Research on Business Model Optimization of Artificial Intelligence Garbage Classification

In recent years,garbage classification and environmental protection are gradually becoming an important step in the construction of ecological civilization in China.However,the popularity and commercial value of the application of artificial intelligence trash cans in Beijing are not high at present.This article analyzes these problems one by one and propose solutions,hoping that the commercial value of artificial intelligence trash cans can be optimized and improved and to make the city greener.This paper uses the questionnaire method and the literature method to research and analyze the optimization of the business model of artificial intelligence in garbage classification.

Prediction and analysis of process failures by ANN classification during wire-EDM of Inconel 718

Wire breakages and spark absence are two typical machining failures that occur during wire electric discharge machining(wire-EDM),if appropriate parameter settings are not maintained.Even after several attempts to optimize the process,machining failures cannot be eliminated completely.A n offline classification model is presented herein to predict machining failures.The aim of the current study is to develop a multiclass classification model using an artificial neural network(ANN).The training dataset comprises 81 full factorial experiments with three levels of pulse-on time,pulse-off time,servo voltage,and wire feed rate as input parameters.The classes are labeled as normal machining,spark absence,and wire breakage.The model accuracy is tested by conducting 20 confirmation experiments,and the model is discovered to be 95%accurate in classifying the machining outcomes.The effects of process parameters on the process failures are discussed and analyzed.A microstructural analysis of the machined surface and worn wire surface is conducted.The developed model proved to be an easy and fast solution for verifying and eliminating process failures.