Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete
In today’s industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/28 |
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| author | Javier Ferreiro-Cabello Francisco Javier Martinez-de-Pison Esteban Fraile-Garcia Alpha Pernia-Espinoza Jose Divasón |
| author_facet | Javier Ferreiro-Cabello Francisco Javier Martinez-de-Pison Esteban Fraile-Garcia Alpha Pernia-Espinoza Jose Divasón |
| author_sort | Javier Ferreiro-Cabello |
| collection | DOAJ |
| description | In today’s industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed to optimize the RSW process for steel reinforcement bars. By creating robust machine learning models trained on limited datasets, the system generates interactive heat maps that provide real-time guidance to production engineers or intelligent systems, enabling dynamic adaptation to changing conditions and external factors such as fluctuating energy costs. These heat maps serve as a flexible and intuitive tool for identifying robust operational points that balance quality, energy efficiency, and productivity. The proposed methodology advances decision-making in welding processes by combining robust predictive modeling with innovative visualization techniques, offering a versatile solution for multiobjective optimization in real-world industrial applications. |
| format | Article |
| id | doaj-art-b9fa20dec85144b7b5b941d67c978152 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b9fa20dec85144b7b5b941d67c9781522025-08-20T02:47:06ZengMDPI AGSensors1424-82202024-12-012512810.3390/s25010028Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for ConcreteJavier Ferreiro-Cabello0Francisco Javier Martinez-de-Pison1Esteban Fraile-Garcia2Alpha Pernia-Espinoza3Jose Divasón4SCoDIP Group, Department of Mechanical Engineering, University of La Rioja, 26006 Logroño, SpainSCoTIC, Scientific Computation & Technological Innovation Center, University of La Rioja, 26006 Logroño, SpainSCoDIP Group, Department of Mechanical Engineering, University of La Rioja, 26006 Logroño, SpainSCoTIC, Scientific Computation & Technological Innovation Center, University of La Rioja, 26006 Logroño, SpainPSYCOTRIP Group, Department of Mathematics and Computation, University of La Rioja, 26006 Logroño, SpainIn today’s industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed to optimize the RSW process for steel reinforcement bars. By creating robust machine learning models trained on limited datasets, the system generates interactive heat maps that provide real-time guidance to production engineers or intelligent systems, enabling dynamic adaptation to changing conditions and external factors such as fluctuating energy costs. These heat maps serve as a flexible and intuitive tool for identifying robust operational points that balance quality, energy efficiency, and productivity. The proposed methodology advances decision-making in welding processes by combining robust predictive modeling with innovative visualization techniques, offering a versatile solution for multiobjective optimization in real-world industrial applications.https://www.mdpi.com/1424-8220/25/1/28energy-sustainable decisionresistance spot weldingintelligent weldingmachine learningartificial intelligence (AI) |
| spellingShingle | Javier Ferreiro-Cabello Francisco Javier Martinez-de-Pison Esteban Fraile-Garcia Alpha Pernia-Espinoza Jose Divasón Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete Sensors energy-sustainable decision resistance spot welding intelligent welding machine learning artificial intelligence (AI) |
| title | Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete |
| title_full | Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete |
| title_fullStr | Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete |
| title_full_unstemmed | Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete |
| title_short | Intelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete |
| title_sort | intelligent sensor software for robust and energy sustainable decision making in welding of steel reinforcement for concrete |
| topic | energy-sustainable decision resistance spot welding intelligent welding machine learning artificial intelligence (AI) |
| url | https://www.mdpi.com/1424-8220/25/1/28 |
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