A comprehensive solution for low-quality preforms and challenging production management.

　　In the production of preforms, especially for large‑size preforms, a particularly vexing challenge arises: quality control. How can we prevent defective preforms from advancing to the next process step? And how can we isolate potentially problematic preforms among millions or even tens of millions of units? Traditionally, this was handled through manual inspection. Today, SPC provides an efficient solution.

　　The aforementioned defective preforms typically arise from changes in the manufacturing process, such as variations in process parameters like temperature, speed, pressure, back pressure, and dwell time. Maintaining stable process parameters is critical to ensuring preform quality, and the foundation for this stability lies in early warning and traceability—functions that can be achieved through SPC.

　　SPC

　　Statistical Process Control (SPC) is a process-control tool that relies on mathematical and statistical methods. It analyzes and evaluates production processes, promptly identifies early signs of systematic factors based on feedback, and implements corrective measures to eliminate their effects, thereby maintaining the process in a controlled state influenced solely by random variations and achieving the goal of quality control.

　　In the preform‑molding domain, SPC enables self‑learning and real‑time monitoring of production processes by leveraging statistical models, helping users promptly identify issues and adjust parameters to ensure product consistency. During discussions with several major domestic PET preform manufacturers, we found that their SPC requirements are exceptionally stringent, primarily because their customers—such as Coca‑Cola, Danone, and Nestlé—demand very high preform quality; any non‑conformance can trigger return disputes. A fully featured SPC software package allows preform suppliers to detect deviations in process parameters and trends early, implement corrective actions and issue alerts, and, in severe cases, initiate machine shutdowns to prevent further scrap. Meanwhile, customers can perform comprehensive trend analysis of time‑series data and alarm logs to pinpoint root causes, intensify inspection of the affected batch, and thereby safeguard product compliance.

　　SPC operating principle

　　Based on the samples collected by the system, a time‑series plot and statistical calculation results are generated. In process control, process parameters fluctuate around the control mean. The horizontal center line in the plot represents the in‑control state and serves as a reference line for plotting data points. When the process is out of control, the mean shifts, indicating an out‑of‑control condition. If the process becomes out of control at one or more of the plotted time points, the resulting variability may be greater.

　　SPC in the Funeng injection molding system. By evaluating metrics such as normal distribution, statistical control status, and process capability, it ensures that products and services consistently meet customer requirements. Moreover, the SPC mathematical model—specifically its out-of-control rules—enables proactive identification of potential product quality issues, helping customers mitigate production risks.

　　Based on international SPC statistical methods, we provide key analytical tools such as trend charts, distribution histograms, X-bar charts, R/S charts, and Cpk (process capability index).

　　SPC in the Funeng Injection Molding System

　　1. Data report analysis function Data report analysis function

　　Real-time comparison and monitoring of historical process‑parameter data, along with the ability to extract and analyze the most recent one to two months of data stored in the historical database in tabular form. The table can display comparisons for up to 2,000 cycles (configurable) across all SPC process parameters, or retrieve historical data from earlier periods—provided sufficient disk space is available, including data from one or two years ago—for analysis. At the top of the table, the current cycle’s SPC process parameters are continuously displayed, including specification limits and Cpk (process capability index), among other information.

　　2. Process Data Overview

　　It can display the upper and lower specification limits, Cpk values, and various alarm‑control settings for all SPC process parameters.

　　3. Historical Data Trend Chart Analysis

　　SPC is closely linked to historical alarm event data. Users can click on any specific time point to view the corresponding changes in SPC process parameters, as well as related alarms and control parameter adjustments, thereby promptly identifying the causes of process parameter variations. Additionally, users can zoom in/out, search forward, or search backward through historical data, facilitating detailed analysis.

　　4. Assess the current production status

　　5. Trace product quality data for a specified time period

　　It can monitor real-time data, store thousands of historical records, and query historical data from the past two years via the database.

　　6. Anticipate potential product quality issues that may arise in the future

　　SPC in Guangdong Funeng’s injection molding system enables customers to achieve consistent product quality and intelligent manufacturing.