The manufacturing of machined parts directly determines the performance boundaries of the entire machine, with their production quality and efficiency having a critical impact on industrial competitiveness.
Under traditional manufacturing models, machined part production commonly suffers from unstable precision control, difficult quality traceability, and low utilization of manufacturing resources.
With the deep integration of information technology and manufacturing technology, smart manufacturing offers new pathways to address these challenges.
Through comprehensive data sensing, analysis, and decision-making throughout the entire process, smart manufacturing achieves a shift from passive control to proactive prediction.
Industrial Internet of Things (IIoT) technology enables the comprehensive digitalization of manufacturing processes.
Artificial intelligence algorithms transform data into decision support.
Machine learning methods enable self-optimization of machining parameters.
Digital twin technology assists in the design and machining of complex components.
This study aims to construct a theoretical framework for the intelligent manufacturing of machined parts.
It explores key enabling technologies. The study also quantitatively evaluates application benefits.
These efforts provide a scientific basis for advancing the development of intelligent manufacturing in this field.
Theoretical Framework for Intelligent Manufacturing of Machined Parts
Physical-Information Cognitive Model of Intelligent Manufacturing Systems
The intelligent manufacturing system for machined parts integrates the physical space, information space, and cognitive space.
The physical space includes manufacturing equipment and workpieces.
The information space consists of sensor networks and computing platforms.
The cognitive space enables intelligent decision-making through algorithms.
Data flows interconnect these three domains. Together, they form a closed-loop feedback mechanism.
At the system’s core lies a data-driven intelligent decision-making loop encompassing four stages: data acquisition, state recognition, intelligent analysis, and precise control.
Manufacturing data bridges the three domains. Intelligent algorithms transform this data into the foundation for decision-making.
This process enables the leap from perceptual intelligence to cognitive intelligence.
Parameter Space and Optimization Boundaries in Component Manufacturing
Machining component production involves a multidimensional parameter space encompassing materials, processes, equipment, and environmental factors.
Traditional methods struggle to identify optimal solutions within this high-dimensional parameter space, whereas intelligent manufacturing leverages data-driven algorithms to effectively determine optimal boundaries.
In complex component manufacturing, nonlinear interactions exist between parameters.
Here is your sentence **split into shorter, clear sentences** and converted to **active voice**:
Engineers establish a mapping model between parameters and performance: (P = f(M, T, E, A)).
Here, (P) represents performance metrics, and (M), (T), (E), and (A) denote material, process, equipment, and environmental parameters, respectively.
Intelligent manufacturing systems use this model to achieve adaptive optimization under specified constraints.
I can also **combine this with your previous active-voice sentences** into a polished, cohesive technical paragraph.
Key Technologies for Intelligent Manufacturing of Machined Parts
Deep Learning-Based Geometric Feature Recognition of Components
Accurate recognition of component geometric features forms the foundation of intelligent manufacturing.
Traditional methods rely on manual expertise and struggle with complex shapes.
Deep learning approaches automatically extract feature representations, significantly enhancing recognition accuracy.
Deep convolutional neural networks extract hierarchical features through multi-layer structures.
Residual networks and attention mechanisms enhance feature recognition robustness.
For 3D components, point cloud deep learning directly processes irregular spatial data.
Feature representation mathematically models geometric characteristics, enabling compact feature descriptions that facilitate parametric design and similarity analysis.
Multi-source Heterogeneous Data Fusion for Manufacturing Condition Monitoring
Component manufacturing processes generate diverse data types with varying formats and frequencies.
Multi-source heterogeneous data fusion technology integrates these data streams to build comprehensive condition monitoring systems.
Three primary fusion strategies exist: data-level, feature-level, and decision-level.
Core algorithms for data fusion include Kalman filtering, Bayesian inference, and deep fusion networks.
Different fusion methods suit distinct application scenarios, as shown in Table 1.
| Fusion Method | Computational Complexity | Real-time Performance | Anti-interference Capability | Application Scenario |
|---|---|---|---|---|
| Kalman Filter | Medium | High | Medium | Parameter state estimation |
| Bayesian Inference | High | Medium | High | Fault diagnosis |
| Deep Fusion Network | Extremely High | Low | Extremely High | Complex state recognition |
| D–S Evidence Theory | Medium | Medium | High | Uncertainty handling |
Knowledge Graph-Driven Process Parameter Self-Optimization
Process parameter optimization is critical to component quality. As a knowledge representation framework, knowledge graphs effectively support parameter optimization decisions.
Manufacturing knowledge graphs comprise conceptual, instance, and application layers, enabling formal representation of the relationship between process parameters and component performance.
The knowledge graph-based parameter optimization process involves case retrieval, parameter extraction, correlation analysis, and iterative optimization.
It can also integrate expert knowledge with data-driven methods to efficiently identify optimal parameter combinations.
