Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Pangestu, Phengky; Pujiyanto, Eko; Rosyidi, Cucuk Nur

doi:10.1016/j.heliyon.2021.e06043

Cited by 32 publications

(18 citation statements)

References 33 publications

(108 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Target users are decision-makers with top and middle management positions (Barletta et al, 2021). Pangestu et al (2021) presented the concept multi-objective multi-pass turning optimization model to determine the optimal cutting parameters, including spindle rotation speed, feed rate, depth of cut, and several roughing passes. The model is aimed at the manufacturing sector to improve the efficiency of production processes efficiency while ensuring the production of products that meet the SM criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Improving the Level of Sustainable Development in Industry 4.0 Context: A New Approach

Patals-Maliszewska¹,

Łosyk²

2021

ERSJ

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, the production needs to arrange low-carbon manufacturing without increasing costs and reducing efficiency (Pangestu et al, 2021). Sustainable Manufacturing (SM) models are currently being sought, the implications of which in manufacturing companies will be an integral part of their functioning in the I4.0 concept.…”

Section: Introductionmentioning

confidence: 99%

Improving the Level of Sustainable Development in Industry 4.0 Context: A New Approach

Patals-Maliszewska¹,

Łosyk²

2021

ERSJ

View full text Add to dashboard Cite

“…Therefore, domestic and foreign scholars are increasingly active in the research on energy consumption modeling and energy-saving and emission reduction methods of CNC machine tools [3][4][5][6]. In the process of CNC machining, the reasonable selection of process parameters not only affects the indexes of machining cost [7], quality [8] and efficiency [9], but also is closely related to the energy consumption of machine tools [10]. How to optimize the process parameters in the machining process of CNC machine tools is an urgent basic scientific problem to be solved under the background of green manufacturing [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption

Jia

Wang

et al. 2021

Sustainability

View full text Add to dashboard Cite

Energy-saving and emission reduction are recognized as the primary measure to tackle the problems associated with climate change, which is one of the major challenges for humanity for the forthcoming decades. Energy modeling and process parameters optimization of machining are effective and powerful ways to realize energy saving in the manufacturing industry. In order to realize high quality and low energy consumption machining of computer numerical control (CNC) lathe, a multi-objective optimization of CNC turning process parameters considering transient-steady state energy consumption is proposed. By analyzing the energy consumption characteristics in the process of machining and introducing practical constraints, such as machine tool equipment performance and tool life, a multi-objective optimization model with turning process parameters as optimization variables and high quality and low energy consumption as optimization objectives is established. The model is solved by non-dominated sorting genetic algorithm-II (NSGA-II), and the pareto optimal solution set of the model is obtained. Finally, the machining process of shaft parts is studied by CK6153i CNC lathe. The results show that 38.3% energy consumption is saved, and the surface roughness of workpiece is reduced by 47.0%, which verifies the effectiveness of the optimization method.

show abstract

“…Liu et al [24] proposed a multi-objective optimization model of cutting parameters for high-efficiency and low-carbon manufacturing with cutting speed and feed rate as optimization variables, carbon emissions and processing time as optimization targets, and a nondominated sequencing genetic algorithm was used to obtain the best turning cutting parameters. Meanwhile, intelligent algorithms are used to solve the optimization solution and multi-objective issues, such as the ant colony algorithm [25][26][27], Taguchi-Grey relational analysis [28], Pareto front [29], multi-pass turning optimization [30], Taguchi based grey relational analysis [31], NSGA-Ⅱ [32], and so on.…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Energy Consumption of Holes Machining Integrating the Optimization of Tool Path and Cutting Parameters on CNC Machines

Feng

Chen

Zhang

et al. 2021

Preprint

View full text Add to dashboard Cite

The application of sustainable manufacturing technologies is the new challenge faced by enterprises, industries, and researchers under the background of supporting carbon peak and carbon neutral. This paper studies how to reduce the energy consumption of holes machining through optimizing tool path and cutting parameters simultaneously. The integrated optimization methodology can further reduce the energy consumption comparing with optimizing the tool path or cutting parameters separately. Firstly, the energy model of holes machining is established based on machine tools’ energy composition, tool path planning, and process parameters. Due to tool path planning as air cutting process has big relationship with reducing energy, especially for holes group with a big proportion in the whole process. The tool path of holes processing is optimized by the improved ant colony algorithm to solve the issue considering the distance from one hole to the next hole. Based on this optimized path, a multi-objective optimization model for hole cutting parameters is established, considering the spindle speed and feed rate as the optimization variables and machining time, energy consumption, and surface roughness as the objective function. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) is employed to solve the multi-objective optimization problem of holes machining. The case study with 50 holes is used to testify the application of the proposed method to provide the practical energy efficiency strategy for holes group or multi-hole parts on CNC machines assisting in achieving sustainable production in manufacturing sectors.

show abstract

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Cited by 32 publications

References 33 publications

Improving the Level of Sustainable Development in Industry 4.0 Context: A New Approach

Improving the Level of Sustainable Development in Industry 4.0 Context: A New Approach

Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption

Minimizing the Energy Consumption of Holes Machining Integrating the Optimization of Tool Path and Cutting Parameters on CNC Machines

Contact Info

Product

Resources

About