Panorama Report on the Application of Data Analysis Technolo

## "Panorama Report on Data Analysis Technology Application of Expansion Machines and Expansion Heads" ### Abstract This paper systematically explores the data collection and analysis technology application of expansion machines and expansion heads in industrial production. By constructing a complete data analysis framework, it elaborates on the data value mining methods for three dimensions: equipment operation parameters, process quality indicators, and energy efficiency management. The article highlights practical application cases of data analysis in process optimization, predictive maintenance, and quality control, and looks forward to the development prospects of cutting-edge technologies such as artificial intelligence and digital twins in this field. Specific suggestions on data security and standardization construction are provided, offering feasible solutions for enterprises to implement digital transformation. **Key Words** Expansion Machine; Expansion Head; Data Analysis; Process Optimization; Predictive Maintenance; Digital Twin; Intelligent Manufacturing ### 1. Construction of the Equipment Data Analysis Framework Modern expansion machine and expansion head data acquisition systems adopt a multi-level architecture design. At the equipment layer, various industrial-grade sensing devices such as pressure sensors (range 0-100 MPa, accuracy ±0.25% FS), displacement sensors (resolution 0.001 mm), and temperature sensors (range 0-300°C) are deployed. These sensors collect key parameters such as hydraulic system pressure, mold displacement, and material temperature in real time at a sampling frequency of 500-1000 Hz. The network layer uses industrial Ethernet and OPC UA protocols to ensure real-time and reliable data transmission. The data acquisition points of a certain model expansion machine typically exceed 200, and a single machine generates data of up to 10 TB per year. The complete data analysis system consists of three core modules: the equipment status monitoring module tracks health indicators such as motor current, oil temperature, and vibration; the process analysis module records process data such as pressure curves and speed curves during each expansion operation; the quality assessment module collects quality parameters such as product size tolerance and wall thickness distribution. By establishing a unified timestamp and batch number association rule, these data form a complete production traceability chain. Studies show that a complete data system can increase the overall equipment efficiency (OEE) by 15%-20%. Data preprocessing adopts a specialized industrial data processing flow. The original data is first filtered using median filtering (window width 7-15 points) to eliminate pulse interference, and then processed using wavelet transform to remove high-frequency noise. The detection of outliers uses an improved isolation forest algorithm with an accuracy rate of over 95%. For missing data, the KNN-based interpolation method is used, which improves the accuracy by 30% compared to traditional linear interpolation. The preprocessed data is stored in a time series database and indexed in multiple dimensions to provide a high-quality data foundation for subsequent analysis. ### 2. Key Data Analysis Application Scenarios In terms of process optimization, a quantitative relationship model between expansion parameters and product quality was established through big data analysis. A case study demonstrated that when the expansion speed is controlled within 0.8-1.2 mm/s, the product roundness error is minimized. Machine learning analysis revealed that the influence weight of mold temperature on wall thickness uniformity is 28%, and the optimal temperature range is 180-220°C. Based on these findings, the process parameters were optimized, and the product qualification rate of a certain enterprise increased from 85% to 97%, with a 25% increase in material utilization. More advanced reinforcement learning algorithms have begun to be used for real-time process adjustments, enabling the equipment to automatically adapt to material performance fluctuations. The predictive maintenance system predicts the lifespan of key components through vibration signal analysis. The wavelet packet decomposition technology can extract 16 frequency band energy features from vibration signals, predicting bearing failures 300 hours in advance with an accuracy rate of 92%. Hydraulic system condition monitoring is achieved through pressure pulsation analysis, triggering an alert when the characteristic frequency amplitude increases by more than 30% compared to the baseline. After the deployment of this system in a certain factory, the rate of sudden equipment failures decreased by 70%, and maintenance costs dropped by 40%. The application of digital twin technology further improved the diagnostic accuracy, with the correlation between the virtual model and the actual equipment's pressure curve reaching 98%. The quality control system enables full-process quality traceability and analysis. Each product carries a unique QR code, linking to the entire process data from raw materials to finished products. Statistical process control (SPC) monitors the CPK values of key dimensions in real time and automatically triggers process adjustments. The machine vision system detects surface defects with a resolution of 0.05mm and a detection speed of 3 seconds per piece. The deep learning algorithm analyzes ultrasonic flaw detection data, with an defect recognition accuracy of 99.2%. After a certain project was implemented, the customer complaint rate decreased by 80%, and quality costs were reduced by 35%. ### III. Integration of Frontier Technologies Digital