Simulation and Analysis of Ball-End Milling of Panel Moulds Based on Deform 3D

Yang, Lin; Zheng, Minli

doi:10.2507/ijsimm16(2)co9

Cited by 11 publications

(10 citation statements)

References 11 publications

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“…Simulation approaches, similar to the one presented in this paper, have also proven useful in improving industrial production processes, e.g., modelling of the metal casting processes [15] and simulation and analysis of ball-end milling of panel moulds [16], as well as for solving problems such as improvement of workplace design which considers ergonomics and productivity [12]. An approach to performance analysis based on simulation in the narrow context of mass storage systems is described in [17].…”

Section: Related Workmentioning

confidence: 99%

Simulation of Photogrammetry-Based 3D Data Acquisition

Gajić¹,

Mihic²,

Драган³

et al. 2019

Int. j. simul. model.

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The paper presents a simulation approach to photogrammetry-based three-dimensional (3D) data acquisition. Photogrammetry requires capturing of series of overlapping photos with certain properties from which 3D reconstruction is later obtained. Scanning a building or a human or jewellery requires different numbers of cameras, setup parameters, spatial orientations, etc. Without precise information on how to effectively take photos, obtaining them can be tedious work without any guarantees that it will provide sufficient 3D reconstruction quality. The proposed simulation approach aims to ease the aforementioned burdens and contributes by improving the process of photogrammetry-based 3D data acquisition. The presented simulator is tested in the context of the development of a 3D scanning system for human body scanning and avatar creation. The experiments confirm that the proposed method leads to an improved quality of 3D object reconstruction in comparison to previous practice in the field of 3D human scanning. Further, it lowers the cost and shortens the time required for the industrial process of construction of 3D scanning systems, thus confirming the value and validity of the presented approach.

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Section: Related Workmentioning

confidence: 99%

Simulation of Photogrammetry-Based 3D Data Acquisition

Gajić¹,

Mihic²,

Драган³

et al. 2019

Int. j. simul. model.

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“…Moreover, material parameters are not the only factors affecting the induction heating. Internal stress caused by machining [23] or heat treatment may significantly influence the dynamics of the process. In order to solve the electromagnetic problem, Maxwell equations are used (Fig.…”

Section: Selective Induction Heatingmentioning

confidence: 99%

Simulation Study of Induction Heating of Multi-Metallic Injection Moulds

Mrozek¹

2018

Int. j. simul. model.

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This paper presents a concept and a simulation study into induction heating of multi-metallic injection moulds. This novel technology involves heating only selected cavity walls which are critical areas in forming thin-walled parts. Such regions are often places, where defects occur. In order eliminate the defects, it requires increasing the temperature of selected critical forming walls within a single cavity. In this work, simplified models of forming inserts of different shapes were created. Walls intended for induction heating were selected and separated. Forming cavities were defined as paramagnetic materials, whereas, heated walls were made of ferromagnetic steel 1.2343. Induction coil was placed 1 -5 mm away from the wall. As a current source 10 kW induction generator EFD Minac 6 was used. The current frequency was 25 kHz. The obtained results show that it is possible, without a significant cycle time growth, to dynamically increase the temperature of selected walls by applying selective induction heating technology.

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“…Recently, investigators solved the trade-off among multiresponses regarding surface quality, energy consumed, and processing efficiency. The technological responses included are cutting energy [11], tool wear [12], cutting force [13][14][15][16], residual stress [17], material removal rate [18], and machining time [19]. Additionally, the surface geometry [20], chip formation [21,22], micro-hardness, and microstructure [23] were considered as important technical outputs.…”

Section: Introductionmentioning

confidence: 99%

Optimization Parameters of Milling Process of Mould Material for Decreasing Machining Power and Surface Roughness Criteria

et al. 2019

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Improving milling performances is an effective solution to decrease the costs required. This paper addressed a multi-response optimization to simultaneously decrease the machining power consumed Pm, arithmetical roughness Ra, and ten-spot roughness Rz. The Grey-Response Surface Method-Multi Island Genetic Algorithm (GRMA) consisting of grey relational analysis (GRA), response surface method (RSM), and multi-island genetic algorithm (MA) was proposed to predict the optimal parameters and yield optimum milling performances. The experimental trials were conducted with the support of a CNC milling center. The influences of spindle speed (S), depth of cut (ap), feed rate (fz), and tip radius (r) were explored using GRA. The nonlinear relationship between machining parameters and grey grade (GG) model was developed using RSM. Finally, two optimization techniques, including desirability approach (DA) and MA were performed to observe the optimal values. The results indicated that the machining power was greatly affected by processing factors and the radius has a significant impact on the roughness criteria. The measured reductions using optimal parameters of Pm, Ra, and Rz are approximately 77.05%, 50.00%, and 58.02%, respectively, as compared to initial settings. The GRMA can be considered as an effective approach to generate reliable values of processing conditions and technological performances in the milling process.

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Simulation and Analysis of Ball-End Milling of Panel Moulds Based on Deform 3D

Cited by 11 publications

References 11 publications

Simulation of Photogrammetry-Based 3D Data Acquisition

Simulation of Photogrammetry-Based 3D Data Acquisition

Simulation Study of Induction Heating of Multi-Metallic Injection Moulds

Optimization Parameters of Milling Process of Mould Material for Decreasing Machining Power and Surface Roughness Criteria

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