It is part of a Reliability Engineering statistical life data analysis. The results from Weibul, Minitab statistical analysis are used to predict the life of each individual component of an engine and also the life cycle of the complete engine. It's accuracy is extremely dependent on the variablity of the data collected and the condition of the tested parts which should represent the range of build or population of the production parts.
The B10, B50, & B90 curves presented on a chart of life cycles versus load with a certain degree of confidence should demonstrate the range expected life of the component or assembly if and only if all the variables have been presented properly. Here is where the results get real sticky. Have the entire range of variables been represented in the sample tests? Have the testing parameters been appropriate to the life cycle loads of the customer (typically 95th percentile). Is the sample size adequate to present the life cycle data with a degree of confidence necessary to not only provide a product that is going to be acceptable to the customer but also provide the company with a degree of confidence that they won't incurr excessive liability costs?
The compaines require that their suppliers use these statistical numbers to insure the relianbility of the product. One little snafu in component metalurgy, machining, casting porosity, fit and finish, can cause all sorts of havoc. that is why the companies have Black Belt Teams to quickly address any problems as soon as they become evident. They use engineers who have passed several hurdles of knowledge in statistical analysis, product design, production processes, etc. to develop the model that will predict the satisfactory product.
Occasionaly the system doesn't work because of an overlooked variable. For instance, say that a spool sticks in a bore less than ten percent of the time but it happens only during a Summer month of August and only at one supplier. First the data has to be collected to identify the variable condition then a team must be sent in to investigate the vendors process. When the team is sent in the company is producing parts to spec one after another and nothing shows up. Then the process gets even more detailed as they try to discover the unknown variable. After much study they find that one operator transferred to the casting process on the afternoon shift only during the vacation months of August is adding additinal casting release oil to allow the die cast body to be seperated from the die without any transfer of metal. It is producing a cold shut in the casting that after machining tends to catch the spool and cause it to bind. Solution, control the process by installing an automatic release oil injector which cannot be tampered with by the operator. Those are the easy ones but they do get through to the end customer. The more difficult ones are those that are in the original design and are not easily fixed by a process change. Those require a major redesign and a lot of cash to fix. Statistics are heavily used for all of the analysis in most cases they know where the product is weak and what the fatigue life cycle will be and so they constantly work to identify and correct those deficiencies.
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Today's Featured Article - Grain Threshing in the Early 40's - by Jerry D. Coleman. How many of you can sit there and say that you have plowed with a mule? Well I would say not many, but maybe a few. This story is about the day my Grandfather Brown (true name) decided along with my parents to purchase a new Ford tractor. It wasn't really new except to us. The year was about 1967 and my father found a good used Ford 601 tractor to use on the farm instead of "Bob", our old mule. Now my grandfather had had this mule since the mid 40's and he was getting some age on him. S
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