiring great deal of time and effort for investigation and experimentation. In this study, machine-learning technique utilizing Bayesian-fine tuned Adaptive Gated Recurrent Unit (B-AGRU) to forecast the mechanical characteristics of aluminium alloys is presented. Training and testing are conducted on dataset, which has undergone comprehensive preparation process that includes cleaning and Z-score normalization. Principal Component Analysis (PCA) is used for feature extraction to increase algorithmic efficiency. The GRU approach, which is implemented in Python, hardness and yield strength, leading in more accurate findings. When compared to standard methodologies, process saves significant time and energy, as evidenced by metrics such as RMSE-20%, MAE-10% and R-squared-97%. This study reveals B-AGRU-based machine learning as a feasible strategy for enhancing efficiency and sustainability in forecasting mechanical properties of aluminium alloys, paving the way for wider application in industrial sector.
{"title":"PREDICTING ALUMINA COMPOSITES’ MECHANICAL CHARACTERISTICS USING A MACHINE LEARNING APPROACH","authors":"Ashwini Kumar, Arunkumar Devalapura Thimmappa, Ritesh Kumar, Manali Gupta","doi":"10.24874/pes.si.24.02.010","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.010","url":null,"abstract":"Obtaining the requisite properties in alloys is crucial problem in the production of aluminium components, requiring great deal of time and effort for investigation and experimentation. In this study, machine-learning technique utilizing Bayesian-fine tuned Adaptive Gated Recurrent Unit (B-AGRU) to forecast the mechanical characteristics of aluminium alloys is presented. Training and testing are conducted on dataset, which has undergone comprehensive preparation process that includes cleaning and Z-score normalization. Principal Component Analysis (PCA) is used for feature extraction to increase algorithmic efficiency. The GRU approach, which is implemented in Python, hardness and yield strength, leading in more accurate findings. When compared to standard methodologies, process saves significant time and energy, as evidenced by metrics such as RMSE-20%, MAE-10% and R-squared-97%. This study reveals B-AGRU-based machine learning as a feasible strategy for enhancing efficiency and sustainability in forecasting mechanical properties of aluminium alloys, paving the way for wider application in industrial sector.","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388582","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}
Pub Date : 2024-03-20DOI: 10.24874/pes.si.24.02.018
Akhilendra Pratap Singh, Mohammad Shahid, Anupam Kumari, M. P. Karthikeyan
In the dynamic realm of manufacturing, it is essential to optimize production processes to attain efficiency and competitiveness. This study presents an innovative enhanced dragonfly optimization (EDFO) method to improve production by utilizing a diverse strategy that combines swarm intelligence and virtual cell development. The suggested methodology includes the parallel EDFO algorithm, which is a cutting-edge variety of swarm intelligence, to address the intricate optimization difficulties related to virtual cell creation. The virtual cell construction process entails the consolidation of machines into cells to optimize output and reduce manufacturing lead times. The benchmark test results offer valuable insights into the algorithm's capabilities and effectively demonstrate its effectiveness in optimizing virtual cell generation for various manufacturing conditions. The proposed approach, which simultaneously takes numerous essential characteristics, is a comprehensive solution for improving production efficiency in virtual cellular manufacturing systems due to its multifunctional nature.
{"title":"ENHANCED PRODUCTION THROUGH NOVEL SWARM-INTELLIGENT ENABLED VIRTUAL CELL FORMATION: MULTIFACETED APPROACH","authors":"Akhilendra Pratap Singh, Mohammad Shahid, Anupam Kumari, M. P. Karthikeyan","doi":"10.24874/pes.si.24.02.018","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.018","url":null,"abstract":"In the dynamic realm of manufacturing, it is essential to optimize production processes to attain efficiency and competitiveness. This study presents an innovative enhanced dragonfly optimization (EDFO) method to improve production by utilizing a diverse strategy that combines swarm intelligence and virtual cell development. The suggested methodology includes the parallel EDFO algorithm, which is a cutting-edge variety of swarm intelligence, to address the intricate optimization difficulties related to virtual cell creation. The virtual cell construction process entails the consolidation of machines into cells to optimize output and reduce manufacturing lead times. The benchmark test results offer valuable insights into the algorithm's capabilities and effectively demonstrate its effectiveness in optimizing virtual cell generation for various manufacturing conditions. The proposed approach, which simultaneously takes numerous essential characteristics, is a comprehensive solution for improving production efficiency in virtual cellular manufacturing systems due to its multifunctional nature.","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388638","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}
Elena Posnaya, Ekaterina Krichevets, Artyom Shevtsov, Ivan Glazunov
{"title":"DEVELOPMENT OF PROMISING INDUSTRY BASED ON SUSTAINABLE ENTREPRENEURSHIP THROUGH IMPROVING QUALITY OF MANAGEMENT","authors":"Elena Posnaya, Ekaterina Krichevets, Artyom Shevtsov, Ivan Glazunov","doi":"10.24874/pes06.01.003","DOIUrl":"https://doi.org/10.24874/pes06.01.003","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388666","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}
The article is devoted to the study of the prospects for increasing the financing of technologies and innovations through the development of remote digital investment transactions in Russia. Based on the best international experience of countries from the IMD Digital Competitiveness Ranking for 2022, using the method of regression analysis, econometric modelling of the significance of the risk of loss of an investor and the legal protection of remote digital investment transactions for financing technology and innovation was carried out. As a result, it was proved that the risk of investor losses and the legal protection of remote digital investment transactions determine the amount of technology and innovation financing. The development of remote-digital investment transactions in Russia opens up broad prospects for increasing funding for technology and innovation. For this purpose, author's recommendations are proposed. The originality of the article is that it revealed a new look at the financing of technologies and innovations - from the standpoint of remote digital investment transactions.
