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Data Collection Trends in Metal Finishing

The finishing industry is moving towards the automation of process and production data collection. Roger Smith outlines new technology to help finishers automate and streamline.

Roger A. Smith, Chief Technology Officer, TrueLogic

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Q. What key trends in data collection have you been noticing within the metal finishing industry?

A. I have been creating and delivering process data collection software to the finishing industry for 25+ years. I’m constantly impressed with the people and companies in this business. It has been entertaining and rewarding over the expanse of my career to experience how the industry has evolved, and I am very excited about what we’ve been seeing recently and what’s coming up. 

In the last two years, there has been a significant increase in the number of requests to automate the collecting and/or sharing of data. Industry 4.0 and Internet of Things (IoT) projects have become more and more common in our daily discussions. Most of these projects start with a few critical data items, then increase in size as the benefits are recognized. Some projects, however, such as new facilities or others that require major investments, take on a much larger scale from the very beginning.

Industry 4.0 is an effort to advance traditional manufacturing through the introduction of the latest technologies in hardware and software, and to create communication streams between them.  

On the hardware side, the availability of new, powerful processing/testing equipment continues to grow. Many of these products come with data interfaces that are easy to communicate with. There is also a large variety of add-on devices (IoT) that can make data collection from legacy equipment possible. Once the data is acquired, software can automatically evaluate it for issues that can be reported on without the need for human intervention.

Along with this flow of process data, many customers want to collect production data to analyze and integrate with their process data. Production data, such as throughput and amperage usage/duration, can be useful in predicting the need for maintenance activities. This allows chemical additions and maintenance to be based on production factors in addition to basing these activities on analysis results or the passage of time.  Furthermore, the inclusion of product attribute data, such as thickness, hardness and color, significantly improves the ability to correlate process data, production data and outcome.

With the increase in types and sources of data, the need to disconnect from the desktop has become imperative. Also, the ability to enter data at the point of origin and to view it “on the go” has become critical. I am seeing many creative solutions, including small form-factor PCs (NUC), Raspberry Pi (or similar based platforms) and mobile phone/tablet solutions. A major benefit of collecting data using a mobile device is the verification of the location at the time of entry.

Tools such as dashboards and imbedded logic are becoming essential elements in successful operations. These tools are most effective when the organization operates in a data-driven mode, increasing the ability to predict and identify issues that can now be addressed much quicker than ever before.

Varying data types and sources feeding disjointed applications has created the need for data sharing between applications. This has become critical in implementing Industry 4.0. For the benefits to be fully realized, vendors across the industry will need to cooperate and collaborate in this sharing. 

All of these trends significantly help organizations become proactive versus reactive in their approach to handling issues. I have seen time and time again how this transition has provided significant improvements to efficiency, quality and costs. Finally, a major benefit I have witnessed from the movement towards automation is a less stressful and more productive environment for all stakeholders, creating enhanced performance, continual process improvements and, as a result, increased margins.

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