Factory control room visualizing manufacturing process data in real-time.

Is Your Manufacturing Process Stable? A Quality-Driven Approach

Nico Varela in Tech & Innovation September 2025 • 4 min read.

"Discover how a new state fluctuation space model can help maintain consistent product quality in complex manufacturing environments."

In the world of manufacturing, maintaining consistent product quality is a never-ending challenge. Modern manufacturing processes often involve multiple operating modes, each with its own set of rules and potential for variability. This complexity makes it difficult to monitor and analyze quality in real-time, increasing the risk of defects and inconsistencies.

Traditional quality control methods, like Hotelling's T² chart, struggle to handle the dynamic nature of multi-mode manufacturing. These methods often fail to account for the non-linear, time-varying characteristics of these processes, leading to inaccurate assessments of product quality.

To address these challenges, a new approach is needed, one that can effectively analyze and monitor quality state stability in multi-mode manufacturing environments. This approach should be able to adapt to different operating modes, identify potential fluctuations, and ultimately ensure consistent product quality.

Introducing the State Fluctuation Space Model

Factory control room visualizing manufacturing process data in real-time.

Researchers have developed a novel framework called the “state fluctuation space model” to tackle the challenges of quality stability analysis in multi-mode manufacturing processes. This model offers a new way to understand and manage the factors that influence product quality, leading to more consistent and reliable results.

The framework operates by breaking down the manufacturing process into smaller, more manageable sub-processes. This allows for a detailed analysis of how different operating modes are formed and how they impact overall stability. By understanding these mechanisms, manufacturers can better identify potential sources of variation and implement targeted control measures.

Sub-Process Division: The entire process is divided into manageable sub-processes for detailed analysis.
Multi-Mode Analysis: Examines how different operating modes form and affect stability.
Fluctuation Space Model: Each mode's quality state is modeled to determine effective fluctuation boundaries.
Deep Learning Integration: Deep Neural Networks (DNN) automatically extract features and recognize mode types.
Stability Monitoring: Selects appropriate models to monitor and analyze process stability.

Once the framework is established, each individual mode's quality state is modeled using a multi-kernel support vector data description (SVDD) method. This method helps to determine the maximum effective fluctuation border for each process state, providing a clear picture of acceptable variation. To further enhance the model, deep neural networks (DNN) are used to automatically extract features and recognize the current mode type. This allows the system to select the appropriate quality fluctuation space model for real-time monitoring and analysis.

Benefits of the New Approach

By implementing this state-driven fluctuation space model, manufacturers can gain a more comprehensive understanding of their processes and identify potential issues before they impact product quality. This proactive approach not only reduces the risk of defects but also enables continuous improvement and optimization of manufacturing operations.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1177/0954408918760903, Alternate LINK

Title: A Quality-Driven Stability Analysis Framework Based On State Fluctuation Space Model For Manufacturing Process

Subject: Industrial and Manufacturing Engineering

Journal: Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering

Publisher: SAGE Publications

Authors: Liping Zhao, Sheng Hu, Yiyong Yao

Published: 2018-03-01

Everything You Need To Know

What is the main challenge the manufacturing industry faces regarding product quality?

The primary challenge in manufacturing is maintaining consistent product quality, especially within modern processes that involve multiple operating modes. These modes introduce complexity and variability, making real-time quality monitoring and analysis difficult, which increases the risk of defects and inconsistencies.

How does the 'state fluctuation space model' improve upon traditional quality control methods?

Traditional methods, such as Hotelling's T² chart, struggle with the dynamic nature of multi-mode manufacturing. The 'state fluctuation space model' addresses this by adapting to different operating modes, identifying potential fluctuations, and ensuring consistent product quality. It contrasts with older methods that fail to account for the non-linear and time-varying characteristics present in complex processes.

In the context of the 'state fluctuation space model', what role do sub-processes and operating modes play?

The framework divides the entire manufacturing process into manageable sub-processes. This detailed breakdown allows for a thorough analysis of how different operating modes are formed and affect overall stability. By understanding these mechanisms, manufacturers can pinpoint sources of variation and implement targeted control measures to enhance product quality.

How does the 'state fluctuation space model' determine acceptable variation within a manufacturing process?

The model uses a multi-kernel support vector data description (SVDD) method to model each individual mode's quality state. This method determines the maximum effective fluctuation border for each process state, providing a clear picture of acceptable variation. This allows manufacturers to understand the boundaries within which a process can operate while maintaining product quality.

What are the benefits of using the 'state fluctuation space model' for manufacturers?

By implementing the 'state fluctuation space model', manufacturers gain a comprehensive understanding of their processes, allowing them to identify potential issues before they affect product quality. This proactive approach reduces the risk of defects and enables continuous improvement and optimization of manufacturing operations. The system uses deep neural networks (DNN) to automatically extract features and recognize the current mode type, improving the efficiency and accuracy of quality control.