## 1 Introduction  
Smart manufacturing has increasingly stringent requirements for material performance, and aluminum alloys are widely used due to their lightweight, high strength, corrosion resistance, and good processability [1]. Improving the quality control of aluminum alloy surface grinding and polishing has become a focus of industry concern [2]. Existing research on quality prediction during the grinding process mainly includes three categories: model-driven [3], statistical fitting [4], and data-driven [5, 6] methods. The model-driven method establishes mathematical/physical models to predict surface roughness, but it is difficult to achieve accurate prediction due to the complexity of the actual machining process. The coupling and interaction between process parameters can be better taken into account by statistical fitting and data-driven methods, which have produced notable outcomes in precision manufacturing [7], CNC machining [8], aerospace manufacturing [9], and automobile manufacturing [10].  
Once welding is completed, robotic arm-assisted grinding is widely used to ensure the consistency and reliability of surface quality [11, 12]. Compared to traditional manual grinding and typical machine tool grinding, robotic arm grinding is more flexible, stable, and adaptable to changing environments [13, 14]. In smart manufacturing grinding tasks, the use of robots is constrained by low productivity and high cost due to the involvement of a large number of small-batch operations. While research on quality prediction for small-batch production has been conducted [15], no related studies have addressed the grinding processes for small-batch aerospace workpieces, particularly where the runtime is highly considered.  
Although existing studies in the processing and manufacturing field have considered the impact of production time on actual production application [16, 17, 18], the runtime for data modeling of a completely new, unfamiliar workpiece requires further research and discussion, particularly considering adaptability and transferability issues under conditions with limited training samples. For the robot grinding scenario of smart manufacturing, each workpiece has unique model characteristics. Traditional statistical fitting methods have good data feature capture capabilities when the data volume is small but cannot accurately fit when the data volume is large, while data-driven methods require longer training and modeling time, which constrains processing efficiency.  
To address the above issues, this study proposes a surface-roughness prediction method based on the fusion of response surface methodology-BP neural network (BPNN). For sample data of different sizes, the training time can be taken into account while ensuring prediction accuracy, thus solving the balance problem between quality and processing efficiency in actual grinding scenarios.  
The contributions of this study are summarized as follows.  
1. Polynomial feature transformation and significance analysis were performed on the proposed parameters. Data preprocessing, including normalization and feature selection, was conducted to ensure the significance of features' impact on prediction results.
2. By incorporating model training time into the evaluation metrics and using the acquired experimental data sample size as a horizontal parameter for comparison, this method ensures compatibility of the model with industrial production requirements.
3. A Data-Model-Verification(DMV) architecture for grinding is proposed, which has obvious accuracy and practicality in modeling and predicting the roughness of ground surfaces.  
The rest of the study is organized as follows. Section 2 presents the latest progress in surface quality prediction. Section 3 describes the principles and implementation process of the method. Section 4 presents the experiment setups and results. Section 5 present discusses, respectively. Section 6 summarizes the work of this study and provides an outlook for the future.