The first Direct Solar DC Grinder prototype was built on an Arduino. The target was to prove that variable solar input could power consistent grinding output through control alone, with no battery buffer. The prototype was embarrassingly simple. It also proved the concept in two weeks.
The path from that Arduino to production hardware involved five distinct stages with very different risk profiles. Managing those stages required a framework that could iterate fast at the concept level and gate hard at the commitment level.
The Two-Path Decision
At the start of every hardware project there is a choice. Build on what exists or pursue the approach that is genuinely new. The first path is lower risk and lower differentiation. The second is higher risk and higher potential reward. The choice depends on three things: how well-defined the problem is, how capable the team is at the specific technical challenge, and what failure costs at the current stage.
The framework I use accepts higher technical risk early, when iteration is cheap and the cost of being wrong is low, and constrains risk progressively as the project advances and the cost of reversal increases.
Stages and Gates
The IIL methodology structures hardware development in five stages: Concept validation, Architecture definition, Prototype development, Pre-production, Production. Each transition is a gate.
Within each stage, the process is agile. Short cycles. Build something. Test it. Learn. Adjust and repeat. The gates are where the agile cycle stops and a different review happens: are all layers aligned? Are assumptions from the previous stage confirmed? Is the risk profile for the next stage acceptable?
What the Arduino Stage Is Actually For
The Arduino prototype stage is not about building something polished. It is about answering the highest-risk question in the project as fast as possible. For the solar grinder, that question was whether adaptive motor control could compensate for irradiance variability well enough to produce consistent output. Everything else was irrelevant until that question had an answer.
Two weeks and a handful of components answered the question. Those constraints shaped the architecture definition stage that followed. The architecture stage started with real data instead of assumptions, which is the point. Teams that skip the fast prototype stage go straight to architecture definition betting that their assumptions are correct. When they are not, the cost is an architecture designed around a problem that turned out not to be the real problem.
Documenting Risk Across Stages
At each gate review I maintain a risk register specific to the next stage. Not a generic list of things that could go wrong, but specific assumptions the next stage depends on, each with a method for testing it and a cost estimate for being wrong. This is how you prevent the most common hardware failure mode: a team that commits to production before understanding what the production environment will do to the system.