Kistler Introduces STASA Quality Control Plastics Injection Molding Software

Software is based on design of experiments method.

Software is based on design of experiments method.

By DE Editors

Kistler North America has introduced its industry exclusive STASA QC plastics injection molding process optimization software. STASA QC is designed to optimize the machinery parameters,  including process stabilization, shortened cycle times, and production efficiencies, most critical to zero-defect medical, automotive,  electrical component, optical, and LSR plastics injection molding operations.

STASA QC is based on a repeatable systematic design of experiments (DOE) method for determining best machinery setting operating points,  as well as online processes. With user-selectable parameters, such as holding pressure levels, injection speed and others, STASA QC recommends a number of experiments, allowing for a setter to change or enhance a selection as needed. The DOE methodology allows for machinery behavior simulation and visualization, preventing unnecessary experiments. All parts created from these experiments and their associated geometries are analyzed to determine best machinery settings. All mathematical calculations occur in the background, with a minimum number of tests required to run at various parameter settings.

During a typical STASA QC simulated injection molding process,  experiments are carried out on a PC, with parameters that can be changed interactively by the clicking and dragging of a mouse. The effects of these changes on each quality feature can be tracked on-screen, without doing so on the injection molding machine. The solution has an integrated report feature for protocols that provides an end-to-end documentation of the setting procedure and all optimization results. Resultant measurements from these experiments are imported into STASA QC for proper system storage of required dimensions and variations, as well as attributive part features for each machinery setting. It also verifies potential processing capability of a defined setting.

By using this data and applying innovative data-based modeling methods,  the software identifies a precise correlation between machinery setting and part quality. With the help of this correlation, the software determines the ideal point and setting at which the machine meets set quality requirements, taking into account statistical fluctuations of part dimensions.  

For more information, visit Kistler North America.

Sources: Press materials received from the company and additional information gleaned from the company’s website.

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DE Editors

DE’s editors contribute news and new product announcements to Digital Engineering.
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