In [11], Wu et al. characterized the features of the big data revolution and proposed big data processing methods with machine learning and data mining algorithms.
In this paper, a prescriptive deviation modelling method coupled with machine learning techniques is proposed to address the modelling of shape deviations in additive manufacturing.
The proposed approach relies on the combination of dimensionality reduction methods with machine-learning techniques.
We compare our method with several machine learning methods and various node split criteria using several real data sets.
Filter methods rank each feature according to a given objective function (e.g., correlation to response variable, inter-feature correlation, etc)., while wrapper methods evaluate and select features which lead to the best predictive performance by associating a filter method with a machine learning algorithm (represented by a hyphen connecting both algorithms) [18].
We also compare our method with another machine learning method, MINERVA [ 15], which uses SVM as well and is based on a larger feature set containing various aspects of information of target residue such as weighted atomic packing density, relative surface area burial, weighted hydrophobicity and so on.
Many variants have been developed since then, with improvements such as automation of the bootstrapping and pattern discovery methods, often with machine learning techniques applied to selection, weighting and combination of local features in the immediate vicinity of the relata of interest.
From Table 2, we can see that the performance of all of these methods with different machine learning models and sequence-based feature representation methods are lower than ours, which indicates the advantages of our method.
Owing to the fact that current research on multi-axis postprocessor methods only deals with machine tool configurations whose linear and rotational movements are orthogonal, this study has presented a postprocessor algorithm for the five-axis machine tool with a nutating head whose rotational axis is in an inclined plane.
