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All Journal Journal Of World Science International Journal of Multidisciplinary Sciences and Arts Enrichment: Journal of Multidisciplinary Research and Development
Lodhi, Shahrukh Khan
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Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Ai-Driven Robotics and Automation: The Evolution of Human-Machine Collaboration Lodhi, Shahrukh Khan; Zeb, Shah
Journal of World Science Vol. 4 No. 4 (2025): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v4i4.1389

Abstract

AI-driven robotics has transformed industries through enhanced automation, yet challenges like ethical dilemmas, workforce displacement, and cybersecurity gaps persist. While prior research focused on functional applications, emotional intelligence and bio-inspired designs remain underexplored. This study examines the integration of emotionally intelligent and bio-inspired robots into human-machine collaboration, evaluates ethical governance frameworks, and proposes solutions for global regulatory harmonization. A mixed-method approach was employed, combining systematic literature reviews of 72 peer-reviewed articles (2014–2024) and case studies of AI robotics in healthcare, manufacturing, and agriculture. Data were analyzed via thematic coding and SWOT analysis. Key innovations include socially intelligent robots for elderly care, BCIs for neural-controlled prosthetics, and swarm robotics for precision agriculture. Ethical challenges like bias in hiring algorithms and accountability gaps in autonomous systems were identified, necessitating transparent AI audits. The research advocates for adaptive regulatory models to balance innovation with ethical safeguards, emphasizing human-centric collaboration. It calls for international standards to address bias, cybersecurity, and liability, offering a roadmap for policymakers and industries to harness AI robotics responsibly.
AI for Predictive Maintenance: Reducing Downtime and Enhancing Efficiency Zeb, Shah; Lodhi, Shahrukh Khan
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 1 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i1.338

Abstract

The implementation of AI predictive maintenance technology by organizations results in operational alterations by providing predictive equipment data instead of traditional maintenance protocols. Artificial intelligence with machine learning technology along with IoT sensors brings organizations two distinct advantages including improved equipment prediction performance and better operations and budget management which reduces unexpected production breakdowns. Better operational performance and longer equipment durability accompany improved safety practices which the manufacturing industry alongside transportation healthcare sectors and aerospace and energy operations have noticed. The implementation of AI-based predictive maintenance meets various deployment challenges caused by initial cost expenses and contradictory data quality as well as security threats during integration of new infrastructure with existing platforms. Edge computing technology provides platforms that link digital duplicates with 5G capabilities to generate autonomous AI repair protocols. The implementation of artificial intelligence-based medical maintenance will progress from specialized practice to fundamental core industrial operations since it enhances equipment stability while decreasing operational breakdowns to achieve superior industrial outcomes in every sector.
The Role of AI in Circular Manufacturing: Towards a Zero-Waste Economy Provides its Headings Lodhi, Shahrukh Khan; Zeb, Shah
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 1 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i1.339

Abstract

The transition to a zero-waste economy necessitates innovative approaches to circular manufacturing, where Artificial Intelligence (AI) plays a pivotal role. This study examines how AI technologies—including predictive maintenance, machine learning, and blockchain—enhance resource efficiency, reduce waste, and optimize supply chains in circular manufacturing systems. Employing a qualitative methodology, the research synthesizes literature from peer-reviewed journals and industrial case studies to analyze AI's applications across product design, production, and end-of-life processing. Findings reveal that AI-driven solutions significantly improve material recovery, operational transparency, and demand forecasting, yet face hurdles such as high costs, data quality issues, and energy demands. The study proposes policy-industry collaboration and advanced technologies like digital twins to overcome these barriers. Implications suggest that AI integration not only accelerates sustainability goals but also fosters economic resilience, as evidenced by reduced emissions and extended product lifecycles. This research contributes a framework for scalable, AI-enabled circular manufacturing, addressing gaps in existing literature while highlighting future directions for innovation in sustainable industrial practices.
AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis Lodhi, Shahrukh Khan; Gill, Ahmad Yousaf; Hussain, Ibrar
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 Lodhi, Shahrukh Khan; Hussain, Hafiz Khawar; Hussain, Ibrar
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.
Co-Authors Gill, Ahmad Yousaf Hussain, Hafiz Khawar Hussain, Ibrar Zeb, Shah