Shot-by-shot data is time-series data
Each cycle is a row in the same record, so the models see a molding machine the way they see a paper machine’s sensor stream.
Continuous optimization, shot after shot.
Injection molding repeats every cycle: clamp, fill, pack, cool, eject. The platform learns how your presses run and shows operators where each cycle is heading, before drift becomes a suspect part.
The injection molding problem set
Where injection molding loses margin between shots
Scrap is decided before the part is measured. Melt, mold temperature, cooling, hold pressure and resin lot drift shot to shot, while inspection only samples and the press keeps running. The process lives in people, so results change with the shift. Startups and tool changes concentrate scrap. Cooling is usually the largest part of cycle length, and the least visible.
Why this works in injection molding
A cyclic process with a continuous signal
Injection molding isn’t a continuous web, but every shot logs melt temperature, pressure, cooling and cycle time, roughly 800 tags per machine. The models learn from that record. A mold and job play the role a grade plays on a paper machine.
Mold and job stand in for grade
Targets and limits are defined per mold and job and switch at changeover, the same grade-transition logic proven across 5 to 40+ paper grades.
Scrap is the lab result
Quarantined and scrapped parts are the after-the-fact quality signal the models learn from, the way lab tests are on a paper machine.
Proof from the platform
Measured where the platform runs today
A direct result in injection molding, backed by the same mechanics behind ProcessMiner’s track record elsewhere on the platform.
25%
Scrap-rate reduction
Injection molding, single-machine pilot
>20%
Less quality variability
Pulp & paper deployments
The 25% scrap-rate figure reflects a single-machine ProcessMiner pilot at an anonymized F500 injection molding manufacturer. The quality-variability figure reflects ProcessMiner deployments in pulp & paper, a cross-vertical parallel, not an injection molding result. Results vary by facility, process, and data quality; past performance is not a guarantee of future results.
Free Data Health Assessment
See what the models would find on your line.
Start with a free Data Health Assessment. We’ll take a closer look and let you know exactly what your own data can do.
Needs 6–12 months of process history with timestamped lab results.
Questions, answered
Frequently asked questions
Injection molding isn't continuous. Why does this platform fit?
Because the data is. Every shot logs the same variables, so the models see a time series of cycles the way they see a paper machine’s sensor stream, and the repetition creates continuous optimization opportunities across melt, mold temperature, cooling, and hold pressure.
What did the 25% scrap reduction involve?
A single-machine pilot at an anonymized Fortune 500 injection molder. The platform learned normal cycle behavior from the machine’s own historian, flagged drift while it was still correctable, and pointed to the likely contributors. Scrap fell 25% on that machine; it is a pilot result, not a fleet average.
Does it work across mold changes and resin lots?
Yes. A mold and job are treated the way a grade is on a paper machine: targets and limits switch automatically at a changeover, and PM Vector’s quantified adjustments absorb resin-lot variation instead of passing it downstream to the part.
Can it shorten cycle time?
It targets the cooling phase, usually the largest and least visible contributor to cycle length. By showing where cooling time can come down without moving quality toward a limit, the platform turns cycle-time improvement into evidence rather than one-off tuning.
Get in touch
Have a question?
Whether it's our platform, your process, or a free Data Health Assessment you'd like to start, let us know.