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AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis Shahrukh Khan Lodhi; Ahmad Yousaf Gill; Ibrar Hussain
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.4616

Abstract

The industrial sector is undergoing a transformation thanks to artificial intelligence (AI), which is bringing revolutionary changes to a number of areas like robots and automation, supply chain efficiency, predictive maintenance, and quality control and assurance. This thorough analysis investigates AI's significant influence on contemporary manufacturing procedures. Artificial Intelligence (AI) improves machine capabilities in robotics and automation, creating more intelligent and flexible systems. Robots can now complete complicated tasks with more flexibility and precision thanks to AI-driven developments, which boosts manufacturing efficiency and human-robot cooperation. Another crucial area where AI has a big impact is predictive maintenance. With the use of machine learning algorithms and real-time data analysis, artificial intelligence (AI) helps manufacturers anticipate equipment faults before they happen. By taking a proactive stance, unplanned downtime is decreased, resource usage is optimized, and machinery longevity is increased. AI has a significant positive impact on quality assurance and control because to cutting-edge technologies like data analytics and computer vision. Artificial intelligence (AI) solutions facilitate predictive quality management, improve fault identification, and offer real-time monitoring. Higher quality standards, less waste, and more customer happiness are the outcomes of this. Artificial Intelligence (AI) tackles issues related to supplier performance, accurate forecasting, and inventory management in supply chain optimization. Automation and analytics powered by AI simplify supply chain processes, increase transparency, and facilitate improved decision-making, which lowers costs and increases flexibility. All things considered, integrating AI into manufacturing processes offers a strategic advantage by promoting increased accuracy, flexibility, and efficiency. The continued developments in AI technology have the potential to significantly influence how manufacturing develops in the future by creating new avenues for creativity and excellence in the sector.
Using AI to Increase Heat Exchanger Efficiency: An Extensive Analysis of Innovations and Uses Shahrukh Khan Lodhi; Hafiz Khawar Hussain; Ibrar Hussain
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.4617

Abstract

Artificial intelligence (AI) has made significant strides toward cost reduction and performance optimization in heat exchanger technologies. Artificial intelligence (AI) methods in machine learning, deep learning, and expert systems provide significant advancements in diagnostics, performance optimization, and predictive maintenance. While deep learning is superior at recognizing intricate patterns, machine learning offers flexibility through data analysis. Expert systems use domain expertise to make decisions, although they might not be as flexible as data-driven methods. Hybrid approaches integrate these strategies to improve flexibility and performance. New developments include smart heat exchangers with IoT capabilities for real-time monitoring, compact designs for a variety of applications, and new materials and coatings that improve durability and efficiency. Reducing environmental effect is also reflected in sustainable solutions like waste heat recovery. Nevertheless, issues like computing costs, data quality, and interaction with current systems still need to be resolved. Optimized computational methodologies, modular integration, and sophisticated sensor technology are required to address these problems. AI has the power to completely transform heat exchanger technology by enhancing sustainability and efficiency. Future breakthroughs will be fueled by ongoing improvements in materials, designs, and AI approaches, offering more complex solutions to satisfy changing environmental and performance requirements.