Notes fromthe pour floor.

Engineering, metallurgy, safety and industry writing from the team building the autonomous operations layer for metal casting. Long, specific and occasionally uncomfortable.

[PLACEHOLDER] Article summaries are illustrative of Smelteon's editorial direction.

9Articles
6Topics
WeeklyCadence

Why soundness hasto be predictedbefore the pour.

Everything in a foundry is inspected after the value has been added. By the time an X-ray finds shrinkage porosity in a structural casting, the plant has already spent the melt energy, the mold, the pour, the cooling time and often the machining. We argue the only economically sane place to decide a casting's fate is before metal enters the mold — and lay out what a plant needs to get there: live melt and mold state, a fill and solidification model fast enough to run with the line, and a control path that can act on the answer.

  • Inspection is a verdict, not a control input
  • A twin that runs offline cannot change today's pour
  • Prediction is only useful if something can act on it
  • Attribution turns every grading into a training label

Everything wehave published.

Engineering · 9 min read

Why soundness has to be predicted before the pour

Inspection tells you what happened after the value was added. We argue the only economically sane place to decide a casting's fate is before metal enters the mold — and what it takes to get there.

Engineering

Industry · 7 min read

The retiring pourer problem

Melting, molding and pouring are craft skills held in individual heads. What happens to a plant's process capability when three people retire in the same year — and what a model can and cannot inherit.

Industry

Safety · 11 min read

Bounded write-back: how an agent earns a setpoint

Shadow, assist, bounded. The three gates between a model and a furnace, why each exists, and what has to be true before a plant should let software move anything.

Safety

Quality · 8 min read

Attribution beats detection

Finding porosity is table stakes. Attributing it to a melt, mold, gating, pour or cooling condition is what turns an inspection result into a process change — and a training label.

Quality

Simulation · 10 min read

What a foundry digital twin has to do to be useful

Offline DOE simulation is a design-time tool. A twin that runs with the line has different requirements: speed, live state, and a promotion path into the cell plan.

Simulation

Architecture · 9 min read

Federated learning without giving away the recipe

How defect and process-window priors can transfer between plants while alloy recipes, gating designs and customer programs never leave a tenant boundary.

Architecture

Industry · 6 min read

Megacastings concentrate risk into a single pour

EV structural castings put an enormous amount of value into one fill event. Why that changes the economics of prediction, and why sampling inspection stops being sufficient.

Industry

Quality · 7 min read

The audit log is the product

Autonomy in a regulated manufacturing process is only as good as the evidence it produces. Designing an assurance-grade action log that an IATF 16949 auditor accepts.

Quality

Operations · 8 min read

Measuring melt energy per tonne, honestly

Energy is one of the largest controllable costs in a foundry and one of the worst-measured. A practical method for baselining it at the heat level before claiming any improvement.

Operations

What we writeabout.

Metallurgy

Melt chemistry, inoculation and fade, sand and core behaviour, gating and solidification — the physics the agents have to respect.

Physical AI

Edge perception, control loops, model routing and what changes when your model's output moves a machine.

Safety & assurance

Autonomy gates, process windows, rollback design and the evidence model that makes it defensible.

Industry

Labour, lightweighting, megacastings, reshoring and the economics of a modern foundry.

“If your AI cannot explain a casting failure to the metallurgist who poured it, it is not ready to change the pour.”

SmelteonFrom “Attribution beats detection”

We publishspecifics ornothing.

There is enough vague AI-in-manufacturing content in the world. Everything we publish should be usable by a process engineer: real parameters, real failure modes, real measurement methods, and honest labels on anything aspirational.

  • Named parameters, not 'AI-powered optimisation'
  • Measurement method stated before any claimed improvement
  • Aspirational and placeholder content explicitly marked
  • No customer numbers published without written approval

Three runningseries.

Series 01

The closed loop

  • Perceive, plan, act, verify, log, retrain
  • One post per stage of the loop
  • Written for controls and process engineers

Series 02

Defect field guide

  • Gas and shrinkage porosity, inclusions, cold shuts
  • Misruns, hot tears, sand and core defects
  • Cause, signal and control response for each

Series 03

Autonomy in practice

  • Shadow-mode baselining
  • Golden-set construction from plant ground truth
  • Safety review and window authoring

9

Articles published

8 min

Median read time

3

Running series

0

Ghostwritten posts

Foundationalreading.

Safety · 11 min read

Bounded write-back: how an agent earns a setpoint

The three gates between a model and a furnace, and what has to be true before a plant lets software move anything.

Safety

Quality · 7 min read

The audit log is the product

Designing an assurance-grade action log that an IATF 16949 auditor accepts rather than tolerates.

Quality

Architecture · 9 min read

Federated learning without giving away the recipe

Transferring defect and process-window priors between plants while alloy IP never leaves a tenant boundary.

Architecture

Housekeeping.

From foundry practitioners, yes. From content agencies, no. Write to hello@smelteon.com with the specific thing you want to explain.

Please do, with attribution and a link. If you are writing about the closed-loop model or the autonomy gates and want a technical review, we will read your draft.

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