Ideas exploration

Industrial waxing processes in manufacturing environments like Siemens require repetitive, precise handling of components — picking pieces from a conveyor, dipping them into wax at a controlled depth and speed, retrieving them, and placing them on a cooling conveyor. This process is currently labor-intensive and inconsistent, exposing workers to heat and fumes while introducing variability in coating quality. This project proposes a robotic arm system that fully automates the waxing cycle, integrating with existing conveyor infrastructure to perform pick, dip, retrieve, and place operations with repeatable precision, including PID-controlled dip speed and hold time to ensure consistent coating thickness across cycles. 

• Detect incoming pieces on the input conveyor belt using a proximity or optical sensor
• Pick up the piece using a pneumatic or servo-driven gripper
• Move the piece into the wax bath with PID-controlled dip speed and hold time for consistent coating
• Monitor and maintain dip parameters (depth, hold duration) via closed-loop feedback
• Retrieve the piece and enforce a controlled drip interval before transfer
• Place the waxed piece onto the output conveyor belt for cooling
• Log cycle counts, dip parameters, and flag fault conditions (missed pick, empty conveyor, etc.)

• The system will be designed for a single, fixed piece geometry — no adaptive gripping for variable shapes
• Wax bath temperature regulation is assumed to be handled externally; the system only interfaces with an already-heated bath
• Conveyor speed will be fixed and manually set, not dynamically adjusted by the system
• The prototype will operate on a benchtop-scale mock conveyor, not full industrial hardware
• No vision system — piece detection will rely on simple discrete sensors, not computer vision

Our custom waxing automation system is designed specifically for pick‑dip‑place wax coating, which makes it much easier to deploy than general‑purpose industrial robots. It includes built‑in closed‑loop dip control (PID‑regulated depth, speed, and hold time) and is already designed around conveyor integration, so there is no need for extra modules or complex setup. Programming complexity is low because the system runs a fixed, task‑specific routine rather than a fully flexible robot program, which is ideal for a student or prototype environment. In contrast, options such as the Universal Robots UR3e, Epson T3 SCARA, and Yaskawa MotoMini require additional programming and hardware to achieve similar functionality, and they typically cost in the tens of thousands of dollars. As a result, this project offers the core features needed for reliable waxing automation at a prototype‑level budget and is suitable for student builders, whereas the comparable industrial solutions are more expensive, more complex, and not aimed at educational use. 

Existing security storage solutions force users to choose between inexpensive but static lock boxes and expensive full safe systems with no room for growth. This project proposes a modular locking cabinet system inspired by IKEA's EKET line, where a single master unit — housing the access control interface, authentication logic, and bus controller — governs an expandable chain of low-cost slave modules, each containing only a locking actuator and a minimal MCU. The bus is treated as untrusted by design: all inter-module communication is encrypted and protected against replay attacks using a rolling counter or nonce scheme, so that a device physically tapping into the bus cannot spoof commands or trigger unlocks. New slave modules enroll automatically on physical connection by authenticating against the master using a pre-provisioned shared secret, requiring no user configuration beyond plugging them in.

• Master unit authenticates users via RFID, PIN, or both, and issues encrypted unlock commands to target modules
• All bus communication is encrypted with replay attack protection (nonce or rolling counter)
• New modules are automatically detected and enrolled on connection; unauthenticated devices are rejected
• Each slave module locks and unlocks independently via a solenoid or servo latch
• System logs all access events (who opened which module, at what time) stored on the master
• Supports per-module permission configuration (user A can access modules 1 and 3, not module 2)
• Alerts on unauthorized access attempts or unrecognized bus activity

• The prototype will demonstrate a stack of 2–3 slave modules maximum
• Enclosure will be constructed from wood or acrylic — the project's focus is the protocol and security architecture, not physical tamper resistance
• No remote access or cloud connectivity — all access control and logging are local to the master
• Slave modules use pre-provisioned shared secrets; a full public-key infrastructure is out of scope
• User management is handled through a simple keypad or serial interface, not a dedicated app

This project provides a modular, expandable security system that can grow over time, unlike typical consumer safes and locks that are fixed single units. Each module connects over an encrypted bus with replay protection and supports secure enrollment, so new devices can be added without weakening overall security. The system also supports per‑module access logging and fine‑grained permissions, features that basic padlocks and many home safes do not offer or only implement through proprietary apps. Because it is designed around a prototype‑level budget rather than a fixed commercial product, it can be expanded without replacing the entire system while keeping hardware costs closer to low‑end consumer devices than to high‑end safes. 

Forklift operators face severely limited forward visibility when loads are elevated or bulky, creating dangerous blind zones where pedestrians can go undetected. This project proposes a retrofittable sensor kit that attaches to the front of a forklift and uses a TF-Mini Plus LiDAR to continuously scan the forward zone and trigger local audible and visual alarms when a person or obstacle is detected within a configurable safety distance — with all real-time behavior running deterministically on a dedicated STM32 microcontroller, entirely independent of any network. The system also incorporates an IMU that logs timestamped motion data using adaptive rate logging — low rate during normal operation, high rate during triggered events — functioning as a lightweight black box for incident investigation. As a stretch goal, a secondary ESP32 co-processor receives log data over UART and forwards it wirelessly to a supervisor dashboard, providing fleet-level visibility without ever touching the safety-critical alarm path. 

• Master unit authenticates users via RFID, PIN, or both, and issues encrypted unlock commands to target modules
• All bus communication is encrypted with replay attack protection (nonce or rolling counter)
• New modules are automatically detected and enrolled on connection; unauthenticated devices are rejected
• Each slave module locks and unlocks independently via a solenoid or servo latch
• System logs all access events (who opened which module, at what time) stored on the master
• Supports per-module permission configuration (user A can access modules 1 and 3, not module 2)
• Alerts on unauthorized access attempts or unrecognized bus activity

• The system is advisory only — no interface with the forklift's drivetrain or automatic braking
• The kit is fully self-contained and does not connect to any forklift electrical or data systems
• Person detection is proximity-based; the system cannot distinguish a person from a static obstacle
• Sensor coverage is forward-facing only; side and rear blind spots are out of scope
• IMU log stores raw data only; post-processing and report generation are out of scope
• Wireless dashboard is a stretch goal — the core system is fully functional without it
• Prototype will be mounted on a representative mock chassis, not a certified forklift

This project provides a retrofittable safety system for industrial vehicles that focuses on affordable proximity detection and rich incident data rather than expensive integrated braking packages. It uses ultrasonic and LiDAR sensing to detect nearby obstacles and logs IMU data and incident events in a built‑in “black box,” giving operators and safety managers better insight into how and when near‑misses occur. Unlike many commercial fork truck safety scanners and camera systems, it does not require complex integration to existing braking systems, but it still provides audible and visual alarms to alert drivers in real time. The hardware is designed for a prototype‑level budget and is suitable for a student build, while commercial systems can cost thousands of dollars and are not meant to be modified or extended.