Having environmental and economic advantages, flax fibers have been recognized as a potential replacement for glass fibers as reinforcement in epoxy composites for various applications. Its widening applications require employing failure criteria and analysis methods for engineering design, analysis, and optimization of this material. Among different failure modes, delamination is known as one of the earliest ones in laminated composites and needs to be studied in detail. However, the delamination characteristics of unidirectional (UD) flax/epoxy composites in pure Mode I has rarely been addressed, while Mode II and Mixed-mode I/II have never been addressed before. This work studies and evaluates the interlaminar fracture toughness and delamination behavior of UD flax/epoxy composite under Mode I, Mode II, and Mixed-mode I/II loading. The composites were tested following corresponding ASTM standards and fulfilled all the requirements. The interlaminar fracture toughness of the composite were determined and validated based on the specific characteristics of natural fibers. Considering the variation in the composite structure configuration and its effects, the results of interlaminar fracture toughness fit in the range of those reported for similar composites in the literature and provide a basis for the material properties of this composite.

The concept of equifinality is a central issue in taphonomy, conditioning an analyst’s ability to interpret the formation and functionality of palaeontological and archaeological sites. This issue lies primarily in the methods available to identify and characterise microscopic bone surface modifications (BSMs) in archaeological sites. Recent years have seen a notable increase in the number of studies proposing the use of deep learning (DL)-based computer vision (CV) algorithms on stereomicroscope images to overcome these issues. Few studies, however, have considered the possible limitations of these techniques. The present research performs a detailed evaluation of the quality of three previously published image datasets of BSMs, replicating the use of DL for the classification of these images. Algorithms are then subjected to rigorous testing. Despite what previous research suggests, DL algorithms are shown to not perform as well when exposed to new data. We additionally conclude that the quality of each of the three datasets is far from ideal for any type of analysis. This raises considerable concerns on the optimistic presentation of DL as a means of overcoming taphonomic equifinality. In light of this, extreme caution is advised until good quality, larger, balanced, datasets, that are more analogous with the fossil record, are available.