{"title":"THE IMPORTANCE OF INVESTOR LOSS RISK AND LEGAL PROTECTION OF REMOTE DIGITAL INVESTMENT TRANSACTIONS FOR FINANCING TECHNOLOGY, INNOVATION AND TELECOMMUNICATIONS","authors":"Olesya P. Kazachenok","doi":"10.24874/pes06.01.004","DOIUrl":"https://doi.org/10.24874/pes06.01.004","url":null,"abstract":"The article is devoted to the study of the prospects for increasing the financing of technologies and innovations through the development of remote digital investment transactions in Russia. Based on the best international experience of countries from the IMD Digital Competitiveness Ranking for 2022, using the method of regression analysis, econometric modelling of the significance of the risk of loss of an investor and the legal protection of remote digital investment transactions for financing technology and innovation was carried out. As a result, it was proved that the risk of investor losses and the legal protection of remote digital investment transactions determine the amount of technology and innovation financing. The development of remote-digital investment transactions in Russia opens up broad prospects for increasing funding for technology and innovation. For this purpose, author's recommendations are proposed. The originality of the article is that it revealed a new look at the financing of technologies and innovations - from the standpoint of remote digital investment transactions.","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140389054","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}
Pub Date : 2024-03-20DOI: 10.24874/pes.si.24.02.004
K. R. Priya Dharshini, R. Kalaivani
{"title":"IMPLEMENTING CHANNEL ESTIMATION AND MODULATION TECHNIQUES USING MIMO-PSK","authors":"K. R. Priya Dharshini, R. Kalaivani","doi":"10.24874/pes.si.24.02.004","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.004","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388362","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}
The increased durability and performance features of polymer concrete panels have led to their widespread application in construction. The manufacturing of precise and effective techniques for identifying forms and flaws is vital to guarantee the high quality of these panels. To increase the accuracy of structure and defect-recognition in polymer concrete panels, this study presents a new Stochastic raven roosting optimization enhanced artificial neural network (SRRO-EANN) forecasting technique. The data sample used to assess the completed model fits the training dataset is referred to the test dataset. The Gaussian filter (GF) is a tool used in the pre-processing and Principal Component Analysis (PCA) feature extraction, leading to more effective utilization and understanding the defect capturing. The findings of the research indicate the effectiveness for the future development of forecasting technologies in the realm of quality control and building material inspection.
{"title":"ENHANCING A NOVEL NEURAL NETWORK ALGORITHM FOR FORECASTING THE IDENTIFICATION OF SHAPES AND DEFECTS IN POLYMER CONCRETE PANELS","authors":"Vinod Mansiram Kapse, Arun Kumar Marandi, Beemkumar Nagappan, Ankita Agarwal","doi":"10.24874/pes.si.24.02.013","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.013","url":null,"abstract":"The increased durability and performance features of polymer concrete panels have led to their widespread application in construction. The manufacturing of precise and effective techniques for identifying forms and flaws is vital to guarantee the high quality of these panels. To increase the accuracy of structure and defect-recognition in polymer concrete panels, this study presents a new Stochastic raven roosting optimization enhanced artificial neural network (SRRO-EANN) forecasting technique. The data sample used to assess the completed model fits the training dataset is referred to the test dataset. The Gaussian filter (GF) is a tool used in the pre-processing and Principal Component Analysis (PCA) feature extraction, leading to more effective utilization and understanding the defect capturing. The findings of the research indicate the effectiveness for the future development of forecasting technologies in the realm of quality control and building material inspection.","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388758","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}
Monaem Elmnifi, Ahmed Nassar Mansur, Ayad K. Hassan, Atheer Raheem Abdullah, Sadoon K. Ayed, Hasan Shakir Majdi, Laith Jaafer Habeeb
{"title":"EXPERIMENTAL AND SIMULATION STUDY FOR IMPROVING THE SOLAR CELL EFFICIENCY BY USING ALUMINUM HEAT SINKS","authors":"Monaem Elmnifi, Ahmed Nassar Mansur, Ayad K. Hassan, Atheer Raheem Abdullah, Sadoon K. Ayed, Hasan Shakir Majdi, Laith Jaafer Habeeb","doi":"10.24874/pes06.01.018","DOIUrl":"https://doi.org/10.24874/pes06.01.018","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388937","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":"OPTIMIZATION OF PERFORMANCE AND EMISSION CHARACTERISTICS OF GASOLINE-ALCOHOL-BASED NANOFUELS USING TEACHING LEARNING-BASED OPTIMIZATION METHOD FOR SUSTAINABLE FUTURE","authors":"Samhita Priyadarsini Gundala, Vinay Kumar Domakonda","doi":"10.24874/pes06.01.011","DOIUrl":"https://doi.org/10.24874/pes06.01.011","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388466","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}
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