scholarly journals Transfer Learning Based Surface Roughness Prediction Integrating Tool Wear Under Variable Cutting Parameters

2021 ◽  
Author(s):  
Yahui Wang ◽  
Lianyu Zheng ◽  
Yiwei Wang ◽  
Jian Zhou ◽  
Fei Tao
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Surface Quality ◽  
Transfer Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Cutting Parameters ◽  
Practical Significance ◽  
Surface Roughness Prediction ◽  
Roughness Prediction

Abstract The monitoring of surface quality in machining is of great practical significance for the reliability and life of high-value products such as rocket, spacecraft and aircraft, particularly for their assembly interfaces of these products. Surface roughness is an important metric to evaluate the surface quality. The current research of online surface roughness prediction has the following limitations. The effect of the varying tool wear on the surface roughness is rarely considered in machining. In addition, the deteriorating trend of surface roughness and tool wear is different under variable cutting parameters. Prediction models trained under one set of cutting parameters fail when cutting parameters change. This paper proposes a surface roughness prediction method considering the varying tool wear under variable cutting parameters. A stacked autoencoder and long short-term memory network (SAE-LSTM) is designed as the basic surface roughness prediction model that uses tool wear conditions and sensor signals as the input. The transfer learning strategy is applied on SAE-LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (Ti6Al4V material) of the aircraft’s vertical tail are conducted and the monitoring data are used to validate the proposed method. Ablations studies are carried out to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and well track the true surface roughness over time.

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

2020 ◽  
pp. 095440542097106
Author(s):  
Yu Su ◽  
Congbo Li ◽  
Guoyong Zhao ◽  
Chunxiao Li ◽  
Guangxi Zhao
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Tool Wear ◽  
Specific Energy ◽  
Machine Tools ◽  
Prediction Models ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Support Vector

The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

Prediction Models for Energy Consumption and Surface Quality in Stainless Steel Milling

2021 ◽  
Author(s):  
Shuo Yu ◽  
Guoyong Zhao ◽  
Chunxiao Li ◽  
Shuang Xu ◽  
Zhifu Zheng
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Energy Consumption ◽  
Tool Wear ◽  
Surface Quality ◽  
Specific Energy ◽  
Prediction Models ◽  
Specific Energy Consumption ◽  
Surface Hardness ◽  
Machined Surface

Abstract Stainless steel is a kind of difficult-to-machine material, and the work hardening in milling easily leads to high energy consumption and poor surface quality. Thus, the influence of machined surface hardness on energy consumption and surface quality cannot be ignored. To solve this problem, the prediction models for machine tool specific energy consumption and surface roughness are developed with tool wear and machined surface hardness considered firstly. Then, the validity of the models is verified through AISI 304 stainless steel milling experiments. The results show that the prediction accuracy of the machine tool specific energy consumption model can reach 98.7%, and the roughness model can reach 96.8%. Later, according to the developed prediction models, the influence of milling parameters, surface hardness, and tool wear on the machine specific energy consumption and surface roughness is studied. Results show that in stainless steel milling, the most significant parameters for surface roughness is the machined surface hardness, while that for energy consumption is the feed per tooth. The machine specific energy consumption increases linearly with the increase of the tool wear and the machined surface hardness gradually. The proposed models are helpful to optimize the process parameters for high efficiency and high quality machining of stainless steel.

Surface Roughness Prediction Using Numerical Scheme and Feedback Control

2015 ◽  
Author(s):  
Michael K. O. Ayomoh ◽  
Khaled A. Abou-El-Hossein ◽  
Sameh F. M. Ghobashi
Keyword(s):  
Surface Roughness ◽  
Feedback Control ◽  
High Carbon Steel ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Experimental Sample ◽  
Surface Roughness Prediction ◽  
Difference Analysis ◽  
Roughness Prediction

This paper proposes a numerical modelling scheme for surface roughness prediction. The approach is premised on the use of 3D difference analysis method enhanced with the use of feedback control loop where a set of adaptive weights are generated. The surface roughness values utilized in this paper were adapted from [1]. Their experiments were carried out using S55C high carbon steel. A comparison was further carried out between the proposed technique and those utilized in [1]. The experimental design has three cutting parameters namely: depth of cut, feed rate and cutting speed with twenty-seven experimental sample-space. The simulation trials conducted using Matlab software is of two sub-classes namely: prediction of the surface roughness readings for the non-boundary cutting combinations (NBCC) with the aid of the known surface roughness readings of the boundary cutting combinations (BCC). The following simulation involved the use of the predicted outputs from the NBCC to recover the surface roughness readings for the boundary cutting combinations (BCC). The simulation trial for the NBCC attained a state of total stability in the 7th iteration i.e. a point where the actual and desired roughness readings are equal such that error is minimized to zero by using a set of dynamic weights generated in every following simulation trial. A comparative study among the three methods showed that the proposed difference analysis technique with adaptive weight from feedback control produced a much accurate output as against the abductive and regression analysis techniques presented in [1].

Development of Surface Roughness Prediction for Steel in CNC Turning by Using Response Surface Method

2011 ◽  
Vol 335-336 ◽  
pp. 921-926
Author(s):  
Siriwan Chanphong ◽  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Response Surface ◽  
Cutting Temperature ◽  
Cutting Parameters ◽  
Plain Carbon Steel ◽  
Cutting Condition ◽  
Surface Roughness Prediction ◽  
Roughness Prediction ◽  
Coated Carbide

This research presents the development of the surface roughness prediction in the turning process of the plain carbon steel with the coated carbide tool by using the response surface analysis with the Box-Behnken design. The effects of cutting parameters on the cutting force and the cutting temperature are investigated. The cutting force and the cutting temperature are measured to help analyze the relation between the surface roughness and the cutting conditions. The models of cutting force ratio and the cutting temperature are also proposed based on the experimental data. The surface plots are constructed to determine the optimum cutting condition referring to the minimum surface roughness.

Tool Wear and Surface Roughness in Diamond Cutting of Die Steels

2013 ◽  
Vol 589-590 ◽  
pp. 227-231 ◽  
Author(s):  
Lai Zou ◽  
Guo Jun Dong ◽  
Ming Zhou
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Surface Quality ◽  
Ultrasonic Vibration ◽  
Cutting Parameters ◽  
Experimental Investigations ◽  
Depth Of Cut ◽  
Machined Surface ◽  
Die Steels ◽  
Micro Morphology

This paper performed a series of experimental investigations for typical die steels with ultrasonic vibration assisted turning. The micro-morphology of rake face and flank face of diamond was detected by scanning electron microscopy, and the roughness of machined surface was measured by Form Talysurf. In order to clarify the influence laws of cutting parameters and tool geometric parameters on tool wear and surface quality. The results revealed that the wear of diamond and surface roughness rely heavily on the feed rate, and have less relativity with the relief angle and the depth of cut to an extent. In addition, the function mechanism of ultrasonic vibration turning had been analyzed, it exhibited that this technological measure has enhanced tool life and improved surface quality to a large extent.

Sign in / Sign up

Export Citation Format

Share Document