The workpiece in the crosshairs: In-process quality management in CNC production

When machining, chips will fly; when work is being done, mistakes will happen. So that nothing goes wrong on the CNC shopfloor, Siemens offers a whole range of apps, in the form of Industrial Edge for machine tools that support machine operators when it comes to quality management functions. Irrespective of whether large unit quantities or single parts are involved: incorrect clamping, faulty parts and marginal tool states can already be identified during production.

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Quality control for all workpieces

Random checks are made after specific machining steps to ensure that a workpiece is within specifications – but generally, this is only done right at the end of production. Frequently, comprehensive tactile and optical measuring equipment is deployed. In-process quality control is a real alternative; it involves less overhead, costs and space and has all of the workpieces in its crosshairs – there are no exceptions. Data captured during machining along with workpiece and tool data are used to identify errors, and ideally, to avoid them in the first place. For all aspects of Sinumerik CNC, there are several Edge apps available that complement one another and which are able to monitor certain processes and flag users about questionable values. They operate on the basis of Industrial Edge for machine tools, where only a small hardware box is required in the control cabinet. High-frequency data can be captured and evaluated while the machine is operational without having a negative impact on the machining process itself.

When setting up

When you start your machine in a CNC production environment, there is always a certain amount of uncertainty. This is because everything has to be just right – for example, the machine must have been correctly set up and the workpiece precisely clamped. Irrespective of whether a machine operator did everything himself, or whether it was a robot in a highly automated process – you are never completely immune to a few chips that cause a slightly skewed clamping situation, or a gradual pressure drop in the clamping equipment. From rejects through unnecessary longer machining times to increased tool wear or even tool breakage: Every head of production knows the consequences of clamping inaccuracies or errors – and in all conceivable variations. An app that optically monitors the clamping process using a camera and artificial intelligence (AI) can come to the rescue. An algorithm trained with reference images checks the recorded clamping situation and verifies that all preconditions are in place for a perfectly machined workpiece.

 

Information from this app can also support the automation system: On a Chiron milling center in the Siemens Bad Neustadt factory, a handling robot is able to distinguish between 13 different blanks, which are fed in on a blister pallet. The system assigns the corresponding robot and machine tool CNC programs to the various blanks and machines them independently, including changing the clamping equipment.

 

Savings in the 5-digit euro range

A comparison clearly highlights the cost savings: Using sensors, a conventional solution checks each blank for specific attributes. When a change takes place, these must be adapted to a new production batch – a tedious process. On the other hand, using the app-based approach, the Siemens factory in Bad Neustadt achieves cost savings in the 5-digit euro range.  

In series production

An application that is extremely useful, especially for large unit quantities involves an all-encompassing quality control of workpieces during the production process. This is realized directly at the machine, either during or briefly after machining. Neither complex sensors nor measuring instruments are required. Bad parts can be identified by recording CNC data during machining. This data is then checked against an algorithm, which has been trained with data sets for good and bad parts.

 

Not only are individual parameters monitored, but also several signals simultaneously or even summed signals. Any deviation indicates that the workpiece that has just been produced possibly does not comply with quality specifications. It can be removed from the process, checked and if necessary, reworked.

 

Final tests are essentially superfluous when this type of permanent in-process monitoring is in place.

In individual part production

One-of-a-kind parts are often demanding, especially large or expensive – or have all of these attributes. The first pass machining should therefore be spot on without requiring exhaustive tests or reworking. Production planning forms the basis to achieve this if it is derived from CAD data and the appropriate CNC program. Each optimization that is made and each error that is then resolved has a direct impact on a successfully machined workpiece. PC software, including an Edge app, can provide support here. Based on 3D surface reconstruction, it can visually detect even more machining errors and analyze them.

 

Also relevant for series production

Non-productive idle times can be identified and reduced so that this application also provides an important optimization basis for series production.

The tools

Tool wear cannot be precisely forecast. In some cases, the quality of the basis material deviates, sometimes the tolerances of the blanks – factors that impact the service life of a tool. A worn cutting edge can negatively impact the part quality, and even cause tool breakage. With information from the Edge app, tool wear can be assessed, and a decision made as to whether the remaining service life allows it to be used again. Using a microscopic camera and AI, this application monitors tool cutting edge wear, so that the tool is neither replaced too early nor too late.

Summary

Industrial Edge for machine tools continually provides a stream of data during production. This data is used directly and meaningfully – this is precisely what Protect MyMachine /Setup, Analyze MyWorkpiece /Monitor, Analyze MyWorkpiece /Toolpath and Analyze MyWorkpiece /ToolCheck apps do. Based on these apps, a comprehensive in-process quality management can be set up. Each individual app represents a reliable virtual colleague on the shop floor. They keep their vigilant eyes on certain processes and in their own specific way help reduce manual intervention, wait times as well as storage space to further boost productivity. 

From CNC4you magazine 2022-2  

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