A single point diamond turning and integrated sensory system in nano machining: A survey, research issues and challenges

Chopade, Sandeep R.; Barve, Shivprakash B.

doi:10.1016/j.matpr.2023.09.070

Cited by 1 publication

(3 citation statements)

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“…Dynamic systems attempt to remove processing disturbances during mechanical contacts between the tool and workpiece [20]. However, these methods require sensors [21], which may include interferometry systems, and hence, more complex production setups. In addition, the material removal process becomes more complex by the addition of further process settings and control loops, and hence, optimization becomes more challenging.…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…Another one of these alloys is RSA 443 (40% of silicon and 60% aluminium) which likewise has a fine nanostructure [27]. However the anisotropic nature of RSA materials is a challenge because it affects surface roughness in ways that are difficult to predict [21]. To some extent, this is being addressed by molecular dynamic simulations [28]; however, this technique is limited to small ensembles of atoms, often in a single plane, which is not truly representative of three-dimensional geometry.…”

Section: Machining Of Rsa Materialsmentioning

confidence: 99%

“…Finding an optimization between the turning parameters (cutting speed, cutting feed rate, and cutting depth) is not straight forward. Generally, small depths of cut result in better surface roughness [21]. However, this results in longer machining times, thus, adversely affecting production economics.…”

Section: Optimization Of Turning Parametersmentioning

confidence: 99%

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Optimization of Surface Roughness of Aluminium RSA 443 in Diamond Tool Turning

Mbangu Tambwe,

Pons

2024

JMMP

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Context—Rapidly solidified aluminium alloy (RSA 443) is increasingly used in the manufacturing of optical mold inserts because of its fine nanostructure, relatively low cost, excellent thermal properties, and high hardness. However, RSA 443 is challenging for single-point diamond machining because the high silicon content mitigates against good surface finishes. Objectives—The objectives were to investigate multiple different ways to optimize the process parameters for optimal surface roughness on diamond-turned aluminium alloy RSA 443. The response surface equation was used as input to three different artificial intelligence tools, namely genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), which were then compared. Results—The surface roughness machinability of RSA443 in single-point diamond turning was primarily determined by cutting speed, and secondly, cutting feed rate, with cutting depth being less important. The optimal conditions for the best surface finish Ra = 14.02 nm were found to be at the maximum rotational speed of 3000 rpm, cutting feed rate of 4.84 mm/min, and depth of cut of 14.52 µm with optimizing error of 3.2%. Regarding optimization techniques, the genetic algorithm performed best, then differential evolution, and finally particle swarm optimization. Originality—The study determines optimal diamond machining parameters for RSA 443, and identifies the superiority of GA above PSO and DE as optimization methods. The principles have the potential to be applied to other materials (e.g., in the RSA family) and machining processes (e.g., turning, milling).

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Section: Hybrid Methodsmentioning

confidence: 99%