Bridging the Data Gap

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by Ryan Boehm, OMAX R&D Engineer

Getting ready for Industry 4.0


Industry 4.0 in a manufacturing setting must confront a myriad of interacting data management challenges: measurement conditioning, aggregation, exchange, etc. Smart technology of the next phase of our industry may hinge on cyber connectivity and automation, but without data collection in place to measure operator-set parameters, how do we know what is happening with the machines themselves?

Machine tools compound data management with further inherent challenges. Machine tools commonly stay in service for 20+ years, but data storage and processing power remain in rapid growth cycles. How often do we still see paper maintenance logs? The lag in mass adoption of data management tools in the industrial sector often creates complications through “data islands,” where siloed information lingers unused in a manufacturing tool’s memory or even in a handwritten log. Simply setting the goalposts for these challenges can be a major activity in this evolving landscape, but make no mistake, the ball is moving.

Maintenance operations have long been the focus of data-driven optimization studies. The associated costs often motivate end users to audit their own maintenance histories. The success of advanced maintenance programs demonstrates the complexity of maintenance forecasting in high-tech machinery and the potential for optimization. Fortunately, many OEMs and related software developers have programs in place to streamline data collection and get your shop ready for the next stages of Industry 4.0. 

System monitoring software enables you to connect operation controls with machine maintenance and upkeep for increased efficiency and reduced downtime. Such programs run in parallel with a machine tool’s controller software to maximize productivity, providing a comprehensive alert system that keeps you and your operators connected and aware of your machine’s status. 

For any machine tool, a good system monitoring program and alert system is a complex relational data model that provides a platform to serve a variety of use cases. This platform allows system designers, service technicians and end users to tailor the behaviour and experience of the software to their needs and application. Operators can manage multiple machines more easily. Purchasers gain the data to maintain a lean but reliable inventory of spare parts. Supervisors and maintenance staff receive automatic notification of important events to reduce down time. Top to bottom, accurate system monitoring allows for a comprehensive understanding of your machine tool input and output. This type of interconnectivity is central when moving into the future phases of an automated machine shop. 

Bridging the Data GapFlexibility in how we use and organize maintenance data allows for further customization. Not every machine shop is the same so why should all monitoring be the same? In addition to the pre-defined maintenance schedules that OEMs may set, machine operators and supervisors should be able to add their own maintenance tracking items. Custom maintenance tracking options include reference metrics (e.g., “Pump Hours” or “Jet Cycles”), counting direction (counting down/depleting or counting up/accumulating), and event trip thresholds (e.g., “Warn if greater than 50 lbs.” or “Err if less than 8 hours remaining”). 

Maximizing data accessibility has proven vital to the use of maintenance monitoring systems. Traditional service records typically include a hard-copy log kept next to the equipment. However, this creates another data island and produces several other shortcomings. With maintenance software, data remains accessible at the machine but also reaches your personal computer or smart device via email. Calculations can be displayed in real time. For example, you can monitor the remaining hours before the next scheduled service from your office or home. Maintenance data from software can be backed up automatically for redundancy. Maintenance notifications can be pushed directly to technicians via email/SMS based on a trip threshold. 

The important takeaway is the ever-growing value of data collection and integration. Data accessibility and interoperability directly contribute to the goal of streamlining processes in your machine shop. In time, as Industry 4.0 becomes a reality, the collected data will empower various evolving automated systems and your shop will be ahead of the curve. SMT

Ryan Boehm is an R&D engineer with OMAX.


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