I will give a brief explanation to my scenario. The company mass produces components like valves/nuts/bolts etc which need to measured for dimensions (like length,radius,thickness etc) for quality purposes. As it is not feasible to inspect all the pieces, they are chosen in a batch style. Foe eg: from a batch of every 100 pieces, 5 will be randomly selected & mean of their dimensions measured & noted for drawing SPC control charts (plots mean dimension on y axis & batch number on x axis).
Even though there are a number of factors (like operator efficiency, machine/tool condition etc) which affect the quality of the product, they don't seem to be measurable. My objective is to develop a machine learning model to predict the product dimensions of the coming batch samples(mean). This will help the operator to forecast if there is going to be any significant dimensional variation so that he can pause working & figure out potential reasons & thus prevent the wastage of the product/material.
I have some idea about R programming & machine learning techniques like decision trees/regression etc but couldn't land on a proper model for this. Mainly because I couldn't think of the independent variables for this situation. I don't have much idea about time series modelling though.
Will someone throw some insights/ideas/suggestions about how to tackle this. I am sorry that I had to write a long story but just wanted to make things as clear as possible.
Thanks in advance. Sreenath
Your requirement may apply with three level by steps:
1.Fundamental
Automatic apply SPC rule with machine learning, ex. identify SPC chart pattern with Nelson rule, and extend to new pattern of variation in specific process.
2.Supplemental
Predicate Cp and SPC trend with multivariant collection and machine learning. For example, particle of smoke will impact wafer yield rate, it may earlier to found if data analysis model link SPC and worker shift arrangement
3.Intelligent agent
Automatic process event through integration between SPC and reaction plan. The agent model by link SPC and FMEA and build with CEP engine in BAM architecture.