Both parameters follow a normal distribution. The mean sealing time is 1.5 seconds, with a standard deviation of 0.5. Seal Strength= 9.64 + 0.003*Temp + 4.0021*Time + 0.000145 Temp*TimeĬurrently, your packages are sealed at an average temperature of 120 degrees Celsius, with a standard deviation of 25.34. The relationship between the two factors is expressed by the model: Seal strength depends on the temperature of the sealing device and the sealing time. Meeting this specification is critical, because when a seal fails the product inside is no longer sterile and puts patients at risk. Your product line at AlphaGamma Medical Devices is shipped in heat-sealed packages with a minimum seal strength requirement of 13.5 Newtons per square millimeter (N/mm2). Here’s how you, as an engineer in the medical device industry, could use Companion to improve a packaging process and help ensure patient safety. If you have a process that isn’t meeting specifications, using the Monte Carlo simulation and optimization tools in Companion by Minitab can help. It can be generated, in this case, by lowering the Producer’s risk to 0.05.Īs you can see, the sample size for an acceptance number of 0 is much smaller-in this case, raising the acceptance number from 0 to 1 has raised the sample size from 59 to 93.Ĭheck out this post for more information about acceptance sampling. For example, allowing 1 defect in the sample will require a sample size of 93 for the 95% reliability statement. If you want to make the same confidence statements while allowing 1 or more defects in your sample, the sample size required will be larger. Setting Producer’s Risk (α) at an arbitrary high value, such as 0.5 (note, α must be less than 1- β to run).īy changing RQL to 1%, the following C=0 plan can be obtained:.Setting AQL at an arbitrary value lower than the RQL, such as 0.1%.Setting the Consumer’s Risk (β) at 0.05, which results in a 95% confidence level.Setting RQL at 5% for 95% reliability or 1% for 99% reliability. The same sample sizes can be generated using Stat > Quality Tools > Acceptance Sampling by Attributes by: These two sampling plans are really just C=0 Acceptance Sampling plans with an infinite lot size. Of course, if you don't feel like calculating this manually, you can use the Stat > Basic Statistics > 1 Proportion dialog box in Minitab to see the reliability levels for different sample sizes. , where the reliability is the probability of an in-spec item. In this blog post, I'll focus on the attribute approach.Ī simple formula gives you the sample size required to make a 95% confidence statement about the probability an item will be in-spec when your sample of size n has zero defects. The variables sampling approach has a strict normality assumption, but requires fewer samples. The attribute sampling approach is valid regardless of the underlying distribution of the data. Variables Sampling: Determine the sample size for a continuous measurement that follows a Normal distribution.Attribute Sampling: Determine the sample size for a categorical response that classifies each unit as Good or Bad (or, perhaps, In-spec or Out-of-spec).The type of response will dictate whether you 'll use: The answer depends on the type of response variable you are using, categorical or continuous. How many samples do you need to be “95% confident that at least 95%-or even 99%-of your product is good?
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