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The selection of materials with the required properties was developing rapidly. There is no single rule to choose suitable material for a particular product. The complexes interrelation among the properties of composites makes more difficult to suggest suitable material for material scientist. The forecasting of relationship among material properties with throughput screening methods can help for the discovery of novel materials. The conventional way of selection of material need more investigations and time consuming. Recently, machine learning approaches are widely used in mechanical domain. Hence in this work, machine learning-based method was proposed to discover the relationship between the properties of natural fibres composites. To apply machine learning approach, there are 26 different natural fibres composites of Coir, Jute, Banana etc. were taken in to consideration. These materials are reinforced with pure export with different manufacturing methods such as Hand layup, Spray layup, Hot press moulding, and Compression moulding to produce the samples as per ASTM standards. The properties like density, tensile strength, hardness etc. were measured and interrelation among them was recognised using proposed method.

Machine learning approaches for prediction of properties of natural fiber composites : Apriori algorithm