A model for the dig-in instability in serial sectioning and iterative orthogonal cutting

Abstract

Microtome serial sectioning is a key part of building brain maps of neurological tissue, which are made by serially cutting resin-embedded brain tissue into thin sections, followed by imaging on an electron microscope; features of interest are traced through a stack of images. However, lateral dimensions of the sections typically do not exceed 1 mm due to instabilities encountered when attempting to cut wider sections. One such instability is the dig-in instability, which occurs in any cutting process with a cutting force component pulling the tool deeper into the workpiece; it is a niche phenomenon in industrially important processes such as machining, where it is easily avoided, and thus is not studied in-depth in the literature; however, microtome cutting is especially susceptible to the dig-in instability due to the combination of high rake angles, small cutting tool wedge angles, and highly lubricated cutting.

There are currently no models for the dig-in instability nor engineering guidelines available linking mechanical characteristics of the cutting system, such as stiffness requirements, to dig-in instability regimes, despite system stiffness being acknowledged in the microtome cutting literature as important to successful cutting. The goal of this research is to generate a model for the dig-in stability which ties together cutting system mechanical characteristics to the maximum allowable width of cut to avoid digging in. A second model was generated to model how variations in cutting parameters result in variations on the resulting cut surface, and how this variation would change with each cutting pass. An instrumented cutting setup was designed and built to measure cutting forces and record cutting videos. A compliant knife was designed to control the stiffness characteristics of the cut.

Delrin polymer specimens were designed as stepped "pyramids" which would increase in width as the cut progressed, to identify the cutting width for which the cutting is unstable. Achieving this link between cutting system characteristics and successful sectioning outcome will enable designing machines capable of cutting at larger widths, and be a stepping stone towards mapping larger brain volumes. This in turn would enable greater understanding of neural function and pathology.