Abstract. The intersection of artificial intelligence (AI) and sustainable manufacturing offers an innovative possibility to decrease industrial waste and, at the same time, improve the production yield. The current paper suggests a new multi-layer AI architecture including a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) neural network and a Multi-Objective Genetic Algorithm (MOGA) optimizer to control real-time process in green manufacturing setting. The architecture acts on the heterogeneous sensor streams, energy metering information, and process logs in a five-stage pipeline including data acquisition, preprocessing, AI inference, decision support and closed-loop feedback. Two industrial datasets (n = 18,400 samples and n = 14,200 samples) of semiconductor fabrication and automotive body stamping data (20192023) are experimentally validated. The proposed system attains a waste reduction of 34.7, a yield improvement of 17.1 and prediction RMSE of 1.72 as compared to the traditional machine learning baselines. The explainability analysis using SHAP reveals that the dominant process variables are the coolant temperature, spindle speed and feed rate. The findings affirm that the proposed hybrid AI architecture is more effective than the current approaches and provides a deployable and scalable system towards smart green manufacturing.
Irshadullah Asim Mohammed· Materials Research Proceedin...· 0 citations
The use of AI is transforming supply chain management, moving from a reactive to a proactive, adaptive, and data-driven approach. This research explores the impact of AI in the supply chain on enhancing operational efficiency and fostering sustainability in today's business landscape. The focus of the research is in the application of machine learning, predictive analysis, smart automation, Internet of Things (IoT) connected systems, demand forecasting, inventory optimization, route planning and real-time tracking of the supply chain network. AI can help with more accurate demand forecasting, curbing overstocking, transportation inefficiencies, optimizing resource usage and enhancing responsiveness to market changes. Concurrently, AI-powered supply chain systems can play a role in sustainability by minimizing material waste, optimizing energy usage, curbing unnecessary transportation, and promoting smart utilization of natural resources. The benefits of AI are also reliant on the quality of data, technology, organization, employee skills, cybersecurity, and responsible data management practices. Part particular special attention is given to the connection between technical efficiency and environmental concern – excessive dependence on computational technologies can create energy and resource problems. The paper, therefore, argues that AI is a strategic enabler, rather than a solution on its own to the problem of supply chain. A combination of intelligent technologies and sustainable business practices adds to the resilience of supply chains, improves economic outcomes and helps an organisation better meet evolving customer and environmental needs. In conclusion, the study highlights the potential of AI to revolutionize supply chain management practices, enabling businesses to build efficient, sustainable, resilient, and transparent supply chains.
Irshadullah Asim Mohammed, Sunakshi Verma, J. Srinivasan et al.· International journal of com...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
