5895
Subsequently , we use the features derived from the graph Kronecker product as inputs to a machine learning algorithm to predict the severity ( class or level ) of average length of lack-of-fusion defects within a layer , which is obtained from offline X-ray computed tomography of the test parts .
 material: as; enabling technology: machine learning algorithm; concept or principle: average; concept or principle: defects; process parameter: layer; process characterization: X-ray computed tomography
Given options, please tell me the categories of all the listed entity words.Output format is "type1: word1; type2: word2". 
Option: manufacturing standard, machanical property, manufacturing process, material, engineering feature, process parameter, machine or equipment, application, process characterization, enabling technology, biomedical, concept or principle 
Text: {0} 
 Entities: as, machine learning algorithm, average, defects, layer, X-ray computed tomography 
Answer:
 material: as; enabling technology: machine learning algorithm; concept or principle: average; concept or principle: defects; process parameter: layer; process characterization: X-ray computed tomography