Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

Abstract

In this paper we present the re-engineering process of an assembly line that features speed reducers and multipliers for agricultural applications. The product operates as an interface between an input torque, typically supplied by an agricultural vehicle, and an output torque, generally moving specific equipment placed on a trolley equipped with a tow hook. The "as-is'' (initial version) line was highly inefficient due to several critical issues, including the high age of the machines, a non-optimal arrangement of them in the shop floor, and the absence of controls and process standards. These critical issues were analysed with the tools offered by Lean Manufacturing, which made it possible to identify irregularities and operations that require effort (Mura), overload (Muri), and waste (Muda). The definition of the "to-be'' (new version) assembly line included actions to update the department layout, to modify the assembly process and to design the line feeding system in compliance with the well-known concepts of Golden Zone and Strike Zone. The whole process addressed, in particular, the problem of the incorrect assembly of the oil seals. The registered error was mainly caused by the difficulty in visually identifying the correct side of the assembled oil seal, and by the mental fatigue of operators at the end of the shift. The solution studied in this paper resulted in a Poka-Yoke solution, which, leveraging the modern technologies and methods of deep learning and computer vision, monitors the process flow of the operators through a camera, preventing its completion in the event of an assembly error.