' Portable Radiation Detector - Sara Crogh
Product Strategy · Requirements · Execution

Portable Radiation Detector

A 4-month project scoped and delivered from concept to functional prototype: PM2.5, environmental sensing (temperature, humidity, pressure), GPS, and a portable case—planned, prioritized, and iterated on a Raspberry Pi platform.

Product Vision & Execution Strategy

Goal: Define a compact, portable detector that combines radiation-adjacent learning with multi-sensor environmental monitoring in a single build, using accessible components and clear success criteria.

Approach: Framed the work as two streams—software/data (programming, sensor interfaces, statistics) and mechanical (case CAD and manufacturing). Sequenced the roadmap: research → modeling/validation → hardware feasibility → software DAQ → case design & manufacturing.

Outcome: A working prototype integrating PM2.5, BME680 environmental sensing, GPS, and a 3D-printed case with a laser-cut top. Decisions were documented, trade-offs were explicit, and each iteration tied back to requirements.

Internal product architecture
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Requirements & Scoped Features

Multi-Sensor Scope

Prioritized PM2.5, BME680 (temperature, humidity, pressure, gas), and GPS to maximize learning value and coverage while keeping the build feasible on Raspberry Pi.

Validation via Statistics

Planned a data-quality approach using counting statistics (Poisson) and comparison of datasets (e.g., indoor vs. outdoor) to benchmark signal reliability.

Electronics Feasibility

Structured circuit exploration (RC, PIN diode, step-up, signal amplification) and analog/digital signal handling as milestones to de-risk integration.

Case Usability & Fit

Defined constraints for screen, battery, and Raspberry Pi; used these as non-negotiables to guide CAD, layout, and future manufacturability decisions.

Execution Plan & Milestones

1

Research & Requirement Setting

Studied radiation concepts (activity, decay types/chains) and set initial scope. Selected Raspberry Pi and mapped target sensor interfaces.

2

Modeling & Data Benchmarks

Defined a validation plan using stochastic processes and Poisson statistics; compared datasets (indoor vs. outdoor) to establish baselines.

3

Hardware Feasibility

Sequenced component ordering and circuit exploration (RC, PIN diode, step-up, amplification) to reduce integration risk before full stack assembly.

4

Software & DAQ

Planned and implemented a Python-based DAQ path on Raspberry Pi; organized code and version control to compare and converge on the final approach.

5

Case Design & Manufacturing

Guided CAD around fixed dimensions and assembly flow; executed a 3D-printed body with a laser-cut plywood top, iterating for fit and accessibility.

System Components

Raspberry Pi 4

Processing and data acquisition hub

BME680 Sensor

Temperature, humidity, pressure, gas

PM2.5 Monitor

Air-quality particulate readings

GPS Module

Location tagging for measurements

Python DAQ

Sensor interfacing and data handling

3D-Printed Housing

Custom enclosure with laser-cut top

Build Photos & Renders

Assembled radiation case Case blueprint CAD model Case side view Hand wiring progress Board and sensor stack

Risks, Constraints & Iterations

Fit & Access Risks: The screen initially exceeded the case spec; PM2.5 placement blocked USB access; the magnetic lid misaligned. I captured these as high-impact issues and set dimensional constraints for the screen, battery, and Raspberry Pi to guide revisions.

Trade-offs & Decisions: Explored hinge options and refined layout to protect the screen and improve access. Printer size limits required splitting the body for print—this informed the assembly plan and future manufacturing considerations.

Thermals & Layout: Added ventilation/fan mounting holes and re-oriented the PM2.5 sensor vertically to avoid port conflicts while maintaining function.

Looking Ahead: For scale, injection molding would remove the print split and enable thinner walls. Future compartments for electronics would further improve serviceability.

Product Management Takeaways

Roadmap by Risk

Sequencing research → modeling → feasibility → DAQ → case design reduced unknowns and made decisions traceable to requirements.

Data-Informed Decisions

Poisson-based validation and dataset comparisons set practical accuracy expectations and guided iteration priorities.

Integration Planning

Explicit constraints for screen, battery, and Pi kept CAD and assembly on track and prevented regressions in usability.

Manufacturing Awareness

Printer limits and filament issues shaped the enclosure plan and pointed to injection molding as a future path.

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