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Data is a key to start the journey towards Industry 4.0, and robust analysis of machine data can enable efficiency-optimised production, and potentially lead to new business models. Article by Beckhoff Automation.

Figure 1: Bystronic’s modern high-bay sheet metal storage facility (in the background) with the associated handling device (C) and the changeover table of the laser cutting machine (foreground) holding a processed sheet. (Photo: Beckhoff Automation)

Industry 4.0 is a buzzword that was invented back in 2011. Eight years on, the industry has very much moved on from defining the term towards actual implementation.

Industry 4.0 essentially covers a wide scope, from organisational processes down to the production machines, with each part needing to play its own role in reaching towards the end goal – creating a smart factory. However, before any intelligence can happen, one should not neglect the foundation of it all, collecting suitable and sufficient data.

Another term that we commonly encounter these days is ‘big data’. Giant IT companies such as Facebook and Google have recognised that collecting and analysing huge amounts of data in a target-oriented manner delivers valuable benefits. The same experience and technology is slowly making its way to the manufacturing industry.

Analysing machine data brings forth some benefits to the overall manufacturing process, such as allowing the system to accurately predict potential machine failures, or monitoring and reporting machine performance, just to name a few. Ultimately, the company will be able to achieve cost savings with predictive parts change, and machine performance data can help to identify areas of improvement in the production process. In addition, targeted improvement actions can be taken.


Dealing with Legacy Systems

Let’s delve deeper into data collection on metalworking equipment. Majority of the metalworking equipment that are being used nowadays are still standalone. They are not connected in any way to a central server; parameters and machine data are mainly stored locally within the controller. Companies are facing challenges to collect data from such equipment because the machine controller can either be locked, no interface for third party system connection, or just way too old. Having said that, there are still equipment in the field that are open, and companies can tap onto the existing controller to retrieve any available data.

In order to overcome such challenges, companies may resolve to one of the following options to collect data from the equipment:

  • integrate with existing controller if it’s open;
  • add auxiliary system with additional sensors onto the machine; or,
  • retrofit the machine controller to a newer system.

While multiple options of implementation are available, it is common to have a mix of solutions and multiple brands of equipment in the plant, each running with a different controller model. This also means that integration of a variety of protocol is expected. In such a diverse environment, any system added to the equipment for data collection is recommended to run with open standards, especially the types of communication protocol supported.

The data acquisition system should also be flexible enough. The system should cater for future expansion, as such projects are most likely to be implemented in phases. Users should also be able to specify if they would like the data to be stored and analysed locally, or transmitted to the server or cloud for further storage and analysis. Companies should always keep the end goal in mind, taking into consideration that the system deployed at the equipment level should be ready to connect with the systems at the IT level, be it a manufacturing execution system (MES) or an enterprise resource planning (ERP) system, to achieve final overall integration.

Simply generating enormous amounts of data is not enough, these data volumes also have to be managed. With proper analytics tool, companies can translate raw data into meaningful information that can be used to improve their production processes, improve product quality and save maintenance cost.

For example, bearing is one of the components in the machine that requires replacement most often, and usually vibration of the machine is monitored to predict bearing failure. Hence, with sufficient amount of vibration data, companies can then better predict when the bearing is most likely to fail with the trends observed, and execute parts change only when required. Another example will be the monitoring of energy used by the equipment – having such data will help in identifying areas for potential energy savings, and to better plan the production to maximise savings.

These are just two of the many benefits that can be achieved with data collection. When exploring solutions for data collection and analysis, system integrators are starting to look at PC-based control to handle the large amount of data expected, something a conventional PLC may not be able to achieve.

In conclusion, with the variety of solution available in the market, companies should always work with open standards and flexible system for their metalworking equipment. Data is indeed a key to start the journey towards Industry 4.0, and robust analysis of machine data can enable efficiency-optimised production, and potentially lead to new business models.








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