Statistical Process Control (SPC)
Assembly Line
Electronic Bore Gage Automates Bore Data Acquisition
Manually writing down bore gage measurements and inputting them into a statistical process control (SPC) system is slow and risks transcription errors even when working on parts with standardized requirements — this process grows unacceptably riskier with custom precision parts and bearings, such as those made by Kamatics Corporation. When the high-mix, low-volume operation started searching for ways to reliably increase productivity and profitability, moving to a digital gaging approach was high on the list.
The company teamed up with Sunnen to produce a digital gage, using the analog PG-800 gage as a starting point and rapidly prototyping the experimental system. Phil Hanna, product manager at Sunnen, says the PG-800 was used as the starting point because it was a well-proven system that Kamatics was already using. The PG-800 can cover diameters from 0.370 inch to 3.00 inches (with optimal extension fingers) and is typically used with a Sunnen PG-400E or PG-500E setting fixture to eliminate the need for master rings.
The Science of Production
Getting to this state of control was an iterative process - each time something was fixed, more data was collected, revealing new causes that the previous issues had masked. Each iteration proceeded the same way - plot the data on a control chart, look for patterns, locate the issue and make any necessary process adjustments.
Construction, once again, is a world that pushes production optimization difficulties to 11. All the things that make science hard to do in a manufacturing environment are even harder in construction. For one thing, construction has a much higher rate of process changes - every new project means new workers, new environmental conditions, new materials, new construction details, etc. Not only does this introduce new causes to the process, but it changes (if only slightly) what the basic process is. As we’ve seen with learning curves, it only takes very small disruptions to ‘reset’ what workers know about a process, and these disruptions occur much more frequently in construction.
Process Monitoring and Control of Semiconductor Production Tools Using JMP
This paper will present data manipulation and statistical analysis across multiple database types and architectures using JMP. These methodologies generate advanced statistical process control strategies to detect and rectify semiconductor production tool deterioration. Examples of data analysis and presentation via JMP will be used to highlight the importance of comprehendible statistical analysis to high yield semiconductor manufacturing.