This study involves the design of an integrated machine dedicated to the core processes of classifying, shelling, and cleaning to address the critical drawbacks of existing Camellia oleifera fruit processing equipment, including the high manual labor requirement, low operating efficiency, unsatisfactory shelling and cleaning performance, and severe camellia seed damage. The classifying system employed a slat drum structure, and response surface methodology (RSM) was utilized to determine and optimize its operating parameters: spiral blade speed: 20 rpm; drum speed: 10 rpm; and rise angle: 9.6°. The shelling system employed a horizontal flexible structure, and polyurethane was the core material. We determined through single-factor experiments that the shelling drum rotation speed was 200 rpm. For the cleaning system, a composite mode integrating drum screening and friction separation was adopted, and single-factor experiments further determined the optimal operating parameters: cleaning drum rotation speed: 20 rpm; friction conveyor shaft rotation speed: 150 rpm; and cleaning inclination angle: 25°. The performance test verified that the integrated machine achieved outstanding results: the shelling rate reached 97.52%, the camellia seed breakage rate did not exceed 2.42%, the impurity content rate did not exceed 1.99%, the loss rate was less than 3.66%, and the processing capacity reached 2614 kg/h.

To address the low shelling rate and high seed breakage in existing oil tea fruit shelling devices, a novel laser scoring device was designed for fresh Camellia oleifera fruits. Experimental studies were conducted to optimize the key parameters of the custom-built laser scoring machine, aiming to improve scoring qualification rates. Through single-factor tests and response surface methodology, a regression model was developed to characterize the relationship between the scoring qualification rate and the following three variables: conveyor speed (12 mm/s), laser power (97 W), and defocusing distance (10 mm). The study revealed interactive effects among these parameters. After optimization and verification under ideal conditions, the device achieved a peak average qualification rate of 85.6%.