Sustainability Evaluation of Process Planning for Single CNC Machine Tool under the Consideration of Energy-Efficient Control Strategies Using Random Forests

Zhang, Chaoyang; Jiang, Pingyu

doi:10.3390/su11113060

Cited by 15 publications

(11 citation statements)

References 28 publications

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“…Research by Moreira et al [14] presents an analysis of the impact of machining parameters on energy consumption within the milling process of BS EN24T alloy (AISI 4340). Zhang et al [15] developed an energy-efficient machine control strategy. The results show that the reduction in energy consumption with this energy-efficient strategy may be up to 25 %.…”

Section: 𝑆𝑆𝐸𝐸𝐴𝐴 =mentioning

confidence: 99%

Energy Optimization for Milling 304L Steel using Artificial Intelligence Methods

Bousnina

Hamza

Yahia

2022

Int. J. Automot. Mech. Eng.

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With increased production and productivity in modern industry, particularly in the automotive, aeronautical, agro-food, and other sectors, the consumption of manufacturing energy is rapidly increasing, posing additional precautions and large investments to industries to reduce energy consumption at the manufacturing system level. This research proposes a novel energy optimisation using a response surface methodology (RSM) with artificial neural network (ANN) for machining processes that saves energy while improving productivity.The feed rate was discovered to be the most influential factor in this study, accounting for 84.13 percent of total energy consumed. Furthermore, it has been established that as the material removal rate (MRR) increases, energy efficiency (EE) declines. This optimization of cutting conditions gives us the optimal values of cutting speed Vc = 129.37 m/min, feed rate f = 0.098 mm/rev and depth of cut ap = 0.5 mm. This approach will allow us to decrease the total energy consumed (Etc) by 49.74 % and increase the energy efficiency (EE) by 13.63 %.

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Section: 𝑆𝑆𝐸𝐸𝐴𝐴 =mentioning

confidence: 99%

Energy Optimization for Milling 304L Steel using Artificial Intelligence Methods

Bousnina

Hamza

Yahia

2022

Int. J. Automot. Mech. Eng.

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“…Another popularly used model in the manufacturing process is the decision tree-based model. Among several models that use decision trees, we deployed random forest, gradient boosting, lightGBM, and CatBoost algorithms following prior studies that developed models for computer numerical control (CNC) machines [26]. The tree model consists of decision trees, and the advantage of this is we can extract feature importance to figure out which feature is important for quality prediction.…”

Section: Tree-based Modelmentioning

confidence: 99%

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

et al. 2021

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With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.

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“…Machine tools as one of the indispensable tools for manufacturing products are typical high-energy, high-carbon manufacturing systems due to the larger mass, longer life cycle with huge resources and energy consumption, and hence, environmental problems are particularly acute [19]. Therefore, the machine tool manufacturing industry must reduce non-renewable resources and energy consumption [20], and decrease environmental pollution [21], in order to alleviate the serious problem of resource constraints. In general, the key challenges of sustainable machine tools design are summarized as follows: Specifically, the design stage is one key step in the process of industrial production, which also determines product performance of most stages of the whole life cycle.…”

Section: Challenges and Opportunities Of Sustainable Machine Tool Designmentioning

confidence: 99%

The Analysis of Key Technologies for Sustainable Machine Tools Design

Feng

Shi

2020

Applied Sciences

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Machine tools as indispensable tools for manufacturing products are typical high-energy, high-carbon manufacturing systems due to their larger mass, longer life cycles and huge resources and energy consumption. The current research trend of sustainable machine tools aims to reduce cost, energy consumption and increase sustainability without compromising their functionality, usability, productivity, accuracy, etc. However, there is a lack of systematic reviews about what are the key technologies for sustainable machine tools design. Thus, a comprehensive literature review of machine tool design for sustainability is needed in order to make clear how to design and evaluate sustainable machine tools from the viewpoint of life cycle sustainability analysis. The aim of this paper is to review the sustainable design and assessment aspects of machine tool design from partial goals to integrated aims according to whole sustainability dimensions such as the environment, economy and society, as well as involving key techniques in different life cycle stages. Recent research and study on improving directly or indirectly sustainability performance of machine tools according to focus points could be summarized as: design optimization of components such as lightweight using topology and bionic methods; structure design with modular design and layout; reducing cutting fluids and lubricant oil by employing minimum quantity lubrication, dry and cryogenic machining; reducing energy consumption; waste reduction by reusing, remanufacturing and recycling; sustainability assessment i.e., energy model, life cycle cost and life cycle social benefit. This paper assists designers and manufacturers to improve sustainability of machine tools by specific optimization measures in their activities.

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Sustainability Evaluation of Process Planning for Single CNC Machine Tool under the Consideration of Energy-Efficient Control Strategies Using Random Forests

Cited by 15 publications

References 28 publications

Energy Optimization for Milling 304L Steel using Artificial Intelligence Methods

Energy Optimization for Milling 304L Steel using Artificial Intelligence Methods

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

The Analysis of Key Technologies for Sustainable Machine Tools Design

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