Reinforcement Learning-Assisted Quality Control System
Reinforcement learning achieves adaptive quality control in manufacturing processes through an “exploration-exploitation” mechanism.
This approach models the manufacturing process as a Markov decision process (including state space, action space, and reward function), continuously optimizing control strategies through interaction with the environment.
The physical-information-cognitive model of the intelligent manufacturing system for machined parts is shown in Figure 1.
This model adjusts process parameters based on historical data and current conditions to prevent quality defects, effectively reduce quality fluctuations, and improve product yield rates.
Experimental Validation of Intelligent Manufacturing System for Machined Parts
Experimental System Construction and Research Methodology
This study constructed a four-layer architecture for an intelligent manufacturing experimental system targeting machined parts.
The layers include the perception layer, network layer, data layer, and application layer.
This architecture enables closed-loop control from data perception to intelligent decision-making.
The researchers employed factor control methods and orthogonal experimental design.
They selected precision shafts, thin-walled housings, and impeller components as research subjects.
The system systematically examined the impact of four critical factors on manufacturing quality: data acquisition frequency, feature extraction algorithms, parameter optimization strategies, and control response time .
Experiments were arranged using an L9(3⁴) orthogonal array, effectively reducing experimental workload while ensuring scientific validity of results.
The experimental design is shown in Table 2. To compare the performance differences between intelligent manufacturing and traditional manufacturing, this study established a parallel comparison experimental group.
Traditional manufacturing used fixed process parameters and relied on offline quality inspection.
In contrast, intelligent manufacturing uses the previously described system.
It adaptively adjusts process parameters and implements online quality closed-loop control.
Each group of experiments was repeated five times. Range analysis and analysis of variance (ANOVA) were used to determine the significance of key technical factors and identify the optimal combination.
| Experiment No. | A: Data Acquisition Frequency /Hz | B: Feature Extraction Algorithm | C: Parameter Optimization Strategy | D: Control Response Time /ms |
|---|---|---|---|---|
| 1 | 100 | Wavelet Transform | Genetic Algorithm | 50 |
| 2 | 100 | Deep Learning | Reinforcement Learning | 25 |
| 3 | 100 | Principal Component Analysis | Gradient Descent | 10 |
| 4 | 500 | Wavelet Transform | Reinforcement Learning | 10 |
| 5 | 500 | Deep Learning | Gradient Descent | 50 |
| 6 | 500 | Principal Component Analysis | Genetic Algorithm | 25 |
| 7 | 1000 | Wavelet Transform | Gradient Descent | 25 |
| 8 | 1000 | Deep Learning | Genetic Algorithm | 10 |
| 9 | 1000 | Principal Component Analysis | Reinforcement Learning | 50 |
Research on Manufacturing Precision and Process Stability
Manufacturing precision and process stability serve as key indicators for evaluating the effectiveness of intelligent manufacturing in components.
This study employs a multidimensional precision evaluation system encompassing dimensional accuracy, geometric accuracy, surface integrity, and microstructural characteristics.
Comprehensive measurement analysis of manufactured components was conducted using precision inspection equipment including coordinate measuring machines, surface roughness testers, roundness gauges, and metallurgical microscopes.
Orthogonal experimental results were analyzed using range analysis to determine the influence of each factor on manufacturing precision.
Findings indicate that for precision shaft components, the feature extraction algorithm (Factor B) and parameter optimization strategy (Factor C) exert the most significant impact on manufacturing precision (P<0.01).
For thin-walled enclosures, data acquisition frequency (Factor A) and control response time (Factor D) demonstrate the most pronounced effects (P<0.01).
For impeller components, all four factors exerted significant influence, with the feature extraction algorithm (Factor B) having the greatest impact.
The optimal technical combination was identified as A3B2C2D3: data acquisition frequency of 1000Hz, feature extraction via deep learning, parameter optimization using reinforcement learning, and control response time of 10ms.
The comparative analysis of component manufacturing precision is presented in Table 3.
As shown in Table 3, the adoption of intelligent manufacturing technology resulted in significant improvements in key precision metrics for all three component categories.
Notably, the flatness of deformation-sensitive thin-walled enclosures improved by as much as 58.3%, attributable to the intelligent manufacturing system’s real-time prediction and compensation of deformation trends during machining.
