AI-Driven Intelligent Management and Regulation of Mold Temperature in Smart Manufacturing

Artificial intelligence (AI) is widely used across industries, and this article focuses on a niche yet hard‑core application: AI in intelligent manufacturing for mold temperature management and regulation.

The presentation is organized into three parts: background introduction, solution overview, and case demonstration.

In the manufacturing B2B context, AI can enhance safety, efficiency, and quality, but traditional approaches rely on manual point measurements and adjustments, which are time‑consuming and incomplete.

The key challenges addressed are (1) intelligent detection and management of temperature across all mold regions, (2) intelligent control of cooling‑liquid flow, and (3) comprehensive quality inspection of finished workpieces.

Traditional methods suffer from limited coverage—only a few points are monitored based on experience, and hundreds of cooling pipes must be manually adjusted, leading to inefficiency and risk.

The proposed solution combines imaging, automation, and chemical‑engineering principles to achieve automatic temperature‑flow management. It consists of three main components: mold temperature detection, cooling‑flow control, and an overall workflow that links the two.

For temperature detection, infrared imaging and machine‑vision techniques monitor the entire mold surface. Abnormal images are identified using GAN‑based anomaly detection, Fourier‑transform analysis, and pixel‑value thresholds to filter out blurred, occluded, or over‑exposed frames.

When a temperature alarm is triggered, the system maps the problematic region to the corresponding cooling pipe and adjusts the flow. The adjustment follows a PID feedback loop, gradually changing the flow to avoid abrupt changes that could jeopardize production stability.

A full‑process system pre‑warning mechanism ensures continuous health checks among the server (model deployment), temperature monitoring subsystem, and cooling subsystem, exchanging heartbeat signals and validating control ranges to maintain safety.

The case study demonstrates deployment in an automotive factory, where the system sits between the mold‑temperature and cooling subsystems. It receives temperature data, computes the required cooling‑liquid flow, sends commands to the flow control unit, and closes the loop while adhering to the three core principles of safety, efficiency, and quality.

Overall, the AI‑driven approach provides a comprehensive, automated solution for intelligent mold temperature management, reducing manual labor and improving production reliability.