Qiaozhi Wang, P.E.
(Prounouced "Chi-ao-Jhi")
Results-driven Professional Engineer specializing in mechatronics, systems integration, and autonomous systems. Demonstrated expertise leading end-to-end development of complex electro-mechanical systems — from concept design through prototype build, validation, and public debut.
Fastener Picker (Oct 2025 - Jan 2026)
Skills: Next.js, TypeScript, Tauri, JSON-driven architecture, Rule-based decision engine, Offline desktop app
Role: Sole developer - concept to deployment
BACKGROUND
In fast-moving design programs, hardware decisions are often made under time pressure and vary based on individual experience. Fastener selection — though seemingly routine — draws on scattered standards documents, legacy flowcharts, and institutional knowledge that is difficult to apply consistently across a team.
Engineers needed a way to standardize decisions and document rationale without adding process overhead. I built the Fastener Picker to address this directly: a lightweight, offline tool that guides users through a structured selection workflow and generates vendor-ready outputs.
FROM STATIC FLOWCHART TO INTERACTIVE DECISION ENGINE
The starting point was an existing fastener selection flowchart and a set of preferences — valuable knowledge, but locked in document form. I converted this logic into a modular, configuration-driven architecture that separates the decision rules from the interface, making the system easy to maintain and extend.
questions.json — Defines the step-by-step decision flow: single select, multi-select, and free-input question types including "Other (specify)" fields.
rules.json — Maps user responses to derived fastener filters, recommended notes, and "why" explanations — the encoded engineering rationale.
Users answer a short series of guided prompts — covering material type, loading conditions, environment, and assembly constraints — and the tool dynamically produces a set of fastener filters and documented rationale, exportable for direct use with vendors such as McMaster-Carr.
Flowchart
question and rule engines
DESIGN AND USABILITY
The UI was built with Next.js and TypeScript and packaged as a Tauri desktop application — fully offline, with no data leaving the machine. Usability features were added for real engineering workflows:
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Inline notes and free-text "Other (specify)" inputs captured directly into results
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Image reference steps with in-app zoom for visual context
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Results page with a copyable summary and back-navigation for iteration
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Collapsible documentation panel with Read Me, Studio Preferences, and Flowchart reference
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Packaged as a Tauri desktop application for non-technical users — fully offline, with no data leaving the machine
Demo Video
OUTCOMES
The Fastener Picker gives engineers a faster, more consistent path to hardware decisions — one that stays aligned with studio standards regardless of who is doing the selecting. Because engineering rationale is embedded directly in the tool's outputs, the results are transparent and traceable, ready to hand off to vendors like McMaster-Carr without additional documentation work. The modular architecture also means the tool isn't a one-time deliverable: it's designed to grow as standards evolve and expand to cover additional fastener types, environments, and specifications.
MY ROLE
I developed the Fastener Picker independently, from concept through deployment — developing the flowchart, translating existing flowcharts and studio preferences into structured decision logic, designing the configuration-driven architecture that keeps rules separate from the interface, and building the full selection wizard with its usability features. I validated the outputs with engineering stakeholders and packaged the final application for use by non-technical team members.