Smart Knife for Robotic Meat Cutting

Abstract

Automation is a key enabling technology for efficiency improvement in the meat industry. This paper presents the development of a novel smart knife based on radio-and microwave-frequency sensing, which is suitable for automatic robotised cutting tasks. Partial-least-square regression and neural network prediction models are shown to determine contact of the knife with a work object and depth of cut. Using a water model, the knife can predict contact with 1.81% error, and depth with 2.45 mm (± 0.18 mm) mean error. With pork loin, error in contact detection was 2.92%, and mean depth error was 7.22 mm (± 1.39 mm).