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Showing events with label
Design of Experiments
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Using DOE to Improve the Performance of a High-Speed Dynamic Seal
Wednesday, October 23, 2024
Executive Briefing Center 9
At Francis Medical, the cannula of our disposable delivery device is inserted into the urethra and then a small round catheter exits the cannula into the prostate where steam exiting from the catheter is used to ablate tissue. Due to hydrostatic pressure from the bladder, the dynamic seal between the cannula and catheter is the only barrier for a possible fluid ingress pathway into the delivery device, which is undesirable. Testing the dynamic seal on the bench is accomplished by pressurizing the cannula using a pneumatic pressure decay tester. While the output of this tester is continuous, the distribution is bimodal and is best modeled as a binary output: either the seal leaks or the seal does not leak. In JMP, there is no straightforward method to calculate the power and sample size to allow for comparison of different design of experiment (DOE) studies for an attribute output. In this paper, we outline the process methodology we used to determine the number of factors to test and how many runs to complete, including a simulated power analysis. In addition, we discovered a unique way to condition the samples prior to pressure decay testing to create more dynamic seal failures than achieved through historical testing. In the end, the results of the experiment allowed us to make definitive design decisions with confidence and improve the dynamic seal performance by a factor of 25 from the current design.
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Design of Experiments
Quality and Process Engineering
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To DOE or Not to DOE? That’s Not Even a Question!
Wednesday, October 23, 2024
Executive Briefing Center 8
From grade school science fairs to undergraduate chemistry labs and beyond, our future researchers, scientists, and engineers are often not formally taught design of experiments (DOE) methods. Instead, they are typically told to use the “one-factor-at-a-time” (OFAT) approach. But OFAT has major disadvantages and will always be less efficient than DOE, requiring more time and effort for worse results. Clearly, “To DOE or not to DOE?” should not even be a question! Join us to hear how a high school dirt biker learned that lesson by harnessing DOE to take his race performance to the next level. We show how, using JMP’s Easy DOE platform, he was able to realize the advantages of DOE without needing in-depth DOE knowledge. Then we advance beyond Easy DOE to JMP's Custom and Definitive Screening Designs, for flexibly accommodating constraints and special cases. Come for the fast dirt bikes and stay to see that OFAT just can’t keep up and that DOE is always the right answer!
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Basic Data Analysis and Modeling
Design of Experiments
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Using Easy DOE to Evaluate Optimal Conditions for Organometallic Catalysis
Wednesday, October 23, 2024
Ped 6
Often involving sensitive reagents and complex, unstable products, the synthesis of organometallic catalysts can be challenging. Relatively weak bonds between metal centers and coordinating groups mean that aquo and dioxygen ligands can interrupt the desired molecular structure, frequently necessitating oxygen- and water-free working conditions. Following synthesis and characterization, the optimal conditions for these catalysts must be found. Costly and unsustainable metals such as rhodium, iridium, and palladium often form the centers of catalysts, and thus their consumption must be minimized. In this work, the relevance of Easy DOE to the optimization and analysis of three iridium catalysts is discussed in two groups of variables. The first set of conditions informs what the best working conditions of the catalysts are and helps outline its capabilities. Variables that are tested are the catalyst substituent (crown ether, methoxyethyl, or methyl), the substrate, the addition sodium or lithium salts, and the addition of water. The second set of conditions form an evaluation of environmental friendliness, nodding to Anastas and Warner’s criteria for green chemistry. Variables that are tested are the solvent (traditional solvents such as dichloromethane vs. greener choices such as acetonitrile), the pressure of hydrogen, and the sensitivity to oxygen. Easy DOE is employed to design these runs, slimming the input required to obtain meaningful data. Together, these two sets of conditions give a picture of the chemical environment that best suits the catalysts, as well as how to tune this chemistry for the greener.
