Integrating Heat Treatment with the Internet of Things

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Industry 4.0 and the Internet of Things (IoT) have been introduced to various manufacturing and industry related processes for a while now and has been termed the Industry Internet of Things (IIoT). One of the areas in which the IoT has been introduced to industry is in heat treatment processes, and in this article we look at how manufacturers are integrating the IoT into these processes and the benefits it brings.

The Internet of Things (IoT) is a collective name for the series of physical systems (or processes), compact and reliable sensors, cloud computing and big data analytics/multivariate analytic processes, that work in conjunction with each other to provide a more efficient system that can analyze data in real-time, provide more accurate data, and can make the data more accessible to any remote users. It is essentially a large interconnected system for the efficient exchange and analysis of data.

Data acquisition is important for any manufacturing process. Heat treatment is often defined as “the controlled application of time, temperature and atmosphere to produce a predictable change in the internal structure of a material.” With this quote in mind, it is easy to see how a lot of data is required to monitor and provide the optimal heating environment.

Heat treatment processes involve heating one or more materials, so that their mechanical, physical, metallurgical properties become modified. Traditionally, many of these processes have relied on an optimized design process but new advances in sensor and data technologies offer a more controlled way of introducing specific properties or effects into a given material(s).

Using IoT in Heat Treatment Processes

There are several considerations that manufacturers who use heat treatment processes adhere to so that production quality and volume demands can be met. These are seen to be: knowing what the metallurgical outcome should be, having the ability to predict the outcome of heat-treatment process, having a repeatable process, possessing the ability to get the most out of heat-treatment equipment, being aware that any changes to operations can have an influence on the outcome, not compromising on quality and knowing costs and predictions of profitability.

Industry 4.0 and the IoT are seen as ways of accomplishing all of these good practice considerations. There are a couple of key ways that it can be achieved for heat-treatments. These are through the use of predictive and preventative maintenance and the optimization of the heat-treatment process.

Predictive and Preventative Maintenance

Predictive maintenance uses analytics to detect any potential risks of failure and predict whether any failures are likely to occur (before they actually occur). On the other hand, preventative maintenance is the process of regularly maintaining and inspecting equipment to protect the equipment from major failure. General maintenance and servicing operations also fall into this category.

Both maintenance processes can be combined using the IoT to provide regular updates as to when maintenance is likely to be required, so that the physical maintenance can take place before any failures occur.

To combine these processes, the furnaces and heat-treatment environments are fitted with sensors that can monitor many different parameters within the furnace environment to ensure that it is running optimally, and to see whether any drops in efficiency are related to something that can be solved by physical maintenance processes.

Overall, these sensors can be used to detect whether any part of the furnace is in poor condition, to predict when breakdowns may occur and to identify the most efficient time-point where maintenance should be undertaken so that productive time-loss is minimized. Where the IoT comes in, is that the data collected from these sensors is translated into the analytical framework, which then uses the historical data and current data to predict these potential failures in the future. This also means that industry can employ a predict-and-fix model rather than a repair-and-replace model.

Optimizing Processes

One main aim of using the IoT is to monitor the maintenance process, but another is to also be able to use these sensors and data analytics method to understand what parameters in the heated environment affect the material in question and to understand how varying these different processes can be used to determine an optimal process. This can be especially tricky when different materials require different heating scenarios, but by obtaining the data more easily, it enables the users to analyze and determine the most effective way to introduce a property or a certain physical change to each given material.


Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Liam Critchley

Written by

Liam Critchley

Liam Critchley is a writer and journalist who specializes in Chemistry and Nanotechnology, with a MChem in Chemistry and Nanotechnology and M.Sc. Research in Chemical Engineering.

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