DAMIC process for handling product defects
The DAMIC process for addressing product defects is a systematic approach based on Six Sigma management. Through a closed-loop management process consisting of five stages: Define, Measure, Analyze, Improve, and Control, it accurately identifies and resolves quality defects in injection molded products, reducing defect rates and improving production stability. Compared to traditional empirical approaches, the DAMIC process offers advantages such as data-driven, rigorous logic, and high replicability. It is suitable for systematically addressing various injection molding defects (such as flash, sink marks, and weld marks), especially in large-scale production, effectively reducing quality fluctuations and improving customer satisfaction. This process emphasizes a customer-centric approach, integrating defect resolution with process capability enhancement to achieve long-term and stable quality improvement.
The Define phase is the starting point of the DAMIC process, focusing on identifying defect issues and improvement goals. First, critical quality characteristics (CTQs)—the product quality indicators that customers prioritize, such as dimensional accuracy, surface finish, and mechanical properties—must be identified. For example, for automotive instrument panels, the absence of sink marks and a dimensional tolerance of ±0.1mm are key requirements. Next, defects must be quantitatively defined to avoid vague descriptions. For example, “flash” could be defined as “excess material edge that exceeds the design dimensions by more than 0.2mm.” Defect standards should be established using images or physical samples. Furthermore, a cross-functional team, including injection molding engineers, mold designers, operators, and quality inspectors, should be assembled, with clear responsibilities for each member. Finally, measurable improvement targets should be set, such as reducing the defect rate from the current 500 ppm (defects per million parts) to below 100 ppm, with a target completion time of three months and management resource support.
The Measure phase focuses on collecting data and assessing current process capability. First, the measurement system, including measuring tools (such as a coordinate measuring machine or roughness tester), measurement methods, and sampling plans, must be determined to ensure the accuracy and repeatability of the measurement data. A Measurement System Analysis (MSA) is typically performed, with a requirement for gage repeatability and reproducibility (GRR) of less than 10%. Next, defect-related data is collected, including defect type, frequency, location, and relevant process parameters (such as temperature, pressure, and time). This can be recorded using tools such as checklists and control charts. For example, data from 30 consecutive production batches, sampling 50 pieces per batch, can be collected to calculate the number and distribution of each defect type. The Process Capability Index (CPK) should also be assessed. If the current CPK value is 0.8, this indicates insufficient process capability and needs to be improved to above 1.33 in subsequent phases. Furthermore, data authenticity must be verified to eliminate outliers or measurement errors to provide a reliable basis for subsequent analysis.
phase aims to identify the root causes of defects through data mining. First, statistical analysis of the data collected during the measurement phase is performed, using Pareto charts to identify key defects. For example, 80% of defects were concentrated in flash and sink marks, which were prioritized for resolution. A fishbone diagram (cause-and-effect diagram) was then used to identify potential causes from six perspectives: personnel, machine, material, method, environment, and measurement. For example, possible causes of flash include insufficient clamping force, mold parting surface wear, and excessive injection pressure. Hypothesis testing and regression analysis are then used to verify the root cause. For example, by comparing the flash incidence rate at different clamping forces, it was found that when the clamping force was 10% below the set value, the flash incidence rate increased from 5% to 30%, confirming that insufficient clamping force was the primary cause. For complex defects, a design of experiments (DOE) approach can be used. By varying key process parameters (such as melt temperature and holding time), multiple experiments are conducted to analyze the impact of these parameters on the defect and identify the most significant influencing factors.
The Improve phase involves developing and implementing solutions to the root causes. Based on the findings from the Analysis phase, specific improvement measures are proposed. For example, for issues such as insufficient clamping force, regular clamping force calibration, replacement of worn clamping cylinder seals, and optimization of mold parting surface fit accuracy can be implemented. For sink marks, the holding pressure can be increased from 50 MPa to 65 MPa, and the holding time from 10 seconds to 15 seconds. Improvement measures must be evaluated for feasibility, including cost, time, and technical difficulty, prioritizing those with minimal investment and rapid results. Small-batch trials are then conducted to verify the effectiveness of the solution. For example, if the flash defect rate drops from 20% to 3% on the same machine using the new parameters, the solution is effective. A contingency plan should also be developed to adjust the parameters promptly if the improvement measures lead to new problems (e.g., excessive holding pressure leading to increased internal stress in the part). Finally, effective measures should be standardized, the Standard Operating Procedure (SOP) updated, and operators trained to ensure proper implementation.
The goal of the Control phase is to maintain improvements and prevent recurrence. First, a process control plan must be established to clearly define the control ranges for key process parameters. For example, clamping force must be controlled within ±5% of the set value, and melt temperature must be controlled at 230°C ± 5°C. Parameter fluctuations should be monitored in real time using control charts, and corrective measures should be taken promptly when anomalies occur. Regular inspections should then be implemented, such as daily clamping force checks, weekly temperature sensor calibrations, and monthly mold wear assessments, to ensure process stability. Relevant documentation, including process parameter tables, mold maintenance manuals, and inspection standards, should also be updated and incorporated into the quality management system. Furthermore, long-term tracking of improvement results is required, such as monitoring the defect rate for six consecutive months to confirm that it remains stable below 100 ppm and that the CPK value reaches or exceeds 1.33. Finally, lessons learned should be summarized and standardized, creating a knowledge base to provide a reference for resolving similar defects and planning the next phase of improvement, ultimately achieving continuous quality improvement.
