Productivity Increase – Model-based optimisation of NC-controlled milling processes to reduce machining time and improve process quality

Brecher, Christian; Wiesch, Marian; Wellmann, Frederik Norbert

doi:10.1016/j.ifacol.2019.11.463

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…These models are driven by real-time production data, thus providing deeper insights than a priori simulation models. For example, based on high-frequency machine internal data, a digital twin of being manufactured part can be generated by material removal simulation, which further approximates the workpiece quality and enables a quick quality feedback loop [4,5]. Statistical process control has been widely used in the manufacturing and process industries to monitor the performance of a process over time and eliminate quality problems [6,7].…”

Section: Introductionmentioning

confidence: 99%

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

Benincá

Kehne

et al. 2021

Int J Adv Manuf Technol

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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.

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Section: Introductionmentioning

confidence: 99%

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

Benincá

Kehne

et al. 2021

Int J Adv Manuf Technol

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“…For instance, the surface quality of parts can be estimated in parallel to the process within a GPU-enabled (graphical processing unit) material removal simulation with a subsequent virtual measurement, which significantly reduces the latency between machining and the detection of defective parts [ 166 , 167 ]. A sufficiently accurate virtual representation of the machining process [ 26 , 168 , 169 , 170 , 171 ] also enables cause-and-effect analysis and, therefore, robust process design and control [ 172 , 173 , 174 , 175 , 176 ] as well as the exploitation of potential process productivity [ 177 ]. Further cases of production processes with knowledge-based approaches include welding [ 178 , 179 ], injection molding [ 180 , 181 ], linear winding [ 182 ], tape laying [ 183 ], metal forming [ 184 ], laser polishing [ 185 ], automated fiber placement [ 186 ], sheet molding compound [ 187 ], fused filament fabrication [ 188 ], and metal additive manufacturing (AM) [ 189 , 190 , 191 , 192 , 193 ].…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

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

Huang

Shen²,

et al. 2021

Sensors

167

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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“…Thus, optimization methods are actively being researched [12,26,46]. Particle swarm optimization promises to obtain optimal cutting parameters for certain requirements, such as roughness and tool lifetime [46].…”

Section: A Parametermentioning

confidence: 99%

“…Particle swarm optimization promises to obtain optimal cutting parameters for certain requirements, such as roughness and tool lifetime [46]. A model-based approach integrating realtime process data actively combines quality measurement data [12]. Thereby, the potential for optimization of the cutting process can be estimated, resulting in improved productivity.…”

Section: A Parametermentioning

confidence: 99%

Privacy-Preserving Production Process Parameter Exchange

Pennekamp

Buchholz

Lockner

et al. 2020

Annual Computer Security Applications Conference

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Productivity Increase – Model-based optimisation of NC-controlled milling processes to reduce machining time and improve process quality

Cited by 5 publications

References 7 publications

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

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

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

Privacy-Preserving Production Process Parameter Exchange

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