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Basic questions about my Resume. 2. describe how you would reduce the deflection on cantilever beams. 3. How would changing the dimensions of the beam affect its deflection. 4. Do not remember the exact question, but had something to do with Poisson's ratio.
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Manufacturing Engineer

Interviewed at Apple

4.1★
Feb 20, 2016

Basic questions about my Resume. 2. describe how you would reduce the deflection on cantilever beams. 3. How would changing the dimensions of the beam affect its deflection. 4. Do not remember the exact question, but had something to do with Poisson's ratio.

Difference between OFAT and fractional factorial DOE process( situation based), when to use which method, was asked to explain a DOE I conducted in detail and my thought process, which has more bias repetition or replication, example of response bias in a study I did, example of correlation study I did, how do I validate a DOE result( explained with ANOVA, regression model coefficient and R2, R2(adj) diff , VIF), how to conduct gage R&R, ndc in gage R&R, ndc of 1 meaning, if ndc < 5 is my measurement system bad, what does a ndc of < 5 mean for sample size, what is Cp/Cpk, when to use Cpm, asked to choose a process to deep dive based on Cp/Cpk values, when to use Pp/Ppk vs Cp/Cpk, can Cp be (-) ve, why not, experience with reliability testing, experience with surface finish, how to measure quantify surface roughness, cosmetic characteristics experience with a part, when to use t test, Chi square test questions, questions on datum selection, usage of positional/ profile tolerances, what is LMC, MMC modifier, calculating boundary conditions for hole and shaft mating, usage of I-MR chart, when to use X bar and X bar S chart, how to segregate data from a process to root cause common cause vs assignable cause. From projects- Asked about a machine availability improvement project, a project where I improved cycle time for a machine, a tolerance stack up project to come up with spec limits for a process, a DOE project- explain the process, what parameters I took, why, how I decided the parameter values, every nitty gritty, a vision project using a 3d profilometer - how I optimized program, what defects I was looking to catch, an X-Ray algorithm improvement project- asked to explain failure mode, issue with algorithm, how I changed spec, what was the impact on yield, how did I communicate idea to design team, question on part traceability from a project, experience with CMM/ stylus probes, experience with Helium leak test, questions on electrolyte filling process development for battery cells. Since I had experience with data engineering asked a ton of questions on how I set up a parameter, how I categorize as optional/ non optional, how I add constraints for NCs, they were curious on how I made SQL scripts for our process techs for root causing, asked about experience with Tableau dashboards.
avatar

Manufacturing Quality Engineer

Interviewed at Apple

4.1★
Sep 4, 2022

Difference between OFAT and fractional factorial DOE process( situation based), when to use which method, was asked to explain a DOE I conducted in detail and my thought process, which has more bias repetition or replication, example of response bias in a study I did, example of correlation study I did, how do I validate a DOE result( explained with ANOVA, regression model coefficient and R2, R2(adj) diff , VIF), how to conduct gage R&R, ndc in gage R&R, ndc of 1 meaning, if ndc < 5 is my measurement system bad, what does a ndc of < 5 mean for sample size, what is Cp/Cpk, when to use Cpm, asked to choose a process to deep dive based on Cp/Cpk values, when to use Pp/Ppk vs Cp/Cpk, can Cp be (-) ve, why not, experience with reliability testing, experience with surface finish, how to measure quantify surface roughness, cosmetic characteristics experience with a part, when to use t test, Chi square test questions, questions on datum selection, usage of positional/ profile tolerances, what is LMC, MMC modifier, calculating boundary conditions for hole and shaft mating, usage of I-MR chart, when to use X bar and X bar S chart, how to segregate data from a process to root cause common cause vs assignable cause. From projects- Asked about a machine availability improvement project, a project where I improved cycle time for a machine, a tolerance stack up project to come up with spec limits for a process, a DOE project- explain the process, what parameters I took, why, how I decided the parameter values, every nitty gritty, a vision project using a 3d profilometer - how I optimized program, what defects I was looking to catch, an X-Ray algorithm improvement project- asked to explain failure mode, issue with algorithm, how I changed spec, what was the impact on yield, how did I communicate idea to design team, question on part traceability from a project, experience with CMM/ stylus probes, experience with Helium leak test, questions on electrolyte filling process development for battery cells. Since I had experience with data engineering asked a ton of questions on how I set up a parameter, how I categorize as optional/ non optional, how I add constraints for NCs, they were curious on how I made SQL scripts for our process techs for root causing, asked about experience with Tableau dashboards.

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