Avoiding faulty assemblies: Quality control per cross-checklist, component scan and PLC

Monday August 8th, 2016Process Data Management, Quality Assurance

QM1CSP shows options for quality assurance that will counteract errors and quality deficiencies in production early on.

 

 

The use of automated components such as cross-checklists, components scans and programmable logic controllers (PLC) helps detect errors and quality deficiencies in production early on and counteracting them deliberately. This is the third of the six golden rules, the software provider CSP GmbH & Co. KG has defined for quality assurance measures in production. Faulty products sometimes enter the market in spite of quality control. With the right software for production monitoring and control the correct assembly of components or the use of only fitting components – or the actual installation of all planned components – can be ensured. This produces high-quality production results and saves expensive rework.

 

 

Stored quality data and operating sequences facilitate the processes

 

 

Cross-checklists, component scans and completeness checks via PLC are measures of production monitoring that produce maximum quality in manufacturing. Cross-checklists can ensure that only the right components are installed in a product. For example, valid components may be assigned part numbers allowing their unambiguous allocation in the process. A scan process is used to verify that the components to be assembled correspond to the correct part or ID number. The assembly process can be stopped early enough through a PLC if this is not the case.

 

QM2Additional benefits are produced in production monitoring by scanning the components. This requires that each part was first marked with a barcode or DMC. The worker scans the present part at the beginning of an operating step. In the background the work sequences are loaded from a database based on freely configurable information. The following scenario would be conceivable: The tasks to be performed, including test instructions, are visualised for the worker on the screen for the respective component. The screw joints and steps to be processed can be displayed on the monitor based on images and texts. In addition, the tool to be used for this operating step may also be indicated here at the same time. The right tool is selected by the software. As soon as the first operating step has been processed with the OK status, the next step is displayed on a new screen. The process is visualised by the sequence of predefined images or graphics all the way to the end.

 

 

Quality department may be supported by self-learning components of a process data system. They expand the scope of tests and automatic features to produce a perfect product from the perspective of measured data and production. At this, the system learns over time how specific products are assembled, i.e. which components belong together, and alerts the departments if irregularities or deviations occur with the installation of parts. This is particularly interesting if cross-checklists cannot be used due to technical process limitations and data analysis is only subsequently possible.

 

CSP GmbH & Co. KG specialising in production monitoring and control that secures operating sequences on assembly line / cellular manufacturing or on rework stations at consistently high product quality. IPM PG worker guidance, amongst others, includes the functions of cross-checklist and component scan thereby facilitating traceability and quality assurance in the production process. Elements of Poka Yoke (Japanese for “zero error) were integrated in the software. This allows performing work activities in the production process even if new elements are introduced to the sequences without elaborate training sessions and as intended. Technical measures and precautions make sure that typical “human” errors – produced from negligence or lack of know-how – cannot slip into processes thus secured.

 

With IPM as higher level analysis system all processes can be subsequently analysed in detail and appropriate measures can be derived at, if necessary.

 

 

Author: Leonie Walter, Walter Visuelle PR GmbH