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With the increasing demand for product quality in the manufacturing industry, the application of machine learning （ML） technology in manufacturing quality control has been under attention. To address the low automation and integration, as well as the lack of quantitative evaluation methods in the manufacturing quality inspection for combine harvester, a combine harvester manufacturing quality end-of-line inspection system was designed and developed. Based on this system, an "end-of-line inspection + secondary grading" manufacturing quality hybrid inspection method was proposed, which used the inspection software to screen out abnormal products outside the qualified range and select superior and inferior products. The secondary grading model performed a secondary inspection on qualified products and marks hidden problems. Firstly, based on the integration and analysis of the combine harvester manufacturing quality inspection requirements, the detection flow was designed. The overall design of the system was tested and simulated by using the Visual Components digital workshop platform. The LabVIEW-based end-of-line inspection software was developed according to the actual requirements and detection functions, and corresponding userfriendly human-machine interfaces were designed. The results of the end-of-line workshop inspection tests showed that the system can meet various inspection requirements and achieve software functions, preliminarily verifying the feasibility of the system. Secondly, local outlier factor （LOF） was selected as the secondary grading algorithm according to the scenario, and it was integrated into the detection flow based on its anomaly detection principle. Then, a manufacturing quality inspection and grading framework was established, and the grading process classified the initially screened qualified products into "good" and "tracked" groups based on the processing results, thereby improving the manufacturing quality inspection and evaluation system. The training results indicated that LOF-based method can identify anomalous samples in the dataset with insignificant differences. In the performance validation process, this method accurately identified the four "tracked" samples in the testing dataset, which was consistent with the distribution of the quartile plots, further validating the effectiveness of this hybrid detection method. The developed end-of-line inspection system for the manufacturing quality of combine harvesters and the proposed grading method had important practical application value, promoting the application of digital workshop concept and ML on agricultural machinery, and providing solutions and methods for agricultural machinery manufacturing quality control.