Developing a hierarchical decomposition methodology to increase manufacturing process and equipment health awareness

Abstract

Manufacturing systems are becoming increasingly complex as more advanced and emerging technologies are integrated into the factory floor to yield new processes or increase the efficiency of existing processes. As greater complexity is formed across the factory, new relationships are often generated that can lead to advanced capabilities, yet produce unforeseen faults and failures. Industrial robot arm work cells within the manufacturing environment present increasing complexity, emergent technologies, new relationships, and unpredicted faults/failures. To maintain required levels of productivity, process quality, and asset availability, manufacturers must reconcile this complexity to understand how the health degradation of constituent physical elements and functional tasks impact one another through the monitoring of critical informative measures and metrics. This article presents the initial efforts in developing a novel hierarchical decomposition methodology. The innovation in this method is that it provides the manufacturer with sufficient discretion to physically deconstruct their system and functionally decompose their process to user-defined levels based upon desired monitoring, maintenance, and control levels. This enables the manufacturer to specify relationships within and across the physical, functional, and information domains to identify impactful health degradations without having to know all possible failure modes. The hierarchical decomposition methodology will advance the state of the art in terms of improving machine health by highlighting how health degradations propagate through the relationship network prior to a piece of equipment compromising the productivity or quality of a process. The first two steps of the methodology, physical decomposition and functional decomposition, are defined in detail and applied to a multi-robot work cell use case.

A virtual engineering based approach to verify structural complexity of component-based automation systems in early design phase

“…Modern manufacturing systems are composed of components and sub-systems of various nature, including: machining and assembly units, smart material handling devices, complex control algorithms and interlinked safety units [1]. Due to the need for the system to be changeable and modular to accommodate changes in functionality, structural complexity increases as more components and interfaces are added to systems at both hardware and software layers [2].…”

Fault Mechanism Analysis for Manufacturing System Based on Catastrophe Model

“…Guo et al [19] used topological data analysis to extract characteristics. Weiss et al [20] extracted key information using a combination of heuristic algorithms. Zhou et al [21] used k-Nearest neighborhood in fault Isolation.…”

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

“…The alignment between operations and trade-off behaviour is essential to management decisions (Parast, 2011). For example, quality improvement impacts maintenance tasks and production planning (Weiss et al, 2018;Rivera-Gómez et al, 2020). Based on Gardner (2020), where it focus on understanding the trade-off between feedback on conformance quality and production speed, a slack in resources which represents cost to an organization is also regarded as a trade-off relation and is inherent to or inevitable in a process (Madapusi and D'Souza, 2012;Parast, 2011).…”