Overall analysis of die castings
Overall die-casting analysis is a crucial tool for evaluating die-cast product quality and optimizing production processes. By comprehensively analyzing casting performance, defect types, production stability, and cost structure, it provides a scientific basis for production improvements. Die-castings, as high-precision, high-efficiency metal forming products, are widely used in the automotive, electronics, aerospace, and other fields. Their quality directly impacts the reliability of the end product. Therefore, an overall analysis must encompass all aspects of the entire process, from raw materials to finished product, to ensure comprehensive and accurate analysis results.
Die-casting performance analysis is the core of the overall analysis, encompassing three key indicators: mechanical properties, dimensional accuracy, and surface quality. Mechanical properties such as tensile strength, elongation, and hardness are tested through random sampling to determine if they meet design standards. For example, aluminum alloy die-castings for automotive structural parts are typically required to have a tensile strength of ≥200 MPa and an elongation of ≥5%. Testing is performed using a tensile testing machine and a hardness tester. Three to five specimens are randomly sampled from each batch, and the average and standard deviation are calculated to assess performance stability. Dimensional accuracy analysis requires a three-coordinate measuring machine (CMM) to measure the deviation between the actual and designed values of key dimensions. For example, the bearing hole diameter tolerance for motor end covers must be within ±0.02 mm, and flatness ≤0.1 mm/m. Surface quality is tested through visual inspection and a roughness tester. Surface defects such as cracks, shrinkage, and pinholes must be absent, and the roughness Ra value must meet the drawing requirements (e.g., decorative parts must have Ra ≤1.6 μm). An automotive parts manufacturer conducted a performance analysis of an aluminum alloy bracket and found that the tensile strength fluctuated by more than 10%. By tracing the process parameters and adjusting the die-casting pressure ratio, the stability was significantly improved.
Defect type analysis is a key component of overall die-casting analysis. Defect incidence rates must be calculated and their root causes identified. Common defects include flash, under-giving, porosity, shrinkage, and cracks, each with its own characteristic characteristics and mechanisms. Flash is often caused by insufficient clamping force or wear on the mold parting surface. If the incidence exceeds 5%, the clamping parameters and mold condition should be checked. Under-giving is often caused by slow injection speed or low molten metal temperature, which can be addressed by increasing the injection speed (set to 5-8 m/s) or the pouring temperature (680-720°C for aluminum alloys). Porosity can be categorized as subcutaneous and internal. Subcutaneous porosity is often caused by incomplete evacuation of gas from the mold cavity, requiring optimization of the exhaust system. Internal porosity may indicate excessive gas content in the molten metal, requiring enhanced degassing (e.g., vacuum die casting). Shrinkage often occurs in thick and large areas, requiring extended holding time or increased localized cooling. Cracks are often caused by excessive internal stress, which can be alleviated by adjusting the release agent dosage and reducing the cooling rate. A defect analysis of zinc alloy die-castings by a certain factory showed that the incidence of porosity reached 8%. By improving the design of the mold venting grooves, the incidence was reduced to 1.5%.
Production process stability analysis requires evaluating the impact of equipment, processes, and personnel on die-casting quality, identifying sources of fluctuation through statistical data. Equipment stability can be measured using OEE (Overall Equipment Effectiveness) metrics, including availability, performance, and quality. An OEE below 60% indicates serious equipment issues and requires enhanced maintenance. Process parameter stability analysis requires recording the fluctuation range of parameters such as injection pressure, mold temperature, and pouring temperature. For example, mold temperature fluctuations exceeding ±10°C can lead to uneven shrinkage in the casting, necessitating calibration of the temperature control system. Human operational influences are primarily reflected in aspects such as scooping volume control and mold cleaning. Standardized operating procedures (SOPs) are implemented to reduce human error, and operators are regularly trained and assessed. A die-casting plant, through a production stability analysis, discovered that injection speed fluctuations were correlated with hydraulic oil temperature. After installing an automatic oil temperature control system, parameter fluctuations were reduced by 60%.
Cost structure analysis is an economic perspective for the overall analysis of die castings. It involves calculating the contribution of raw materials, energy, equipment, and labor costs, identifying areas for cost reduction. Raw material costs account for the largest proportion (approximately 50-60%), including molten metal loss (in the gate and overflow) and scrap loss. Significant cost reductions can be achieved by optimizing the gating system, reducing gate volume, and improving yield (target ≥90%). Energy costs account for 10-15%, including electricity and gas consumption. The use of variable-frequency motors and waste heat recovery systems can reduce energy consumption by 15-20%. Equipment costs include depreciation and maintenance expenses. Properly planning equipment load (utilization rate ≥80%) can reduce depreciation costs per unit of product. Labor costs account for 10-20%. Automation (such as robotic loading and unloading) can reduce labor requirements. One plant reduced labor costs by 40% after introducing an automated production line. Cost analysis should be conducted monthly, comparing actual costs with target costs and formulating improvement measures.
Application scenario adaptability analysis requires evaluating the performance of die-cast parts in their operating environment to ensure they meet the requirements of the end product. Automotive parts require consideration of corrosion resistance, heat resistance, and vibration fatigue performance. For example, chassis castings must pass salt spray testing (500 hours without rust), and engine peripheral castings must withstand temperatures exceeding 150°C. Electronic components require attention to electromagnetic shielding and dimensional stability. For example, mobile phone midframes must pass thermal shock testing (-40°C to 85°C cycling) with a dimensional change rate of ≤0.05%. Aerospace components have extremely high weight and reliability requirements, requiring non-destructive testing (such as X-ray inspection) to ensure internal defects. A battery casing produced by a factory for new energy vehicles was found to have insufficient impact resistance through application adaptability analysis. Adjustments to the alloy composition (by adding magnesium) resulted in the battery casing passing the puncture test.
