Alex Yamada: Manufacturing Quality Engineer at Apple in Sunnyvale, California

• Optimized SolidWorks CAD models for high volume manufacturing capability through conducting root cause analyses, CAM/FEA simulations, and statistical tolerance analyses reducing the printing time of chassis models by 12% and assembly time by 25%- Conducted FEA stress and strain simulations to identify extraneous structures within the sensor assembly and eliminate them which significantly reduced additional support material generation• Slashed production costs by 5% through implementing innovative woodworking techniques such as dovetails and geometric interlocks to 3D printed assemblies which eliminated the need for adhesives and fasteners in the manufacturing and post-processing stages of final product assembly• Led the planning and construction efforts of the company’s auxiliary additive manufacturing laboratory facility through developing 2D floor plans using AutoCAD software- Designed a ventilation system using a modified fume hood and carbon filter setup to properly vent out toxic fumes from resin curing machinery• Implemented Fusion360’s novel Generative Design software to apply physical parameter constraints and load forces to the model before generating an optimized design resulting in efficient, cost-effective geometric structures without compromising the integrity of the model

• Designed parts using PTC Creo 6.0 for consumer electronic products employing lean manufacturing, human factors, and design thinking techniques to achieve quality and safety standards and consumer-based requirements• Modified CAD designs for high volume manufacturing capability using data from Autodesk Moldflow software to minimize production errors through large scale factory injection molding of chassis assembly parts• Presented technical design updates to multiple internal organizational teams and external partners addressing both technically-driven and market validation based design alterations• Developed 3D chassis models to securely house the company's novel thermoelectric circuit and created 2D shop drawings to communicate dimensioning and tolerancing requirements with machining and fabrication shop staff

• Created SolidWorks CAD models and assemblies, researched material properties for testing, and conducted failure analysis investigations of field-tested prototype modules for the deployment of a sensor network• Applied techniques such as datum establishment and fit type analysis (ie. RC, LC, LN) to reduce assembly failures, specifically regarding silicone o-ring sealed sensor housings, when switching between fused deposition modeling (FDM) and stereolithography (SLA) 3D printers- Analyzed the printing resolutions of printer types and calculated the maximum potential error and plotted this against a normal distribution using MATLAB to identify what dimensions could be utilized for the O-ring seal grooves on both printers

• Developed an on-board electronic system and versatile slide-track rail payload system to create a semi-autonomous kayak able to conduct long-term data collection and chemical analyses accounting for weatherproofing, durability, vibration, and transportation- Planned and wired the layout of the electronics system ensuring that the system could withstand the max current draw from the motors and would not fail even if the vehicle capsized- Machined out a slide-track rail on both sides of the kayak enabling for quick and easy attachment/detachment of payloads for transportation

• Assisted a graduate mechanical engineering student conduct experimental tests on the effectiveness of pneumatic air layer drag reduction and super-hydrophobic chemical coatings on reducing the amount of drag experienced by the hulls of marine vessels

• Developed SolidWorks/ANSYS FEA simulations for vehicle hydrodynamics in freshwater, heat transfer within the electronics housing, and stress analysis of motor mounting brackets and machined chassis frame brackets• Managed the 40+ member organization by facilitating weekly design discussions, defining critical process parameters, and prioritizing tasks by urgency to ensure successful vehicle manufacturing and testing results• Designed, manufactured, and tested pressure vessels, pneumatic ballast/actuator systems, and vehicle chassis for the $15,000 autonomous vehicle, and managed the integration of hardware component designs via CAD modeling• Collaborated with software team to develop a dynamic simulator model in Gazebo through determining component material parameters (ie. buoyant force, density, mass, etc), assigning model linkages in Blender, and applying high resolution meshes from SolidWorks• Led multiple technical workshops teaching new members concepts such as the fundamentals of additive manufacturing, rapid prototyping, geometric tolerancing, propulsion systems, and SolidWorks FEA simulations.• Ensured all components have material properties that are inexpensive, corrosion resistant, and waterproof and developed effective alternatives to costly commercial products• Ensured strict organizational deadlines were adhered to, while at the same time, maintaining the flexibility necessary for changes in the event of unexpected obstacles throughout the build process• Maintained constant communication between the members and project management team over Slack and Trello and managed updates on a shared Gantt Chart• Organization Website: https://urobotics.berkeley.edu/

• Created a micro-work class ROV capable of object retrieval/deployment, variable ballast, dimension/depth/temperature measurements, sediment collection, bolt installation, valve operation, and various other tasks

• 2017: Developed an inexpensive robot kit which students around the world could utilize in order to study water quality conditions within their local communities- Devised waterproofing methods for web-cameras, motors, and laser-measurement systems to create a $150 underwater robot able to collect precise water and sediment samples at specific depths up to 20m- Awarded 3rd in Engineering Mechanics Category• 2015: Conducted drag reduction research on submersible vehicles via external surface textures and thruster placement optimization