Tool wear monitoring in roughing and finishing processes based on machine internal data

Abstract

Data analytics plays a significant role in the realization of Industry 4.0. By generating context-related persistent datasets, every manufacturing process in real production becomes an experiment. The vision of Internet of Production (IoP) is to enable real-time diagnosis and prediction in smart productions by acquiring datasets seamlessly from different data silos. This requires interdisciplinary collaboration and domain-specific expertise. In this paper, we present a novel tool wear monitoring system for milling process developed in the context of IoP. This system is based on high-frequency data from the numerical control of the production machine without additional sensors. The novelty of this paper lies in the introduction of virtual workpiece quality and fusion of multiple build-in sensor signals and a force model as decision support. This bridges the time gap between quality inspection and production at the shop floor level, establishes an automated statistical process control system, and provides a more plausible prediction of tool lifetime. The monitoring of two different milling processes in a real production environment is exemplary demonstrated in this paper. The first case is a face roughing process with the aim of rapidly removing large amounts of material. The second case is a face finishing operation that follows roughing and aims to achieve the desired surface quality.

WITHDRAWN: Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements

“…Recently, computer vision and feature extraction are continuously evolving and their popularity is increasing with the help of advanced technology [26][27][28][29].…”

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

“…Since their degradation and damage level can be affected by working operations as well as other disturbances in harsh manufacturing environments, such as lubricants, soiling, and temperature, the results from traditional wear and fatigue models are connected with considerable uncertainties. DT-driven condition monitoring (CM) and predictive maintenance (PdM) highlight the possibility for the process-parallel monitoring and diagnosis of the health status of the critical components (i.e., tools [ 116 , 117 ], bearings [ 118 ], ball screws [ 119 ], gears [ 15 , 120 , 121 ], pumps [ 122 ]) and the energy efficiency of the equipment [ 123 , 124 ] in order to handle the conflict between the unplanned maintenance operations and the resulting costs and productivity, particularly in SMEs, due to their limited capacity for the full deployment of a PdM strategy [ 125 ]. Additionally, DT-based optimal control [ 126 , 127 ] as well as machine dynamics issues [ 128 , 129 ] from a rotating system [ 130 ] to feed drive [ 131 , 132 ] are other important aspects.…”

Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

“…While recent advances concerning CPS and the IIoT contribute to promising developments with an application in production [4], [10], [11], [14]- [16], [19], [21]- [23], [25], [31], [41], [45], [46], [52], [62], [65], [68], [70]- [72], [76], [77], [81], [83], [97]- [99], [108], [109], the need for security to enable novel forms of industrial collaboration must be especially considered for real-world deployments, where CPS are likely connected to the Internet. Apart from operational reasons (e.g., to ensure safety or privacy), secure approaches also convince traditionally conservative stakeholders, such as companies that fear for their competitiveness, to contribute to cross-domain and inter-company collaborations.…”