| dc.description.abstract | The machine yield rate is generally derived from the number of good products output from the machine divided by the number of products input to the machine. But if it is due to the production costs, the inspection station cannot be set after every machine in the production line. Because we cannot get the number of products input to the machine and the number of good products output from the machine, we cannot know the yield rate of each machine. The ceramic substrates are easy to crack, but the inspection stations usually ignore the products with micro-crack, so we cannot know which machine makes the product crack. Therefore, the cracked defects cost much on the ceramic substrate production line, but the complicated production line also makes the calculation of the machine yield rate difficult. Based on the above problems, if we know the machine yield rate of each machine in the production line, we can know which machines make the product crack.
In previous research, there has been an approach that can overcome the above problems. The approach uses the EM algorithm to estimate the past machine yield rate from the finished production data. However, machines may be abnormal after we start to use them, and the abnormal machines will produce more defective products. If we only know the past machine yield rate, we can only know the machines have been abnormal or not from their historical machine yield rate, and we cannot know the machine will be abnormal or not in the future. But if we know the future machine yield rate, we can know which machines will be abnormal and produce a lot of defective products, and then we can save the costs in advance.
Aiming at the above issues, this research proposes a future machine yield rate forecasting approach based on the time series forecasting, using the past machine yield rate to forecast the future machine yield rate. And we forecast the future machine yield rate for each period based on Online Learning. We compare the forecasting results with the real production data. The average error of the weekly forecasting model is 2.85%, and the average error of the daily forecasting model is 2.76%. Besides, the future machine yield rate forecasting approach can be applied to the machine maintenance early warning. The weekly forecasting model is estimated to save 13% of micro-crack defects per week on average. The daily forecasting model is estimated to save 17% of micro-crack defects per day on average. Reducing the micro-crack defects can save production costs. | en_US |