Artificial intelligence approaches for waste minimization and yield optimization in green manufacturing systems
TL;DR
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.
Abstract
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.