## 3 Literature Survey
### Big Data Approaches for Specific Manufacturing Applications
#### 3.2.2 Fault Prediction
It should be noted that this particular framework, while creative, was not tested on actual data but rather on hypothetical datasets due to data proprietorship policy [67], hence providing clear opportunities for future research. Neural networks are applied to recognize lubrication defects in a cold forging process, [68] predict ductile cast iron quality [69], optimize micro-milling parameters [70], predict flow behavior of aluminum alloys during hot compression [71], and predict dimensional error in precision machining [72]. Finally, a process approach is taken to improve reliability of high speed mould machining [73]. It was seen in the preceding models featuring NN that data reduction plays a role of minimal importance because the neural network accomplishes feature creation and selection in the hidden layers. In [68], a total of 20 features are selected for model input with no explicit data reduction step. Nor was any reduction step performed in [69], where the dataset was relatively small, consisting of only 700 instances of 14 independent variables in the training set. In [70] and [71], only three features are input into the artificial neural networks (ANN). In [72], an extension of a simulation and process planning approach in [73; 74], the number of input variables is five. Finally, quality and efficiency in freeform surface machining are driven by three primary issues: tool path, tool orientation, and tool geometry [75]. A feature-based approach to machining complex freeform surfaces in the cloud manufacturing environment yields the capability for adaptive, event-driven process adjustments to minimize surface finish errors [76]. An observation across the set of articles reviewed in Section 3.2 is that a specific data reduction step is rarely utilized, either because the feature set was small to begin with or because the machine learning technique could accommodate. The exceptions used either statistical measures (Cohen's kappa) or PCA to reduce the feature set. The article using the former technique did not report how many features the case study began with and how many were ultimately used for model training. It is, therefore, not clear the extent to which the technique is useful. In the case of PCA with subject matter expertise, a feature set of 1000 reduced to 16. Additional discussion and possible extension will be included in Section 4. A second observation is that as in Section 3.1, variation exists in the frame of reference for which different articles approach the topic of predictive analytics. Some articles focus on the organizational capability to perform predictive analytics. These incorporate robust discussion on technology-centric elements such as architecture for data capture, storage, and extraction or at which levels different analyses may be performed (cloud, edge, real-time, offline, etc.). These typically featured commercially available technologies such as Hadoop or MapReduce and address some of the prerequisites for building organizational competencies in this area. Other articles, on the other hand, employed the term 'framework' to refer to a problem-solving approach or methodology, a sequence of actions to be performed by the analyst or data scientist. These articles more directly align with the objective of this literature review, but it is important to distinguish between the two perspectives as each are important. Indeed, the organizational capability for data capture, storage, and migration must necessarily precede any in-house capability to analyze smart manufacturing data or use it to train a machine learning model. Table 3 provides a summary of the forgoing studies that approach big data analytics applied to specific manufacturing use cases. The table summarizes whether the paper focuses on organizational capabilities, methodological approaches for the analyst, case studies, or some combination. For case studies, the machine learning algorithm is listed.