Boston Scientific
Process
Skills Used
Project Management, Design, Communication, Project Scheduling, Time Management, Python, Excel, Data Analysis, Presentation
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As a Process Development intern at Boston Scientific Corporation (BSC) I was given the opportunity to understand one of BSC’s premier product lines. One of the product lines was going through some troubles. The lasers that produced the part would often drift and had to be taken down for hours to recalibrate. Downtime ranged from 10 hours to up to 40 hours to readjust the settings. This resulted in loss of material, engineering time and setbacks in production.
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The laser machines were cutting features into a ¾ inch piece of metal with features sizes in the thousandths of an inch range. The parts being produced were used in one of BSC’s newest product lines.
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An Overview of the Process:
The ¾ inch of metal would be inserted into the laser machine. The settings would be configured, and the laser would begin cutting. After the laser is finished, the part is sent through a cleaning process and afterwards the part is measured on a machine. When the machine is done measuring, the machine prints out all the measurements of the features and then determines whether the part passed or failed. If the part fails it can’t be used. If enough parts fail in one set, then the machine must be taken down for recalibration.
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The Mission
Find a way to reduce the calibration time.
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A Snapshot of the Project
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Conducted research to understand new technologies.
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Worked across departments and managed schedules to get mutual beneficial results.
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Created and executed experiments to test hypotheses and collect data.
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Used Python and Excel to collect, process and analyze data.
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Analyzed a process and took a data driven approach to decrease inefficiencies (brought process down from upwards of 40 hours to 3 hours).
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Tested out solution with senior engineer and validated my proposed solution.
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Coordinated with Senior engineers to conduct testing and small scale validation.
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Journey to Success
My first goal was to familiarize myself with the manufacturing process. I took the code that governed the machines, analyzed it, and did my best to understand what the inputs in the code produced the resulting finished product. From there, I started making a catalogue in Excel of all the passing parts and constantly updated the list during my time at BSC. I did this to understand what range of measurements the laser machine naturally produced when it was functioning properly.
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After I gained a full understanding of the inputs governing the machines, I created an Excel that would be used to monitor my experiments and show the inputs that I would be altering and the resulting features from each part. This list would be compared against the base data I collected from passing parts noted prior. Once I had a full list of the experiments that I wanted to conduct, I spoke to the senior engineer in charge of the manufacturing process, and I discussed my plans with him. I worked with him to schedule time on the machines to conduct my experiments, while still ensuring that manufacturing was still hitting their production goals.
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As I progressed through each experiment, I catalogued the data in Excel. Once I collected the data I needed for my analysis. I imported the data into Python Juptyer Notebooks and began the statistical analysis. I established the passing parts noted prior as my base and used the some of the summary statistics as ranges of normalcy. I then compared that to the experimental data to the baseline data and noted which features on the part moved and how much they moved. I noted correlations or noticeable patterns.
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Once I completed my analysis, I began the next step of creating a hypothetical protocol. Essentially this protocol would tell the engineer what inputs they needed to change in order to get the machine to function in the passing range. I tested this protocol out with a senior engineer and validated that it worked. I then created a report and a presentation that showcased my results for other senior engineers of different departments.
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The Tasks I Undertook
I conducted research to better understand the inefficiencies and problems that were plaguing the manufacturing department. I then was able to create my own experimental setup and strategy to tackle the problem. I used Python and Excel to conduct statistical analysis and propose a solution. I validated my work by testing it with a senior engineer. I presented my results to senior engineers and VPs of Manufacturing.
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Lessons Learned Along The Way
I was able to run my experiments and iterate upon my hypothesis constantly in a professional environment. I enjoyed being given the freedom to run my own experiments and develop the process and procedure myself. I was excited to be faced with a challenge and enjoyed facing that challenge head on and bringing in the skills that I had honed at my university to the table. Overall, my experience gave me a good idea what it's like to do true problem solving in a professional environment and how to iterate and pivot when necessary.
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