Prediction of Surface Roughness in Milling Additively Manufactured High-Strength Maraging Steel Using Broad Learning System

Additive manufacturing (AM) provides a promising method to fabricate advanced functional parts with different mechanical and material performances from their traditional counterparts. However, the poor surface quality makes the subsequent post-processing necessary for precision application. Hybrid manufacturing combining additive and subtractive manufacturing processes is an effective method to improve the surface quality of additive manufacturing (AMed) metal parts rapidly by using a subtractive process, in which surface roughness is an important technical indicator. Therefore, accurate surface roughness prediction is crucial for process and quality control in the subtractive machining of additively manufactured parts. In this study, a prediction method utilizing a broad learning system (BLS) is developed to predict the surface roughness of machined AMed maraging steel parts considering aging heat treatment. First, feature extraction was performed on the force signal during the cutting process in the time domain, frequency domain, and time–frequency domain. Then, the maximum information coefficient was used to select important features from high to low feature by feature. Furthermore, the important features and cutting parameters were fused as the input of BLS. Finally, the corresponding prediction results were compared with those based only on cutting parameters. The results show that the prediction accuracy of machined surface roughness is higher when fusing force signal features and cutting parameters. The prediction errors (mean absolute percentage error) were reduced by 67.28% and 16.39% to 0.53% and 0.51%, respectively, for the AMed maraging steels with and without heat treatment.