Industry 4.0 is here to stay. To remain competitive, heat treaters must take steps toward adopting automated and interconnected technologies, data analysis, intelligent systems, computer-based algorithms, machine learning, and autonomous decision-making. Predictive maintenance capabilities are particularly critical to heat treating operations. This paper provides background on Industry 4.0 and its relevance to the heat treating industry. It describes the small, incremental actions that can be taken to implement technologies for monitoring and managing heat treating operations, gathering and analyzing data sets, and fostering innovation among personnel.

Heat-treating industry is adopting more and more industry 4.0 techniques and solution packages, to improve production process and product quality. Proper specification, measurement, and control of heat-treating atmospheres are always critical to achieving the desired metallurgical and microstructural results. The combination of atmosphere measurements and other furnace operating parameters (e.g., furnace temperature and pressure) can provide a better view of the whole production. Thermodynamic calculations and field experiences can be integrated into the smart solution to provide process engineers more capabilities to manage and optimize production. In this article, our recent research and development work on smart solutions for the heat-treating industry will be presented and discussed.

The heat treat industry is quickly moving away from paper chart records due to changes in heat treatment standards such as AMS 2750 and CQI9. This move to digital data capture is leading to a step-change in the amount of data captured in heat treatment facilities. The amount of data collected in manufacturing can range from a few gigabytes per day to multiple terabytes per day, depending on the size and complexity of the manufacturing operation. However, manufacturers typically use only 10-30% of the data collected due to the following reasons: Lack of data integration - data is collected by different systems in different formats, making it difficult to integrate and analyze; Data quality issues - data is often incomplete, inaccurate, and outdated, making it difficult to make sensible decisions; Limited data analysis capabilities - manufacturers often need more tools and expertise to analyze the data effectively; and Data overload - the growing volume of data generated can be overwhelming and makes it challenging to extract meaningful insights. This paper aims to help explain how to maximize data value in a heat treat operation, focusing on data integration, quality, and analysis capabilities.