Abstract
The application of statistical process control (SPC) through control charts is an effective means of monitoring production process quality. This paper aims to enhance the quality of the gear shaving process in transmission gear manufacturing, ensuring long-term stable engagement between gears and improving the yield of critical items. By utilizing MINITAB software to monitor the gear shaving process in real-time, we can distinguish between normal and abnormal fluctuations, issue early warnings for abnormal fluctuations, and take measures to eliminate them, thereby restoring process stability and achieving the goal of improving and controlling quality.
1. Introduction
Gear engagement transmission is common in mechanical transmissions. To ensure long-term stable engagement, it is necessary to ensure that all parameters meet the design requirements. Therefore, each dimensional deviation should be monitored during the processing. The typical manufacturing process for automotive transmission gears includes rough turning, finish turning, hobbing, chamfering, gear shaving, heat treatment, sandblasting, and honing. Some gears also undergo grinding after heat treatment. Due to the small amount of material removal during honing, the gear shaving process serves as the final assurance for various deviations in gear tooth surface processing.
Statistical process control (SPC) charts are graphs that include control limits for controlling product quality during production. They can determine the causes of quality fluctuations and assess whether the production process is in a stable state. By distinguishing between normal and abnormal fluctuations and issuing early warnings for abnormal fluctuations, measures can be taken to eliminate them and restore process stability. When the process is only affected by random factors, it is in a state of statistical control (referred to as the controlled state). When systematic factors are present in the process, it is in a state of statistical失控 (referred to as the uncontrolled state).
There are various types of control charts, and the choice of which to use depends primarily on whether the monitored process data is continuous. Additionally, factors such as the ease of sampling, cost, and time must also be considered. Commonly used SPC charts are mainly divided into measurement-type control charts and count-type control charts.
- Measurement-type control charts obtain measurement data, also known as continuous random variables in probability statistics, such as part dimensions, material strength, temperature, and weight. They mainly include: mean-range control charts (X-R), mean-standard deviation control charts (X-S charts), median-range control charts (Me-R charts), and individual value-moving range control charts (X-MR charts).
- Count-type control chart data, also known as discrete random variables in probability statistics, most commonly take one of two values in quality control, such as pass/fail or accept/reject. They mainly include: percent defective control charts (P charts), number defective control charts (NP charts), defects control charts (C charts), and units defects control charts (U charts).
2. Application of MINITAB in Statistical Process Control: A Case Study
2.1 Measurement Data
This paper evaluates the process capability of the gear shaving process by measuring the gear’s cross-ball distance (M value). Five sets of data, each containing 10 measurements, were collected. The M value measurement data is continuous and belongs to the measurement-type control chart, as shown in Table 1.
Table 1: M Value Measurement Data (mm)
| Set | Measurement 1 | Measurement 2 | Measurement 3 | Measurement 4 | Measurement 5 |
|---|---|---|---|---|---|
| 1 | 124.370 | 124.37 | 124.37 | 124.37 | 124.37 |
| 2 | 124.365 | 124.37 | 124.38 | 124.38 | 124.38 |
| 3 | 124.380 | 124.38 | 124.37 | 124.38 | 124.39 |
| 4 | 124.360 | 124.36 | 124.36 | 124.36 | 124.36 |
| 5 | 124.375 | 124.38 | 124.38 | 124.38 | 124.36 |
(Continued for Sets 6-10)
2.2 Control Chart
Using MINITAB software, the subgroup samples were set as 5 samples per group, divided into 10 groups, to generate a mean-range control chart.
Criteria for Abnormality Detection: Based on the arrangement of points on the control chart (with no defects and randomly distributed), the following phenomena indicate abnormalities:
- Continuous 7 or more points on one side of the centerline;
- Continuous 7 points increasing or decreasing;
- Continuous 11 points with at least 10 on one side of the centerline;
- Continuous 14 points with at least 12 on one side of the centerline;
- Continuous 17 points with at least 14 on one side of the centerline;
- Continuous 20 points with at least 16 on one side of the centerline;
- Periodic fluctuations in points.
Based on the above criteria, none of the 10 sample means exhibited any abnormalities, indicating that the gear shaving process is in a controlled state. If abnormalities are detected, a thorough investigation should be conducted from the six aspects of “man, machine, material, method, environment, and measurement.” Adjustments should be made promptly to maintain statistical control.
2.3 Process Capability Index Analysis
The process capability index (Cpk) reflects the reliability of part quality when the production process is in a normal state and factors such as man, machine, material, method, and environment are also stable. By setting the subgroup size to 5 and measuring the Cpk value, we can assess the process capability.
The Cpk is the ratio of the allowed maximum variation range of the process performance to the normal deviation of the process. A Cpk of 1 indicates that the process meets specifications exactly; a Cpk less than 1 indicates that specifications are exceeded. After calculation using MINITAB software, the Cpk value was 1.42, which is greater than 1.33, indicating sufficient process capability and excellent technical management. The process should be maintained.
3. Conclusion
In the part processing stage, timely process control and suppression of unstable factors at the source can effectively reduce waste rates, improve quality, and thus reduce production costs. The statistical process control function of MINITAB software is an effective quality management tool that plays a significant preventive role in quality control. By analyzing data, we can monitor the gear shaving process, promptly detect abnormal fluctuations during processing, and actively improve unstable factors. By adopting this method, the part processing process remains in a controlled state, ultimately achieving stability and improving product quality.
In summary, the application of SPC through MINITAB software in gear shaving process quality control is crucial for ensuring product quality and stability. It provides a scientific and effective means for quality management and continuous improvement in the manufacturing industry.