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

2021 ◽  
Author(s):  
Chunhua Feng ◽  
Xiang Chen ◽  
Jingyang Zhang ◽  
Yugui Huang ◽  
Zibing Qu
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Ant Colony Algorithm ◽  
Tool Path ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Tool Path Planning ◽  
Machining Time ◽  
Cnc Machines ◽  
Multi Objective

Abstract 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.

Region based Efficient CNC Machining using Point Cloud Data

2020 ◽  
pp. 1-35
Author(s):  
Mandeep Dhanda ◽  
Aman Kukreja ◽  
Sanjay Pande
Keyword(s):  
Path Planning ◽  
Surface Quality ◽  
Point Cloud ◽  
Tool Path ◽  
Cnc Machining ◽  
Real Surface ◽  
Tool Path Planning ◽  
Machining Time ◽  
Cloud Data ◽  
Planning Strategy

Abstract This paper presents an efficient tool path planning strategy for three-axis CNC machining using curvature based segmentation (CBS) of freeform surface from its representation in the form of a point cloud. Curvature parameters estimated over the point data are used to partition the surface into convex, concave, and saddle like regions. Grid based adaptive planar tool path planning strategy is developed to machine each region separately within its boundaries. In addition to the region by region machining, strategy to stitch the obtained regions is also developed to minimize the tool lifts and tool marks. The developed region based tool path planning strategy is compared with the point cloud based adaptive planar strategy, iso-scallop strategy and commercial software for parts with various complexities. The result shows significant improvement in terms of performance parameters viz. machining time, tool path length and code length while maintaining the desired part surface quality. The proposed method is also tested by machining a real surface and analyzing its surface quality.

scholarly journals Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 217 ◽  
Author(s):  
Yu Su ◽  
Guoyong Zhao ◽  
Yugang Zhao ◽  
Jianbing Meng ◽  
Chunxiao Li
Keyword(s):  
Energy Consumption ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
Relational Analysis ◽  
304 Austenitic Stainless Steel ◽  
Grey Relational ◽  
Optimization Of Cutting Parameters

Energy conservation and emission reduction is an essential consideration in sustainable manufacturing. However, the traditional optimization of cutting parameters mostly focuses on machining cost, surface quality, and cutting force, ignoring the influence of cutting parameters on energy consumption in cutting process. This paper presents a multi-objective optimization method of cutting parameters based on grey relational analysis and response surface methodology (RSM), which is applied to turn AISI 304 austenitic stainless steel in order to improve cutting quality and production rate while reducing energy consumption. Firstly, Taguchi method was used to design the turning experiments. Secondly, the multi-objective optimization problem was converted into a simple objective optimization problem through grey relational analysis. Finally, the regression model based on RSM for grey relational grade was developed and the optimal combination of turning parameters (ap = 2.2 mm, f = 0.15 mm/rev, and v = 90 m/s) was determined. Compared with the initial turning parameters, surface roughness (Ra) decreases 66.90%, material removal rate (MRR) increases 8.82%, and specific energy consumption (SEC) simultaneously decreases 81.46%. As such, the proposed optimization method realizes the trade-offs between cutting quality, production rate and energy consumption, and may provide useful guides on turning parameters formulation.

scholarly journals Analysis and Multi-Objective Optimization for Reducing Energy Consumption and Improving Surface Quality during Dry Machining of 304 Stainless Steel

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4693
Author(s):  
Feilong Du ◽  
Lin He ◽  
Haisong Huang ◽  
Tao Zhou ◽  
Jinxing Wu
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Stainless Steel ◽  
Energy Consumption ◽  
Surface Quality ◽  
Feed Rate ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Multi Objective Optimization ◽  
Multi Objective

Cutting quality and production cleanliness are main aspects to be considered in the machining process, and determining the optimal cutting parameters is a significant measure to reduce energy consumption and optimize surface quality. In this paper, 304 stainless steel is adopted as the research objective. The regression models of the specific cutting energy, surface roughness, and microhardness are constructed and the inherent influence mechanism between cutting parameters and output responses are analyzed by analysis of variance (ANOVA). The desirability analysis method is introduced to perform the multi-objective optimization for low energy consumption (LEC) mode and low surface roughness (LSR) mode. Optimal combination of process parameters with composite desirability of 0.925 and 0.899 are obtained in such two modes respectively. As indicated by the results of multi-objective genetic algorithm (MOGA), genetic algorithm (GA) combined with weighted-sum-type objective function and experiment, the relative deviation values are within 10%. Moreover, the results also reveal that the feed rate is the most significant factor affecting the three responses, while the correlation of cutting depth is less noticeable. The effect of low feed rate on microhardness is primarily related to the mechanical load caused by extrusion, and the influence at high feed rate is determined by plastic deformation.

scholarly journals A Systematic Method of Optimization of Machining Parameters Considering Energy Consumption, Machining time and Surface Roughness with Experimental Analysis

2021 ◽  
Author(s):  
Chunhua Feng ◽  
Haohao Guo ◽  
Jingyang Zhang ◽  
Yugui Huang ◽  
Shi Huang
Keyword(s):  
Experimental Data ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Cutting Parameters ◽  
Multi Objective Optimization ◽  
Systematic Method ◽  
Machining Time ◽  
Cutting Energy ◽  
Energy Consumption Model ◽  
Consumption Model

Abstract For improving energy efficiency of machining process, extensive studies have focused on how to establish energy consumption model and optimize cutting parameters. However, the existing methods lack a systematic method to promote the widespread use of energy efficiency methods in the industry. This paper proposes a systematic method integrating energy model, experiment design, and multi-objective optimization model. Firstly, the energy model is established considering cutting energy and non-cutting energy. Then, the orthogonal experiment is designed with the three levels of four factors of spindle speed, feed speed, cutting depth, and cutting width in the X and Y cutting directions. The data of energy consumption, surface quality and machining time are obtained to study the effects of different cutting elements and cutting directions. Meanwhile, the standby, spindle idling, feed, SEC, material cutting and idling feed models of the CNC machine tools are established based on the experimental data. In addition, for verifying the accuracy of the established energy consumption model, five sets of experimental data are tested that show the prediction accuracy can reach 99.4%. Finally, a multi-objective optimization model for high efficiency and energy saving of processing process is establishes to optimize the cutting parameters from the three perspectives of energy consumption, processing time and surface quality. Combining the case of milling with constraints including machine tool performance, tool life, processing procedures, and processing requirements, the Pareto solution set is used to solve the Pareto of the target model. Through drawing a three-dimensional needle graph and two-dimensional histogram, the optimal cutting parameter combination for rough machining and semi-finish machining are provided, assisting in promoting the application of the sustainable techniques in the industry.

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