Research on a Five-Axis Machining Center Worktable with Bionic Honeycomb Lightweight Structure

Chen, Yujia; Zha, Jun; Kan, Fan; Long, Hao; Chen, Yaolong

doi:10.3390/ma14010074

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…However, this increase in production and efficiency has not come without environmental consequences. The simultaneous escalation of energy consumption has underscored the urgent need for sustainable manufacturing practices that minimize resource utilization, improve energy efficiency [4,5], and promote environmentally friendly methodologies. Through a series of carefully designed experiments conducted on a CNC five-axis milling machine, this study investigates the complex interaction between various machining parameters, innovative toolpath planning strategies, and consequential energy consumption patterns.…”

Section: Introductionmentioning

confidence: 99%

Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency

Nugrahanto,

Gunawan,

Chen

2024

Sustainability

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Computer Numeric Control (CNC) five-axis milling plays a significant role in the machining of precision molds and dies, aerospace parts, consumer electronics, etc. This research aims to explore the potential of the machine learning (ML) technique in improving energy efficiency during the CNC five-axis milling process for sustainable manufacturing. The experiments with various machining parameters, forms of toolpath planning, and dry cutting conditions were carried out, and the data regarding energy consumption were collected simultaneously. The relationship between machine parameters and energy consumption was analyzed and built. Subsequently, a machine learning algorithm was developed to classify test methods and identify energy-efficient machining strategies. The developed algorithm was implemented and assessed using different classification methods based on the ML concept to effectively reduce energy consumption. The results show that the Decision Tree and Random Forest algorithms produced lower Root Mean Square Error (RMSE) values of 4.24 and 4.28, respectively, compared to Linear, Lasso, and Ridge Regression algorithms. Verification experiments were conducted to ascertain the real-world applicability and performance of the ML-based energy efficiency approach in an operational CNC five-axis milling machine. The findings not only underscore the potential of ML techniques in optimizing energy efficiency but also offer a compelling pathway towards enhanced sustainability in CNC machining operations. The developed algorithm was implemented within a simulation framework and the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of the machining results and integrating both conventional and advanced regression algorithms into CNC machining processes. Manufacturers stand to realize substantial energy savings and bolster sustainability initiatives, thus exemplifying the transformative power of ML-driven optimization strategies.

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Section: Introductionmentioning

confidence: 99%

Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency

Nugrahanto,

Gunawan,

Chen

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…However, this surge in efficiency and productivity has not come without its ecological repercussions. The simultaneous escalation of energy consumption and carbon emissions has underscored the urgent need for sustainable manufacturing practices that minimize resource utilization, improve energy efficiency [4,5], and promote environmentally conscious methodologies. Through a series of meticulously designed experiments conducted on a CNC 5-axis milling machine, this study delves into the intricate interplay between various machining parameters, innovative toolpath planning strategies, and consequential energy consumption patterns.…”

Section: Introductionmentioning

confidence: 99%

Innovative Approaches to Sustainable CNC Machining: A Machine Learning Perspective on Energy Optimization

Nugrahanto,

Gunawan,

Chen

2023

Preprint

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Industry 4.0 brings advanced technologies and automation, enabling greater productivity and efficiency. However, it also increases energy consumption and carbon emissions. Sustainable manufacturing in the era of Industry 4.0 is vital for addressing the environmental impact by minimizing resource consumption, optimizing energy efficiency, and adopting eco-friendly practices. CNC (Computer Numeric Control) 5-axis milling plays a significant role in the machining of precision molds and dies, aerospace, consumer electronics, etc. This research aims to explore the potential of the artificial intelligence (AI) technique in improving energy efficiency on CNC 5-axis milling process for sustainable manufacturing. The experiments were carried out using a CNC 5-axis milling machine with various machining parameters, toolpath planning, and dry cutting condition. Data on energy consumption of CNC 5-axis milling machine were collected, then the relationship between the machining parameters, toolpath planning, and energy consumption was analyzed and built. Furthermore, an AI-based optimization algorithm was developed that uses machine learning and optimization techniques to identify energy-efficient machining strategies. The developed algorithm was implemented within a simulation framework, the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of machining result. Finally, verification experiments were carried out to evaluate the applicability and performance of the AI-based energy optimization approach on a CNC 5-axis milling machine. By integrating AI into the CNC machining process, manufacturers can achieve significant energy savings, leading to reduced carbon emissions and improved sustainability.

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“…Honeycomb materials have good mechanical properties and excellent thermal conductivity, therefore, they are widely used in aerospace, construction, transportation, and other fields [1][2][3][4][5]. Additionally, there are many multicellular cell structures in nature, such as wood, bone, and the outer skin of plant cells that are light weight and have good mechanical properties, and therefore they have been the focus of many bionic studies [6][7][8][9]. Honeycomb materials without classification are called first-order honeycomb materials.…”

Section: Introductionmentioning

confidence: 99%

A New Type of Hierarchical Honeycomb in-Plane Impact Study

et al. 2021

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Honeycomb materials have low density, high specific strength and stiffness, impact resistance, and good sound insulation effect, and therefore are widely used in aerospace, automobile, and ship field applications. In this paper, we study the in-plane impact response of a second-order hierarchical honeycomb (SHH) material. Its main structure is a hexagonal honeycomb, and the substructure is composed of an augmented double arrow honeycomb (ADAH) negative Poisson’s ratio unit. Through a finite element simulation, the failure stress of an hierarchical honeycomb in two directions of quasi-static crushing and dynamic crushing was analyzed; the failure stress of the hierarchical honeycomb under different densities, different speeds, and different substructures was discussed; and the theoretical failure stress was verified. The numerical analysis results show that a second-order hierarchical honeycomb (SHH) has better collapse stress than a first-order regular hexagonal honeycomb (FHH) and an augmented double arrow honeycomb (ADAH).

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Research on a Five-Axis Machining Center Worktable with Bionic Honeycomb Lightweight Structure

Cited by 6 publications

References 18 publications

Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency

Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency

Innovative Approaches to Sustainable CNC Machining: A Machine Learning Perspective on Energy Optimization

A New Type of Hierarchical Honeycomb in-Plane Impact Study

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