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Kistler Introduces Injection Molding Software

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This software is designed to optimize the machinery parameters, including process stabilization, shortened cycle times, and production efficiencies,

This software is designed to optimize the machinery parameters, including process stabilization, shortened cycle times, and production efficiencies,

April 6, 2012—

Kistler North America, a worldwide supplier of precision sensors, systems and instrumentation for the dynamic measurement of pressure, force, torque and acceleration, has announced the global market introduction of its industry exclusive STASA QC plastics injection molding process optimization software. STASA QC is expressly 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.

Traditional injection molding machinery optimization involves time-consuming, manual "trial-and-error" adjustments of relevant parameters until all quality targets are met. During this phase, user experience with similar parts, materials and injection molding machinery is critical. Online process optimization (i.e., during active production) is even more complex, as each parameter change can mean new machinery setting modifications, cycle time data recording and molded parts measurements. Due to post-production shrinkage or water absorption, parts can take several days to be ready for use, often first requiring time-consuming readjustment of machinery operating points, creating costly downtime.

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. This is of benefit, particularly for the online optimization of active production processes. STASA QC 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, STASA QC 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. The best machinery operating point is one that is producing the fewest defective parts. At the same time, STASA QC automatically determines the effects of machinery settings on individual part quality features.

New Kistler STASA QC software offers lower overall production costs, with shorter cycle times, fewer defective parts and greater ability to accurately forecast parts processes; faster production start-up; fewer required experiments; safer injection molding processes; more stable and continuous zero-defect parts production; fewer required readjustments during large-scale production processes; and more readily available, accurate process information, with fully reproducible and recordable results. 

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