Siemens' AnalyzeMyWorkpiece /Monitor Enables Process-Parallel Quality Control
EMO 2019: Siemens expands its software portfolio for machine tools with AnalyzeMyWorkpiece /Monitor, which enables continuous monitoring of workpiece production in machine tools.
Siemens expands its software portfolio for machine tools with AnalyzeMyWorkpiece /Monitor, which enables continuous monitoring of workpiece production in machine tools.
The software acquires measured values such as position data, torque values or control deviations with context information from the machine with high frequency and without feedback through Sinumerik Edge. The measured values are compared with a reference model. If deviations exceed a predefined level, the application logs the result, sends it to the machine tool and enables prompt responses by the operator. Process-parallel quality control is thus supported during production.
The AnalyzeMyWorkpiece /Capture Edge application records selected data that can then be visualized using the AnalyzeMyWorkpiece /Toolpath PC application. The high-frequency data points support detailed visualization and a comprehensive analysis of the workpieces, meaning that workpiece quality can be optimized during production launch, the company says.
Siemens is also presenting other machine availability and process optimization applications. For machine availability, the AnalyzeMyMachine /Condition software can be used to monitor critical machine parameters. To increase efficiency, Siemens offers OptimizeMyMachining /Trochoidal for trochoidal milling where NC programs can be optimized directly on the machine tool.
An introduction to the standards, decision-making, training, cybersecurity, sensors, machine monitoring and cloud computing that make up the IIoT.
A panel discussion at the recent Top Shops Conference focused on various points of view regarding the value of connecting machine tools to a network for monitoring performance and recording results. Because machine monitoring helps a shop make better decisions about manufacturing processes, it is a good example of data-driven manufacturing in action.
A manufacturer that is distinctive for its attention to in-cycle machining productivity describes its efforts to obtain efficiency improvements outside of the machining cycle. The shop’s primary tool is a simple, daily, graphical recap that illustrates when each machine tool was and was not making parts.