The three applications are: Analyze MyWorkpiece /Capture; Analyze MyWorkpiece /Toolpath; and Analyze MyMachine /Condition.
These three join the Optimize My Machining /Trochoidal application that significantly extends tool life via trochoidal milling and which was unveiled back in September 2018.
Siemens is now offering applications that address the three main value drivers of edge computing in the machine tool sector: process quality, machine condition and productivity improvement.
Analyze MyWorkpiece /Capture records all types of workpiece, tool and toolpath data with a high temporal resolution. Additional data such as drive currents, control deviations or spindle torque can also be recorded. Users determine which data should be captured and how, for instance only for selected machining steps or from specific threshold values. The recorded data can subsequently be imported into the CNC shopfloor management application Analyze MyWorkpiece /Toolpath, where it can be visualised and analysed.
The recorded position data can be used to reconstruct a 3D representation, and additional measurement values can also be depicted with colour coding as a fourth dimension using the component topology. The colour coding enables functions such as the identification of deviating path velocities. The colour-coded visualisation of path curvature offers further scope for analysis.
Fundamentally, the applications enable target data – generated for instance by CAM – to be compared to data recorded during the actual process. As the resulting findings enable optimisation of the CADCAM model and the generated NC program, the Analyze MyWorkpiece /Toolpath application helps provide the assurance of quality right from the very first produced workpiece. The improved degree of transparency enables the deduction of wide-ranging findings, which can be used to bring about an overall improvement in process quality and process development.
Analyze MyMachine /Condition captures a mechanical fingerprint of the machine tool. Flexibly configurable measurement series can be used to capture a variety of parameters such as rigidity, friction and reverse backlash in the individual axes. By visualising the measured results and comparing them to reference data, an overall improvement of machine tool condition transparency is achieved. The early identification of non-conformance made possible by the application allows optimized machine operation and reduced machine downtimes. Condition-dependent maintenance also makes for optimised processes and cost savings. Improving and correcting machine parameters such as control variables can prevent any potential loss of quality in running production.