twin technology has constructed a virtual-real integrated intelligent analysis platform. The digital twin system for the expansion machine developed by an advanced manufacturing enterprise contains complete models of mechanical structure, hydraulic system, and control algorithms, and can simulate the material flow under different processes in real time. When the deviation between actual production data and simulation results exceeds 3%, the system automatically generates optimization suggestions. This technology shortens the new product development cycle by 60% and reduces the trial production cost by 50%. With the improvement of the accuracy of multi-physics field coupling simulation in the future, digital twin will achieve more precise process predictions. Deep learning technology brings revolutionary breakthroughs in analysis methods. Three-dimensional convolutional neural networks process industrial CT data to automatically classify internal defects, with a speed 50 times faster than manual processing. Generative adversarial networks (GAN) simulate the quality distribution under various process conditions to provide massive training data for parameter optimization. Time series prediction models use the long short-term memory network (LSTM) with the Attention mechanism to predict mold life errors within 3%. A certain innovative project used deep reinforcement learning for adaptive control, with the system response time reduced to 100ms level. The edge-cloud collaborative computing architecture optimizes the efficiency of data analysis. Embedded AI chips are deployed on the device end to achieve real-time analysis with a latency of 10ms; deep learning and long-term trend prediction are carried out on the cloud. 5G network ensures real-time data transmission with a latency of 50ms or less. Blockchain technology ensures the immutability of data and has been applied to the supply chain quality traceability system. The data of a distributed analysis platform shows that this architecture reduces bandwidth usage by 70% and increases analysis efficiency by 3 times. ### IV. Implementation Strategies and Suggestions The data security system adopts a hierarchical protection strategy. Industrial firewalls and intrusion detection systems are deployed at the network layer; HSM encryption modules are used at the equipment layer; and attribute-based access control (ABAC) is implemented at the application layer. The TLS1.3 protocol is used for data transmission, and AES-256 encryption is used for storing data. The security practice of a certain enterprise shows that a complete security solution can reduce data leakage risks by 95%. At the same time, data de-identification norms should be established, and core process parameters should be anonymized. Standardization construction includes data interface standards, analysis process standards, and model evaluation standards. It is recommended to refer to international standards such as ISO 23081 and formulate data management norms suitable for the enterprise's actual situation. Talent cultivation should adopt a "process + data" composite training model and establish an internal certification system. A statistical report from an industry alliance shows that standardization construction can increase data sharing efficiency by 60% and improve the consistency of analysis results by 45%. The implementation path recommendation adopts a three-step strategy: the first stage (1-3 months) completes the digital transformation of key equipment and establishes basic analysis capabilities; the second stage (3-6 months) realizes process closed-loop optimization; the third stage (6-12 months) builds an enterprise-level analysis platform. Project management adopts the agile development model, delivering verifiable results every two weeks. A successful case demonstrates that this progressive implementation has increased the return on investment by 40% and significantly enhanced employee acceptance. ### V. Conclusion and Outlook The application of data analysis technology in the expansion machine and expansion head machine fields has achieved remarkable results. Through the mining of equipment data value, enterprises have achieved multiple goals such as process optimization, quality improvement, and cost reduction. Practice shows that the overall equipment efficiency (OEE) of data-driven enterprises can be 15-25 percentage points higher than the industry average, and the product defect rate can be reduced by 30-50%. These achievements fully verify the commercial value of data analysis technology. The future development trend will present three characteristics: in terms of analysis depth, the integration of physical models and data models will produce more interpretable analysis results; in terms of application breadth, data analysis will cover the entire life cycle from equipment to products; in terms of technical height, the combination of artificial intelligence and edge computing will achieve more intelligent real-time decision-making. It is recommended that enterprises incorporate data analysis into the core of their intelligent manufacturing strategies, continuously increase investment, and actively participate in the formulation of industry standards to jointly promote industrial digital transformation. The key success factors for implementing data analysis projects include: strong support from senior management, organizational guarantee for cross-departmental collaboration, a step-by-step technical route, and a continuously optimized talent cultivation mechanism. Through systematic promotion of data analysis applications, the expansion machine and expansion head machine manufacturing fields will undoubtedly usher in a new era of quality transformation and efficiency transformation.