Characteristics of high-shear and low-pressure grinding for Inconel718 alloy with a novel super elastic composite abrasive tool

Gu, Zhiqiang; Tian, Yebing; Han, Jinguo; Wei, Chengwei; Babbar, Atul; Liu, Bing

doi:10.1007/s00170-022-10179-2

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…As a result, using AI has become a game-changing tactic to alter decision-making procedures and improve energy systems' sustainability, resilience, and efficiency [1,5]. The historical development of sustainable energy practices highlights the turning points and difficulties influencing today's energy industry [6]. It highlights the vital need for creative decision-making methods.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency of energy systems. This article conducts an exhaustive analysis of the critical aspects concerning the AI-sustainable energy nexus, encompassing the challenges in technological integration and the facilitation of intelligent decision-making processes pivotal for sustainable energy frameworks. It is demonstrated that AI applications, ranging from optimization algorithms to predictive analytics, possess a revolutionary capacity to bolster intelligent decision-making in sustainable energy. However, this integration is not without its challenges, which span technological complexities and socio-economic impacts. The article underscores the imperative for deploying AI in a manner that is transparent, equitable, and inclusive. Best practices and solutions are proposed to navigate these challenges effectively. Additionally, the discourse extends to recent advancements in AI, including edge computing, quantum computing, and explainable AI, offering insights into the evolving landscape of sustainable energy. Future research directions are delineated, emphasizing the importance of enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining socio-economic ramifications, exploring models of human-AI collaboration, fortifying security measures, and evaluating the impact of emerging technologies. This comprehensive analysis aims to inform academics, practitioners, and policymakers, guiding the creation of a resilient and sustainable energy future.

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Section: Introductionmentioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Kaur,

Kumar,

Singh

et al. 2024

JSE

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“…Sustainable technological development involves improving the conditions of micromachining by conducting roughing and finishing in a single pass [ 34 ]. In addition, it improves the quality of the achieved surface of machined workpieces.…”

Section: Introductionmentioning

confidence: 99%

Effects of a New Type of Grinding Wheel with Multi-Granular Abrasive Grains on Surface Topography Properties after Grinding of Inconel 625

et al. 2023

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Finishing operations are one of the most challenging tasks during a manufacturing process, and are responsible for achieving dimensional accuracy of the manufactured parts and the desired surface topography properties. One of the most advanced finishing technologies is grinding. However, typical grinding processes have limitations in the acquired surface topography properties, especially in finishing difficult to cut materials such as Inconel 625. To overcome this limitation, a new type of grinding wheel is proposed. The tool is made up of grains of different sizes, which results in less damage to the work surface and an enhancement in the manufacturing process. In this article, the results of an experimental study of the surface grinding process of Inconel 625 with single-granular and multi-granular wheels are presented. The influence of various input parameters on the roughness parameter (Sa) and surface topography was investigated. Statistical models of the grinding process were developed based on our research. Studies showed that with an increase in the cutting speed, the surface roughness values of the machined samples decreased (Sa = 0.9 μm for a Vc of 33 m/s for a multigranular wheel). Observation of the grinding process showed an unfavorable effect of a low grinding wheel speed on the machined surface. For both conventional and multigranular wheels, the highest value for the Sa parameter was obtained for Vc = 13 m/s. Regarding the surface topography, the observed surfaces did not show defects over large areas in the cases of both wheels. However, a smaller portion of single traces of active abrasive grains was observed in the case of the multi-granular wheel, indicating that this tool performs better finishing operations.

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“…The choice of tools in curved surface lapping will directly affect the nal processing result. Flexible bond abrasives have good elasticity and lapping characteristics, and have good surface adaptability when lapping curved surfaces, which can effectively improve the surface quality of workpieces and ful ll the high-quality lapping requirements for curved surface components [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

The constant force control based on an optimized environmental model for the robotic lapping of curved surface

Shi

Wang

et al. 2023

Int J Adv Manuf Technol

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During the curved surface lapping processing by robot, the surface quality of the processed workpieces cannot be fully guaranteed because of the uncontrollability of lapping contact force. Therefore, the accurate control of the contact force between the tool and the workpiece is important to improve the processing quality. This paper proposes an impedance control strategy based on an optimized environmental model to address the serious constraints brought by the uncertainty of the environmental model in impedance control on the compensation accuracy of contact force, and constructed a contact environmental model of elastic abrasives based on neural network algorithm, which improved the compensation accuracy of contact force through iterative iteration and prediction between the environmental model and the impedance model. Through comparison experiments, it is found that compared with the unoptimized method, the force control tracking error is reduced by 60.9%, the contact force variance is reduced by 25.7%, the contact force error during curved surface force control lapping process is reduced by 58.3% and the contact force variance is reduced by 52.5%, and the workpiece roughness Ra value is reduced by 37.2%. Therefore, the robotic constant force control system based on optimized environmental model can achieve a better force control effect, which can realize accurate and stable control of contact force during curved surface lapping process by robot, and has certain potential for engineering application.

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Characteristics of high-shear and low-pressure grinding for Inconel718 alloy with a novel super elastic composite abrasive tool

Cited by 12 publications

References 21 publications

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Effects of a New Type of Grinding Wheel with Multi-Granular Abrasive Grains on Surface Topography Properties after Grinding of Inconel 625

The constant force control based on an optimized environmental model for the robotic lapping of curved surface

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