As the core basic parts of industrial manufacturing, the dimensional accuracy of hardware plastic mold accessories directly determines the service life, product yield and production efficiency of the mold. In the field of precision machining, the improvement of dimensional accuracy needs to be promoted in multiple dimensions such as equipment selection, process optimization, material selection and process control.
The accuracy level of precision machining equipment is the physical basis of dimensional accuracy. Modern mold manufacturing generally uses high-end equipment such as five-axis linkage CNC machining center (CNC), wire cutting machine (WEDM) and electric spark forming machine (EDM). For example, the five-axis CNC can avoid the cumulative error caused by secondary clamping by machining complex surfaces through multi-axis linkage. Its positioning accuracy can reach ±0.003mm and the repeat positioning accuracy can reach ±0.001mm. WEDM uses extremely fine electrode wire (diameter 0.03-0.3mm) to cut cemented carbide molds, and the surface roughness can be controlled below Ra0.4μm. In order to ensure the long-term and stable operation of the equipment, regular geometric accuracy calibration is required, such as using a laser interferometer to detect the straightness and verticality of the equipment, and the error must be controlled within ±0.005mm/m. In addition, the dynamic performance of the equipment, such as spindle runout and guide rail parallelism, also needs to be optimized through dynamic balancing tests and vibration monitoring to avoid processing errors caused by equipment problems.
Tool path planning needs to take into account both efficiency and precision. When generating non-interference tool paths through CAM software, the surface features need to be layered, such as setting a separate processing strategy for areas with a cavity depth exceeding 50mm to avoid stress concentration caused by one-size-fits-all cutting. The selection of cutting parameters needs to be dynamically adjusted according to the material properties. For example, when processing 718H steel, the cutting speed should be controlled at 80-120m/min and the feed rate should be 0.08-0.15mm/r; when processing P20 plastic mold steel, the cutting speed needs to be appropriately reduced and the feed rate needs to be increased to reduce cutting heat. For tiny features (such as cooling water channels with a diameter of 0.1mm), superhard tools (such as PCD or CBN) need to be used in combination with micro-lubrication (MQL) technology to avoid dimensional deviations caused by tool wear.
The cutting heat and residual stress generated during the process of hardware plastic mold accessories are the main causes of precision loss. For cutting heat, an efficient cooling system is required, such as oil-based or water-based cutting fluids that need to be circulated and cooled to ensure that the temperature of the cutting zone is ≤80°C. For difficult-to-process materials (such as titanium alloys), liquid nitrogen cooling (-196°C) technology can be used to suppress phase change. The release of residual stress needs to be achieved through multiple aging treatments. For example, low-temperature tempering at 150-200°C can release more than 80% of residual stress. In addition, the blank needs to be preheated before processing. For example, heating the mold steel to the austenitizing temperature and keeping it warm for 2 hours can significantly reduce the tissue stress.
Real-time monitoring is a closed-loop control method to ensure accuracy. The detection accuracy of the three-coordinate measuring machine (CMM) must reach ±0.002mm, and the measurement environment temperature must be controlled at 20±2°C. Feedback of the measurement results to the CNC system through the data interface can achieve error compensation. For example, a German company uses an online measuring probe to dynamically adjust the tool path during the processing process to control the cavity size deviation within ±0.005mm. In addition, a measurement database needs to be established to analyze historical data, such as monitoring dimensional stability through SPC (statistical process control) charts to detect process deviations in a timely manner.
Material properties directly affect processing difficulty. High-hardness mold steel (such as SKD61) needs to be vacuum quenched and cryogenically treated to improve stability. For example, cryogenic treatment (-196°C) can refine grains and improve fatigue resistance. The pretreatment process needs to match the processing process. For example, the tempering temperature after quenching needs to be adjusted according to the CNC processing parameters. In addition, a material performance database needs to be established to record the hardness, toughness and other indicators of different batches of steel to provide a basis for process optimization.
The rigidity and positioning accuracy of the fixture are the key to processing stability. The use of zero-point positioning system (Zero Point) and hydraulic fixtures can achieve fast and high-precision clamping. For example, by pre-embedded positioning pins and reference surfaces, the clamping error is controlled within 0.003mm. The fixture needs to be regularly tested for accuracy, such as using a laser tracker to detect the flatness and verticality of the fixture, and the error must be ≤0.01mm. In addition, the fixture structure needs to be optimized according to the shape of the workpiece. For example, for special-shaped workpieces, flexible fixtures or vacuum adsorption technology can be used.
By building a virtual processing environment through digital twin technology, tool paths and stress distribution can be simulated. For example, before processing, cavity deformation can be predicted through finite element analysis (FEA) to optimize process parameters in advance. Combined with AI algorithms, real-time analysis of processing data (such as cutting force and vibration signals) can be performed. For example, tool wear trends can be predicted through machine learning models, and tools can be replaced in time to avoid dimensional deviations. A company has improved the dimensional accuracy of mold accessories by 40% through AI prediction models. In addition, a digital process library needs to be established to standardize the processing parameters of different materials, such as sharing best practice cases through cloud platforms.
The dimensional accuracy of hardware plastic mold accessories needs to be collaboratively optimized from multiple dimensions such as equipment, process, material, and measurement. With the development of intelligent manufacturing technology, digitalization, networking, and intelligence will become the core trend of future precision processing. By building a virtual processing environment and integrating AI algorithms, real-time monitoring and error compensation of the processing process can be achieved, pushing the industry towards higher precision and higher efficiency. In the future, mold manufacturing companies need to further deepen the application of digital technology, such as achieving traceability of process data through blockchain technology to provide data support for quality control.
