EXPLAINABLE MACHINE LEARNING FRAMEWORK FOR SUSTAINABLE ENERGY OPTIMIZATION IN INDUSTRIAL 4.0 SMART MANUFACTURING SYSTEM

Abstract

In recent years, energy optimization has come to prominence as a key field for solving issues such as excessive use of industrial energy, inefficiencies in industry operations, environmental pollution, and emissions of greenhouse gases. Indus try 4.0 has enabled the revolution of manufacturing systems by incorporating various technologies such as AI, IoT, cloud computing, cyber-physical systems, and smart automation. Even though there have been numerous improvements in industrial automation, smart manufacturing systems in modern industries still incur high energy consumption costs. This poses sustainability problems within the industries. Conventional machine learning techniques employed for energy optimization in industrial applications are often operated as black boxes, posing issues with transparency and industry trust. An Explanable Machine Learning Framework for Sustainable Energy Optimization of Industry 4.0 Smart Manu facturing Systems through Application of Smart Manufacturing Energy Metrics Dataset is proposed. This research will employ advanced machine learning tech niques along with Explainable Artificial Intelligence (XAI) approaches such as SHAP and LIME. The proposed framework uses Random Forest, XGBoost, Support Vector Re gression (SVR), and LSTM algorithms for forecasting energy consumption and optimal planning of production processes. Both SHAP and LIME techniques have been used for explaining feature importance in order to enhance trust in AI-based decision-making by the industry. Experiments have shown significantly better re sults than state-of-the-art IoT-based frameworks, where the energy savings are around 34%, machine downtime reductions are 43%, and increased resource uti lization is 38%. The proposed framework aims at contributing to the development of sustainability in manufacturing, the application of Explainable AI technologies, and intelligent energy management in Industry 4.0.