| Part Type | Evaluation Index | Traditional Manufacturing | Intelligent Manufacturing | Improvement Rate /% | Significance P |
|---|---|---|---|---|---|
| Precision Shaft Parts | Cylindricity /mm | 0.015 ± 0.003 | 0.006 ± 0.001 | 60.0 | <0.01 |
| Coaxiality /mm | 0.022 ± 0.004 | 0.009 ± 0.002 | 59.1 | <0.01 | |
| Surface Roughness Ra /mm | 1.6 ± 0.2 | 0.4 ± 0.1 | 75.0 | <0.01 | |
| Thin-Walled Housing | Flatness /mm | 0.024 ± 0.005 | 0.010 ± 0.002 | 58.3 | <0.01 |
| Parallelism /mm | 0.018 ± 0.003 | 0.007 ± 0.001 | 61.1 | <0.01 | |
| Wall Thickness Accuracy /mm | ± 0.08 | ± 0.03 | 62.5 | <0.01 | |
| Impeller Components | Profile Accuracy /mm | 0.035 ± 0.007 | 0.012 ± 0.003 | 65.7 | <0.01 |
| Flow Passage Consistency /% | 87.5 ± 3.2 | 96.8 ± 1.5 | 10.6 | <0.05 | |
| Blade Thickness Uniformity /% | 85.3 ± 4.1 | 95.7 ± 1.8 | 12.2 | <0.05 |
The study on process stability employed Statistical Process Control (SPC) methods to analyze quality fluctuations within and between batches.
Results indicate that smart manufacturing not only enhances average precision levels but also significantly improves manufacturing process stability.
The process capability index (Cpk) of the smart manufacturing system increased from 1.33 in traditional manufacturing to 1.92, demonstrating greater process stability and closer alignment with design targets.
The inter-batch standard deviation decreased by 56.9%, while the intra-batch standard deviation decreased by 62.3%, proving that smart manufacturing effectively reduces manufacturing fluctuations and improves product consistency.
Spectral and wavelet analyses of manufacturing signals revealed that smart manufacturing technology significantly suppressed random fluctuations and systematic vibrations during machining, particularly in the 40–120 Hz frequency band.
Vibration energy decreased by 67.5%, effectively improving component surface quality and microstructure.
Research on System Adaptability and Robustness
This study also validated the robustness and reliability of the intelligent manufacturing system through interference testing and long-term operational testing.
Under conditions of material fluctuations, environmental changes, and equipment variations, the system maintains stable product quality through adaptive adjustments.
Long-term operational testing demonstrates that the mean time between failures for key functional modules significantly exceeds that of traditional systems, while self-diagnostic and predictive maintenance capabilities effectively reduce downtime.
Analysis of Application Benefits in Intelligent Manufacturing of Machined Parts
Enhanced Precision in Precision Component Manufacturing
The improvement in precision achieved through intelligent manufacturing of machined parts manifests primarily in the following three aspects:
(1) By continuously monitoring physical parameters such as tool/workpiece displacement, cutting force, and thermal deformation, the system constructs a disturbance model driven by the LS-SVM algorithm, enabling sub-micron precision control.
(2) During digital twin-driven operations, the system fuses multi-source sensor data via UKF algorithms to overcome nonlinear and non-Gaussian noise.
This enables real-time optimization of process parameters with response times under 5ms, achieving 2.3 times the control precision of traditional methods.
(3) The system enables end-to-end collaborative optimization across design, process, manufacturing, and inspection, eliminating “information silos.”
Application results demonstrate: – Cylindricity, coaxiality, and surface roughness of precision shaft components improved by 60%, 59.1%, and 75%, respectively;
Thin-walled part deformation control accuracy increased by 62.5%;
Feature recognition accuracy for complex structural components reached 96.8%, achieving significant overall machining precision enhancement.
Production Efficiency and Cost Optimization
The intelligent manufacturing system for machined parts enhances efficiency and reduces costs by optimizing resource allocation.
Based on queueing theory and system dynamics, the system constructs an efficiency-cost coupling model to achieve global optimization.
Regarding production efficiency, the system uses a time management model to reduce processing time through adaptive parameter optimization.
It shortens setup time with rapid changeover technology.It also minimizes idle time using intelligent scheduling algorithms.
Data indicates equipment utilization increased from 68.5% to 91.3%, with idle time reduced by 22.7%.
Regarding costs, the system uses a dynamic control model based on full lifecycle analysis.
It optimizes direct costs, including materials, energy, and labor, as well as indirect costs such as depreciation, maintenance, and inventory.
The system reduced the material loss rate from 28.5% to 9.2%.
It decreased energy consumption by 26.8%.It increased per-capita efficiency by 237.5%.It also lowered equipment failure rates by 74.7%.
This resulted in significantly reduced manufacturing costs and maximized system benefits.
Conclusion
This study establishes a theoretical framework for the intelligent manufacturing of machined parts.It explores key enabling technologies.
The researchers validate the advantages of intelligent manufacturing systems in enhancing manufacturing precision, stability, and resource utilization efficiency.
The research demonstrates that technologies such as deep learning, multi-source data fusion, knowledge graphs, and reinforcement learning hold significant application value in the intelligent manufacturing of machined parts.
Future research will focus on cognitive computing enhancement, autonomous decision-making capability improvement, and multi-system collaborative optimization to further elevate the level of intelligent manufacturing for machined parts.