A dairy parlor is real automation - vacuum control, endpoint detection, CIP sequencing, washdown-proof sensors - wrapped around a process variable with legs and opinions. A controls tour of the parlor.

Pet food, breakfast cereal, snack puffs, and plastic pellets are the same machine wearing different paint. A controls tour of extrusion - and why the money is made or lost in the transitions.

"Where did this one come from?" means something different in a produce plant, a semiconductor line, and a diagnostics factory. The architecture is the same four verbs - what changes is the granularity, the mark, and who audits you.

We stopped arguing about CAD packages and counted our own server instead. A quarter-million drawings later, the answer is clearer than any forum thread - and it is two packages, not one.

They are all torque-witness and tamper-evidence tools, and each one has a rightful place on an industrial machine. How to mark, seal, and know when somebody touched what they should not have.

One person at the HMI, one at the camera. The real reason has nothing to do with speed - it is about what happens to focus, aim, and honesty when the person adjusting the lens is also the person judging the image.

Free-running cameras and software triggers promise a vision system with no encoder and no photoeye. Here is where that actually works, where it quietly fails, and the rule of thumb we use before wiring anything.

Cognex, Keyence, Halcon, Matrox, SICK, Banner, Omron, Beckhoff, OpenCV... why a dozen vision software brands exist, what a license actually buys, and where free OpenCV plus AI honestly wins.

The lesson two decades of machine vision keeps teaching: if the lighting is not perfect, you chase it forever with mechanical and software patches. Three machines that proved it - an 8-foot linelight that gets brighter at the ends, overdriven LEDs on white-on-white membrane, and a can line where we iterated the light instead of the code.

The California Farm Bureau's weekly wrote about our ag division's machines: the five-robot onion topping-and-tailing line and an AI-vision jalapeno de-stemmer.

The most-watched clips from our shop floor - washdown robots at work, in-house test runs, a window display for Old Town Auburn, and one very good dog.

25 article teasers on the bench right now - vision installs, lighting, torque witness marks, casino-town commissioning runs, a backwards-milled California, and how AI might find Black Barts treasure. Tell us which to write first.

Electronics recycling has a sorting problem that classical machine vision cannot touch. The product arriving on the belt is not a product - it is everything the industry shipped over a decade, coming back scuffed, cracked, missing parts, and in every possible orientation. We built a system for an electronics recycling operation that identifies items on a moving conveyor with a deep-learning classifier and then tells the operators what it found in the most direct way we could

A high-payload robot with an 11.5 HP spindle, a granite-backed vacuum table, line-scan inspection, and the dust, static, and handling lessons that came with machining hundred-inch acrylic castings.

A dried fruit processor dries tomato halves on large trays, cut side up. Halves that land cup down or on edge do not dry properly, so somebody has to walk the line and flip them by hand. The plan was a robot flipper - but before anyone builds a robot line, there is a cheaper question to answer: can a camera even tell which way a tomato half is sitting? This is the story of how we scoped and built that prototype, and why we think feasibility prototypes are a discipline of thei

This week we ran an experiment on our own back catalog: could we harvest real, fab-proven part footprints out of ten-plus years of panel layout drawings, automatically? The answer was yes - 72 drawings in, over a thousand unique blocks out, and our parts geometry library tripled in two days. Here is what worked and what bit us, because none of it is in any manual. Your old drawings are a dataset If your drafters ever named footprint blocks after the catalog part number - and

This is the anatomy of a web inspection system we built for a medical device line running adhesive product on a carrier web at 800 feet per minute - every major component, why it was chosen, and where to learn more about each one. The customer details stay private; the engineering does not. The problem shape Small repeating products on a fast-moving web, defects invisible under white light, and a requirement to inspect both sides without slowing the line. Manual inspection wa

Onions are a miserable product to automate. No two are the same size, the same shape, or even close to the same orientation when they arrive. The skin flakes, the roots trail dirt, and everything in the room gets hosed down at the end of the shift. This is the story of a machine we built for an onion processing facility that tops and tails onions robotically - the kind of job that has historically been done by a row of people with knives. The problem shape The facility needed