Digital illustration of a manufacturing process under a quality control grid.

Is Your Manufacturing Process Stable? A Quality-Driven Approach

Soraya Malik in Tech & Innovation September 2025 • 4 min read.

"Discover how a state fluctuation space model can revolutionize quality control in multi-mode manufacturing, ensuring consistent product excellence."

In the realm of modern manufacturing, achieving consistent product quality is paramount. However, the increasing complexity of industrial processes, often operating in multiple modes due to varying product grades, production rates, or unforeseen disturbances, poses a significant challenge to maintaining stability and reliability.

Traditional quality control methods, such as Hotelling's T² chart, frequently fall short in effectively managing situations where process states exhibit multi-mode, nonlinear, and time-varying characteristics. The formation of a product can be viewed as a process profoundly impacted by state decision variables (process parameters) that exert substantial influence on output quality characteristics, which determines product quality. Fluctuations in these state variables can trigger abnormal changes and subsequent quality issues.

To proactively address these challenges, real-time monitoring and analysis of quality state stability emerge as crucial strategies for ensuring product excellence. A novel approach, known as the state-driven fluctuation space model, holds immense potential for revolutionizing quality stability analysis, particularly in multi-mode manufacturing processes.

What is the State-Driven Fluctuation Space Model?

Digital illustration of a manufacturing process under a quality control grid.

The state-driven fluctuation space model offers a comprehensive framework for analyzing quality stability in manufacturing processes, with a particular focus on addressing the challenges posed by multi-mode operations. This method acknowledges that manufacturing processes often operate in distinct modes, each exhibiting unique characteristics and regularities. By understanding and accounting for these different modes, the model enables more accurate and effective quality control.

The state-driven fluctuation space model consists of four key stages:

Multi-Mode Division: The entire manufacturing process is divided into several sub-processes, followed by the analysis of the multi-mode formation mechanism to establish the stability analysis framework.
Quality State Fluctuation Space Sub-Model Construction: Each single-mode quality state fluctuation space model is constructed using the multi-kernel support vector data description (SVDD) method to determine the maximum effective fluctuation border of the process state.
Mode Recognition with Deep Neural Networks (DNN): The DNN is used to automatically extract process state features and recognize the mode type for the current process state.
Quality State Stability Analysis: By selecting an appropriate quality stable fluctuation space model, the process stability state is monitored and analyzed to realize quality stability analysis.

A critical aspect of this model lies in the construction of quality state fluctuation space models for each mode. These models leverage the multi-kernel support vector data description (SVDD) method to define the maximum effective fluctuation boundary for each process state. By employing deep neural networks (DNN), the model can automatically extract relevant features and accurately recognize the current mode of operation. This information is then used to select the appropriate quality stable fluctuation space model, enabling effective monitoring and analysis of the process stability state.

The Future of Quality Control

The state-driven fluctuation space model represents a significant advancement in quality control methodologies, offering a more robust and adaptive approach to managing the complexities of modern manufacturing processes. By embracing this innovative framework, manufacturers can unlock new levels of quality assurance, reduce waste, and enhance overall operational efficiency. This research paves the way for future explorations into integrating factors like equipment state and environmental conditions for even more comprehensive and dynamic feedback control systems.

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Everything You Need To Know

What is the primary challenge the state-driven fluctuation space model addresses in manufacturing?

The primary challenge addressed by the state-driven fluctuation space model is ensuring consistent product quality in complex manufacturing environments, particularly those operating in multi-mode scenarios. This model tackles the difficulties arising from varying product grades, production rates, and unforeseen disturbances that can lead to fluctuations in process parameters and subsequent quality issues. By focusing on real-time monitoring and analysis of quality state stability, it aims to proactively identify and address these fluctuations, thereby maintaining product excellence and minimizing waste.

How does the state-driven fluctuation space model differ from traditional quality control methods?

The state-driven fluctuation space model distinguishes itself from traditional quality control methods, such as Hotelling's T² chart, by its ability to effectively manage multi-mode, nonlinear, and time-varying characteristics of manufacturing processes. Traditional methods often struggle in these complex scenarios. The new model, on the other hand, is designed to understand and account for the distinct modes of operation within a manufacturing process, allowing for more accurate and effective quality control. It leverages techniques like multi-kernel support vector data description (SVDD) and deep neural networks (DNN) to adapt to the dynamic nature of modern manufacturing, offering a more robust and adaptive approach to quality assurance.

Can you explain the four key stages of the state-driven fluctuation space model?

The state-driven fluctuation space model consists of four key stages. First is Multi-Mode Division, which involves dividing the manufacturing process into sub-processes and analyzing the multi-mode formation mechanism to establish a stability analysis framework. Second is Quality State Fluctuation Space Sub-Model Construction, where individual fluctuation space models are built for each mode using the multi-kernel support vector data description (SVDD) method to define the maximum effective fluctuation boundaries. Third is Mode Recognition with Deep Neural Networks (DNN), using DNNs to automatically extract process state features and identify the current mode of operation. Finally, Quality State Stability Analysis, where the appropriate fluctuation space model is selected to monitor and analyze process stability.

What role do Deep Neural Networks (DNN) play in the state-driven fluctuation space model?

Deep Neural Networks (DNN) are crucial in the state-driven fluctuation space model for automated feature extraction and mode recognition. In the third stage, DNNs are employed to automatically extract relevant features from the process state data, enabling accurate identification of the current mode of operation. This information is vital for selecting the appropriate quality stable fluctuation space model, ensuring effective monitoring and analysis of process stability. The use of DNNs allows the model to adapt to the complexities of multi-mode manufacturing processes and enhance the accuracy of quality control.

How does the state-driven fluctuation space model contribute to the future of quality control, and what further advancements are anticipated?

The state-driven fluctuation space model signifies a major advancement in quality control, providing a more robust and adaptive approach to managing complex manufacturing processes. By embracing this framework, manufacturers can achieve higher levels of quality assurance, reduce waste, and increase overall operational efficiency. Future developments are expected to incorporate additional factors such as equipment state and environmental conditions, leading to even more comprehensive and dynamic feedback control systems. This evolution promises to further refine the ability to maintain consistent product excellence and optimize manufacturing operations in the face of increasing complexity.