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Data Exploration and Visualization
Design of Experiments
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Using DOEs and MCSs in Structural Assessments of Subsea Equipment
Wednesday, October 23, 2024
Ped 7
A marine drilling riser system is used in offshore exploration as a conduit connecting the drilling vessel with the subsea well. It is a complex structural subsea piping system commonly constructed by 75-90-feet long joints, typically sequentially assembled until they reach the wellhead, sometimes at water depth exceeding 10,000 feet. In a recertification project of a riser system meant to ensure compliance with regulatory requirements, inspection findings strongly indicated that the system had been exposed to an accelerated corrosion process. Corrosion rates for carbon steel in a seawater submerged application are normally measured to 0.1-0.4 mm per year. The inspection data showed localized corrosion rates exceeding 4 mm per year. Thirty riser joints were completely disassembled and inspected. However, 65 riser joints were inaccessible as they were located offshore and already in service. To quantify the operational risks and estimate the probability of non-compliance with the governing code, it became urgently necessary to extrapolate the corrosion data from the 30 inspected units to the inaccessible 65 units. Data distributions from the sample of 30 riser joints was used to run Monte Carlo simulations, using transfer equations modelled through a Fast Flexible Filling Design DOE in which the responses were generated through deterministic computer simulations. While the results of the simulations showed that the risk of non-compliance was unacceptable if the system was utilized to its design limits, even a slight reduction of the pressure level in the pipes reduced the risk of non-compliance to acceptable levels.
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Basic Data Analysis and Modeling
Design of Experiments
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Design of Experiments for Complex Biochemical Systems
Wednesday, October 23, 2024
Ped 8
Cell-free expression (CFE) systems are a suite of methods that reconstitute complex cellular functions like transcription, translation, and metabolism outside the confines of a living cell. CFE systems have numerous biotechnological uses in sensing, biomanufacturing, medicine, basic research, and education. Most CFE systems are made from combining cellular lysates with a complex blend of excipients that improve activity. While the number of excipients makes exploring the combinatorial spaces challenging, high-throughput experimentation with acoustic liquid handling makes it feasible to optimize formulations if paired with an appropriate statistical framework. Here we describe our use of design of experiments (DOE) to optimize excipient combinations for specific use cases of CFE. We pair our DOE with functional data analysis (FDA) to collapse activity over time measurements to metrics readily used for analysis. Initial formulation DOE examples range from five to 14 components. We further describe our efforts to push to higher scales, attempting mixture-process DOE designs with as many as 42 components using an experimental set-up that allows for 1,536 formulations to be tested at once.
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Advanced Statistical Modeling
Design of Experiments
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Improve Your Gauge Throughput: Combining Custom DOE with a Gauge R&R
Thursday, October 24, 2024
Executive Briefing Center 9
Improving gauge throughput for production demand while maintaining low measurement variation can be challenging. Often, throughput and measurement variation are competing variables and understanding the relationship between them can be difficult without utilizing a design of experiment (DOE). In this case study, we show how JMP’s Custom DOE platform can be coupled with a measurement systems analysis (gauge repeatability and reproducibility) to increase throughput while maintaining the accuracy and precision of the gauge. Utilizing JMP's DOE platform, the development time was reduced by an estimated 50% as opposed to a one-variable-at-a-time approach. We demonstrate the physical constraints of the gauge and how these constraints are added into the Custom DOE. We then model the DOE results including optimizing the design space and experimentally validating the 60% increase to the gauge throughput.
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Data Exploration and Visualization
Design of Experiments
Quality and Process Engineering
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Using Augment Design to Resolve a DOE Blocking Mistake
Thursday, October 24, 2024
Executive Briefing Center 150
DOE plays an important role in AMAT End-End continuous process, design improvement and optimization. Unfortunately, most DOE practitioners may randomly pick any DOE design with JMP that they are familiar with while lacking a DOE statistics foundation, resulting in poor predictive models. As one of the most powerful Resolution IV algorithms, DSD allows us to study both main and interaction effects of a large number of predictors in a relatively small DOE run size. However, DSD cannot tolerate any Orthogonality Violations such as GRR Noise, SPC Time Noise, Design Constraint or Recursive Stepwise Algorithm. We studied a special case regarding DSD Blocking Design. Instead of assigning the two operation systems as Predictor factor, Blocking factor has been assigned. The DOE experimental operators did not pay full attention and changed the run order, not following the Blocking plan. After completing the first half of nine runs (1st Blocking), the design process owner found Blocking mistakes and stopped the DOE runs immediately, which induced poor design evaluation. After discussion, we found five alternative resolutions to improve the Orthogonal design structure. We conducted detailed Design Diagnostics on each alternative DOE proposal considering the DOE schedule and cost constraints. Among the five proposals, Augment DOE, adding nine new Augment points (all at corners) is the best to recover the most Orthogonal risks at the highest Return of Investment Ratio. Through this DOE Blocking case study, we have further upgraded our JMP DOE knowledge with effective communication through this crisis.
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Basic Data Analysis and Modeling
Design of Experiments
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