Optimize to Save Energy

As more machine tool end users are aiming to reduce energy costs and carbon dioxide emissions, data-driven manufacturing methods have an important role to play.


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Over the past decade, an increasing number of machine tool end users have adopted specific targets to reduce energy costs and more importantly, carbon dioxide emissions. In recent years, the focus of this effort has shifted to data-driven manufacturing methods to achieve this. While it is important to monitor production throughput, downtime and energy consumption to get a real-time picture of shopfloor performance, it is equally important to optimize your manufacturing equipment right from the start. Ideally, such optimization should begin at the OEM level, or when upgrading or retooling equipment.

Developers of industrial control systems can play a major role in enabling suppliers of machine tools and other manufacturing equipment to reduce energy usage and improve efficiency from the ground up. In fact, control developers should be at the forefront of the energy-efficiency initiative, and it is important for end users to understand the technology now offered to support this initiative.

A review of the offerings from my employer, Bosch Rexroth Corp. (Hoffman Estates, Illinois) can serve this purpose. Of course, other control developers are also cognizant of the importance of optimization and energy efficiency, further reinforcing the timeliness of this glimpse at current technologies and the concepts behind them.

From our perspective, the starting point is an overarching strategy called 4EE (Rexroth for Energy Efficiency) which has several onboard software tools available with its IndraMotion MTX CNC platform, bundled into an “efficiency workbench.” The main elements of this efficiency workbench are the Cycle Time Analyzer and Energy Analyzer. From the beginning of the machine design and through commissioning, these tools enable the machine builder to analyze prospective part programs and tune them with transparency and detail, thereby maximizing equipment productivity and efficiency. The results are both lower energy consumption and lower overall CO2 emissions.

The Cycle Time Analyzer tool, for example, provides real-time recording of critical data generated in the CNC system. The types of data captured include the execution of NC program sequences, detailed execution times, and the dependencies among the NC program’s G-code blocks, interpolator motion settings and corresponding functions of the PLC. With the recorded details and software visualization tools, engineers can optimize programs in order to achieve the shortest workpiece cycle times. These visualizations can take the form of synchronized timelines displayed on a single screen to show what events or measurements are coinciding. Another visualization may show tool paths that are color-coded to indicate the energy consumed by corresponding machine motions.

CNC machine tools execute thousands of NC blocks, hundreds of auxiliary functions triggered by M codes and many tool changes per workpiece cycle. Saving fractions of a second for each of the command sequences quickly adds up to many saved minutes per hour, hours per day and days per year. In one example, during an upgrade of an automated machining center, the NC program cycle time was reduced from about 600 seconds to 480 seconds by using the Cycle Time Analyzer tool—a significant productivity gain. Additionally, due to the clearly recorded details and a better understanding of the CNC’s programmed activity, improvements were implemented that increased machine reliability and reduced downtime.

It is important to note that machinery optimized for the shortest cycle time may still have room to reduce energy usage, such as when peak rates apply. Together with the Cycle Time Analyzer, Bosch Rexroth’s Energy Analyzer tool records and displays graphically the energy consumption of the machine’s main components (electric drives, hydraulic power unit, coolant pump and so on).  This information enables users to optimize the motion profiles of servo and spindle axes with the activation of other energy consumers, resulting in a clear understanding and control of energy consumption during the machine cycle. Avoiding unnecessary acclerations is an example of these savings.

The concepts of data-driven manufacturing apply to decisions about the purchase of equipment. With analytical software tools such as the Cycle Time Analyzer and Energy Analyzer, programmers can record, visualize and save vital details about specific setups. The insights gleaned from this data effectively drive machine optimization and preserve the findings in documentation.

For more information for the efficiency workbench, visit short.mmsonline.com/workbench.