Abstract Although die-casting is one of the most popular mass production processes of precise metal parts, the manufacturing environment of the die-casting factory remains at the traditional level. In this study, we developed three core technologies to realize a smart-factory platform for die-casting industry: 1) a novel cost-effective product-tracking technology to obtain high-quality process data providing individual product information, 2) an advanced process data acquisition system that considers process failure, and 3) a fault detection module based on an artificial neural network. Our newly developed systems for the die-casting process were verified using 1500 test production. Based on the pilot production data, we developed a fault detection module with the pre-processing of time series temperature and pressure measurement data. The developed fault detection module shows 96.9% accuracy for untrained data. The technologies developed in this study are expected to be a promising smart-factory platform to reduce the defect rate and production cost in die-casting industry.

中文翻译：

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压铸行业从传统工厂向智能工厂的迁移：基于人工神经网络的新型工艺数据采集与故障检测

摘要 虽然压铸是最流行的精密金属零件批量生产工艺之一，但压铸厂的制造环境仍停留在传统水平。在这项研究中，我们开发了三项核心技术来实现压铸行业的智能工厂平台：1) 一种新颖的、具有成本效益的产品跟踪技术，可获取提供单个产品信息的高质量过程数据，2) 先进的过程考虑过程故障的数据采集系统，以及3）基于人工神经网络的故障检测模块。我们新开发的压铸工艺系统已通过 1500 次试生产验证。基于中试生产数据，我们开发了一个故障检测模块，对时间序列温度和压力测量数据进行了预处理。开发的故障检测模块对未经训练的数据显示 96.9% 的准确率。本研究开发的技术有望成为一个有前途的智能工厂平台，以降低压铸行业的缺陷率和生产成本。