Floor surface roughness model considering tool vibration in the process of micro-milling

Lü, Xiaohong; Zhang, Haixing; Zhang, Jia; Feng, Yixuan; Liang, Steven Y.

doi:10.1007/s00170-017-1123-2

Cited by 34 publications

(9 citation statements)

References 23 publications

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“…Sensor technologies are essential to elicit tacit knowledge, for example the tacit knowledge of the operator can be captured by a 'sensorized' hand-held belt grinder and a 3D scanner to generate a program of a robot that can replace the operator [67]. The modeling of the physical reality and realizing it in the CPS are critical tasks [68][69][70][71].…”

Section: Virtual Operatormentioning

confidence: 99%

Enabling Technologies for Operator 4.0: A Survey

et al. 2018

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The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space.

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Section: Virtual Operatormentioning

confidence: 99%

Enabling Technologies for Operator 4.0: A Survey

et al. 2018

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“…Micro-milling process is exclusively developed to make tiny components with greater geometric complexities and highest level of precisions. Application of micro-milling could be seen in aerospace, electronics, biomedical, and robotics (Lu et al 2018). This process considers end mill tool (dia in the range of 90-450 µm) and edge radius (0-5 µm).…”

Section: Micro-milling Processmentioning

confidence: 99%

CFNN-PSO: An Iterative Predictive Model for Generic Parametric Design of Machining Processes

Ghosh

Martinsen

2019

Applied Artificial Intelligence

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Every production process consists of a large number of dependent and independent variables, which substantially influence the quality of the machined parts. Due to the large impact of process variabilities, it is difficult to design optimal models for the machining processes. Mathematical or numerical models for production processes are resource driven which are not cost effective approaches in terms of computation and economical production. In this article, a new artificial neural network (ANN) based predictive model, is introduced which exploits particle swarm optimization (PSO) algorithm to minimize the root mean square errors (RMSE) for the network training. This approach can effectively obtain an optimized predictive model that can calculate precise output responses for the production processes. In order to verify the proposed approach, two case studies are considered from literature and shown to produce significant improvements. Further, the proposed model is validated on abrasive water jet machining (AWJM) with industrial garnet abrasives and optimal machining conditions have been obtained with optimized responses, which are substantially improved while compared with grey relational analysis (GRA).

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“…Mustapha [24] developed a hybrid analytical model for estimating the transverse vibration response of micro-end-milling and compared the response profiles from the experiment and the developed model, which showed reasonably close similarity. Lu [25] built a surface roughness prediction model using the measured results of tool vibration and predicted the floor surface roughness in the micro-milling of straight grooves with good accuracy.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Cutting Vibration during Micro-End-Milling of the Straight Groove

Howard

Pang

et al. 2020

Micromachines

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Micro-end-milling is a cutting technology that removes redundant material from machined workpieces by small-diameter end mills, and is widely used to manufacture miniature complex parts. During micro-end-milling, the cutting vibration caused by weak tool rigidity and high spindle speed is known as a key factor for decreasing machined quality and accelerating tool failure. This study reports on experiments of micro-end-milling of the straight groove for AISI 1045 steel. The waveform characteristics of acceleration vibration were revealed, the relationship between acceleration and milling parameters were analyzed and two types of relationship models were developed. The results show that, during micro-end-milling of the straight groove, the components of acceleration vibration from largest to smallest are in turn the transverse acceleration αY, the feed acceleration αX and the axial acceleration αZ. Compared with feed velocity vf and axial depth of cut ap, the spindle speed n has the highest influence on cutting vibration. The response surface model of acceleration vibration was shown to have a higher prediction accuracy compared to the power function model and is more suitable for the prediction and control of cutting vibration during micro-end-milling.

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Floor surface roughness model considering tool vibration in the process of micro-milling

Cited by 34 publications

References 23 publications

Enabling Technologies for Operator 4.0: A Survey

Enabling Technologies for Operator 4.0: A Survey

CFNN-PSO: An Iterative Predictive Model for Generic Parametric Design of Machining Processes

Experimental Investigation of Cutting Vibration during Micro-End-Milling of the Straight Groove

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