The next step is process control. Process control limits are established to ensure that process consistency is maintained. Process changes that stray outside of those limits are viewed as “red flags,” requiring a deeper assessment of changes that have occurred. If a process requires multiple changes during startup, or if a process requires frequent changes during production, something is wrong!
There is a reason that the scientific molding approach has been defined as a repetitive, or standardized, process. It is important to remember that it is possible to have more than one working process. Our goal as processors always remains the same: Easy startup, 0 to 1.5% scrap and 100% efficiency based on quoted cycle. This defines true process. If the process we have deemed valid does not provide high efficiencies, low to zero scrap and adequate startup results, we must reevaluate and look for ways to achieve maximum yields. We as molders are not limited only to process changes. We must review mold design, component function and mold modification as potential sources of achieving the goals for which we strive. Consider all available criteria prior to a complete change of process approach. Never assume that the process itself is invalid, until you have ruled out that other criteria are limiting the process requirements and controls.
All historical data must be recorded for future analysis. It is important to note that when processes go “bad,” it isn’t the process that has failed. Data give us direction and insight into changes that have occurred. In most cases, recordable data provide a troubleshooting blueprint used to correct whatever change has occurred. The first thing to remember when documenting a process is that strong data references are key! A great comparison is the difference between a black and white picture and the same picture that has been colorized. The more information that is recorded, the better we can distinguish changes in molding conditions. A poor approach toward process monitoring will always result in vague interpretations of available data, because the data sets are lacking. Limited data lead to poor interpretations of data sets. Evaluation requires more time because the lack of available information inhibits our ability to quickly assess what has happened, and how best to correct the condition.
With this, let’s address the meat of the topic, which is why molders fail.
- Button-pushing cowboys. Many times over the years, I have watched this scenario unfold: Rather than trying to identify what has changed, a molder starts pushing buttons in an attempt to make corrections. The proper approach requires us to first ask, “What has changed”? Process control is set aside, and process limits are totally ignored. Think of the potential outcomes of this approach: Bad parts reach the customer, hours of scrap result from poor evaluation and consistency is non-existent in the molding approach.