---
title: "How Cavity Pressure Supports Autonomous Injection Molding"
id: "12537"
type: "post"
slug: "how-cavity-pressure-supports-autonomous-injection-molding"
published_at: "2026-11-03T14:23:51+00:00"
modified_at: "2026-10-28T15:24:38+00:00"
url: "https://www.plasticsengineering.org/2026/11/how-cavity-pressure-supports-autonomous-injection-molding-012537/"
markdown_url: "https://www.plasticsengineering.org/2026/11/how-cavity-pressure-supports-autonomous-injection-molding-012537.md"
excerpt: "Cavity pressure feedback helps control variation, monitor part quality, and support increasingly autonomous injection molding processes."
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  - "Auxiliaries"
  - "Editor's Choice Technical Paper"
  - "Equipment"
  - "Feeding Systems"
  - "Industry"
  - "Industry 4.0"
  - "Injection Molding"
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  - "Process"
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  - "Silicones"
  - "Software"
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taxonomy_post_tag:
  - "adaptive process control"
  - "autonomous injection molding"
  - "cavity pressure monitoring"
  - "cavity pressure sensors"
  - "CoPilot injection molding"
  - "DECOUPLED MOLDING"
  - "in-mold sensors"
  - "injection molding automation"
  - "injection molding process control"
  - "process variation injection molding"
  - "recycled content injection molding"
  - "smart injection molding"
---

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 » How Cavity Pressure Supports Autonomous Injection Molding

# How Cavity Pressure Supports Autonomous Injection Molding

Cavity pressure feedback provides the real-time process information needed to support adaptive control and the transition towards autonomous injection molding. Courtesy of RJG, Inc.

### Cavity pressure feedback helps control variation, monitor part quality, and support increasingly autonomous injection molding processes.

Injection molding machines can repeat identical settings while the polymer experiences different conditions inside the mold. Variations in viscosity, recycled content, check-ring performance, gate condition, or cooling can alter cavity pressure. This difference between machine repeatability and part repeatability highlights the importance of cavity pressure as a process signal.

**You can also read:** [Digital Process Control Supports Stable Molding of PCR and PIR](https://www.plasticsengineering.org/2026/08/digital-process-control-supports-stable-molding-of-pcr-and-pir-011843/)

RJG has built its processing philosophy around this principle. Founder Rod Groleau began exploring in-mold cavity pressure during the 1960s and introduced the first commercial strain-gage cavity pressure sensor in 1969. Since then, RJG has expanded this approach through advanced sensing, process monitoring, DECOUPLED MOLDING®, CoPilot®, and autonomous process control.

Brandon Birchmeier of RJG Inc. explored the evolution of cavity pressure during “Cavity Pressure: The Sensor That Transformed Injection Molding—and Is Powering Its Autonomous Future” at the **2026 SPE Injection Molding Innovation Summit** in Cleveland. His presentation highlighted how a signal once used primarily to understand conditions inside the mold now provides critical feedback linking part quality, process control, material variation, and increasingly autonomous decision-making.

## **A Signal From Where Quality Happens**

Machine data reflects the operating conditions commanded by the press, while cavity pressure provides direct information about the conditions the polymer experiences inside the mold. By tracking pressure throughout filling, packing, holding, and cooling, the sensor generates a pressure profile that can indicate melt arrival, peak pressure, pressure decay, gate seal, cavity balance, and shot-to-shot variation. Processors can then compare each cycle with a reference profile associated with acceptable part quality.

Sensor location significantly influences the type of process information obtained. RJG recommends selecting the position according to the defect or process variable under evaluation. Sensors placed near the end of fill can help identify short shots and dimensional variation, while sensors closer to the gate provide earlier feedback for process control during packing.

This information also supports automated part containment. CoPilot monitors cavity pressure, compares each cycle with established limits, and identifies suspect shots before they move downstream. CoPilot GO simplifies tool trials and real-time part sorting by capturing cavity pressure data, generating pressure templates, and separating suspect parts without a complex machine interface.

Multiple in-mold sensors integrated into a multicavity injection mold provide localized process data for monitoring and control. Courtesy of [RJG, Inc.](https://rjginc.com/sensors/)

## **From Cavity Pressure to DECOUPLED MOLDING®**

Cavity pressure becomes particularly valuable when it moves beyond process monitoring and contributes directly to process control. In RJG’s DECOUPLED MOLDING® III strategy, the machine fills most of the cavity under velocity control before continuing packing at a controlled velocity. A cavity pressure sensor near the gate monitors pressure inside the mold, and once it reaches the target, CoPilot signals the machine to transition to hold.

This strategy shifts process control from machine-side parameters to cavity conditions. Rather than relying solely on a fixed transfer position or machine pressure value, the system responds directly to the conditions experienced by the polymer inside the mold.

This distinction matters most when material viscosity changes. RJG reports one case in which viscosity varied by 11%, while cavity pressure control maintained the target cavity condition through corresponding machine adjustments. In another example, a DECOUPLED III process reduced downtime from 20 stops to two within 24 hours. Although results depend on the mold, material, machine, and sensor strategy, these examples demonstrate the value of direct cavity feedback.

Comparison of DECOUPLED MOLDING® II and III, illustrating how cavity pressure feedback shifts the process control point closer to the cavity and reduces process variation associated with changes in material viscosity. Courtesy of [RJG, Inc.](https://rjginc.com/cavity-pressure-control/)

## **The Feedback Loop Behind Autonomous Molding**

RJG extends this concept through Autonomous Process Control for CoPilot. The system integrates machine-side data with cavity pressure feedback, monitors alignment with established good-part conditions, and adjusts fill and pack within defined control limits. This approach expands process monitoring. Conventional systems can identify process drift, while autonomous control can detect deviations and support corrective adjustments during production.

Material flexibility provides a strong example. Recycled content, regrind, alternate resin lots, and wider material specifications can introduce viscosity and pressure changes even when machine settings remain unchanged. RJG identifies these materials as key targets for autonomous control because in-mold feedback lets the system respond to actual process variation rather than infer it from machine settings alone.

The same approach can also capture expert molding knowledge. Manufacturers can define the in-mold conditions that produce acceptable parts and set them as a repeatable process target. CoPilot can then monitor the process and respond within defined control limits, reducing dependence on constant technician intervention while keeping experienced processors in control of the strategy itself.

By **[Maria Vargas](https://www.plasticsengineering.org/author/mariavargas/)** | November 3, 2026

##### [Maria Vargas](https://www.plasticsengineering.org/author/mariavargas/)

[+ postsBio ⮌](#)

María José Vargas is a mechanical engineer and MSc candidate in Materials Engineering and Nanotechnology at Politecnico di Milano. Her work focuses on environmental stress cracking in polyethylene, polymer failure behavior, plastics processing, and sustainable polymer applications.

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