Weeks 2 and 3 at Microtech

I returned to Microtech a couple weeks ago, and have since worked on many more aspects of the production process.

I started by continuing to familiarize myself with the key functions and methods used at Microtech. In addition to the measuring and auditing parts that I had learned to to before I left, I learned about a few more important functions of the quality lab. First is a process called burst testing. In a battery, the inside is filled with paste, and as the cell uses power the electricity contained in the compound undergoes chemical changes, causing it to release gases. In order to prevent the battery from exploding from use, the battery seals must be breathable enough to allow the produced gases to escape. In order to ensure that the seals are breathable enough, we put them into a machine called a burst tester. The seals are capped using metal parts similar to those you would see on a battery, and then are taken into a sealed chamber in which they are pressurized until the plastic seals burst. The machine records the pressures at which each of the seals bursts, so that we can analyze any trends in the data to identify if there are defective cavities or groups. For each type of battery seal, there are established limits on what is considered an appropriate pressure for the seal to burst at. If the seal bursts too early, then that means that the battery will die out early when begin used because the seal would pop before the all of the battery acid is used. If the seal bursts at a pressure that exceeds the required specifications, then that means that the rest of the battery would break before the seal, allowing batter acid to pour out into the device using it, potentially causing more damage. The next major task that I got into in the quality lab is a process called gage R&R. Gage R&R is a widely used statistical method in industry to ensure two major components of any production process: Repeatability and Reproducibility. In a gage study, multiple operators (people) measure the saem set of parts using some technique (gage) in a random double blind setting. Once measurements of all the parts are taken by all the operators, the values are entered into Minitab statistical software and evaluated. The results show the variability in the system, and minitab isolates the various sources of variability in the system - operator error, instrumental error, methodological error, and production error. Using these results, it becomes possible to determine the viability and efficiency of a certain gage as a measurement method. The repeatability and reproducibility are also analyzed, helping to provide mathematical backing for the variation in measurements by a single operator or instrument and between operators or instruments, respectively. 

Outside the quality lab, I delved right into the core of my research project, which is the statistical analysis of processes using the eDart system to collect data from the injection molding machines.The eDart collects data directly from the mold on various parameters such as Injection Integral, Peak Pressure, Shot temperature, and more. In industrial production, the goal is to optimize the production process so that each and every parameter is fine tuned to the best possible setting for the best production of high quality outputs, and in order to find out exactly what those best fitting settings are, we use a process called DOE ( Design of Experiments). In a DOE, each of the desired parameters are manipulated and isolated in a matrix that can then be compared with the output data in order to determine which changes resulted in positive, neutral, or negative standards of quality. Additionally, the data from the eDart allows us to make a couple additional inferences. First, it allows us to correlate the parameters that we manipulate (mold temperature, barrel temperature, hold pressure, etc.) with other parameters that we do not manipulate but still change, that we call covariates (injection integral, peak shot stroke, injection pressure, etc.). Second, it allows us to see similarities between the covariates and the final quality of the products. By allowing us to further break down the process and to create these inferences, the task of creating the optimal process becomes easier and more concrete.