scholarly journals On-Line Compensation for Micromilling of High-Aspect-Ratio Straight Thin Walls

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 603
Author(s):  
Yang Li ◽  
Xiang Cheng ◽  
Siying Ling ◽  
Guangming Zheng
Keyword(s):  
Aspect Ratio ◽  
Cutting Parameters ◽  
Thin Wall ◽  
Dimensional Error ◽  
Machining Quality ◽  
Thin Walls ◽  
Wall Deformation ◽  
On Line ◽  
Dimensional Errors ◽  
Parameter Compensation

In order to improve the machining quality and reduce the dimensional errors of micro high-aspect-ratio straight thin walls, the on-line cutting parameter compensation device has been introduced and corresponding micromilling processes have been investigated. Layered milling strategies for the micromilling of thin walls have been modeled and simulated for thin walls with different thicknesses based on the finite element method. The radial cutting parameters compensation method is adopted to compensate the thin wall deformation by raising the radial cutting parameters since the thin wall deformation make the actual radial cutting parameters smaller than nominal ones. The experimental results show that the dimensional errors of the thin wall have been significantly reduced after the radial cutting parameter compensation. The average relative dimensional error is reduced from 6.9% to 2.0%. Moreover, the fabricated thin walls keep good shape formation. The reduction of the thin wall dimensional error shows that the simulation results are reliable, which has important guiding significance for the improvement of thin wall machining quality, especially the improvement of dimensional accuracy. The experimental results show that the developed device and the machining strategy can effectively improve the micromilling quality of thin walls.

Modeling of Dimensional Errors in Slender Bar Turning Process Using Artificial Neural Networks

2009 ◽  
Vol 16-19 ◽  
pp. 549-553
Author(s):  
Bo Di Cui ◽  
Jian Liang Guo
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Model Simulation ◽  
Cutting Parameters ◽  
Turning Process ◽  
Dimensional Error ◽  
Radial Basis ◽  
Dimensional Errors ◽  
Bar Turning

Accurate predictive modeling is an essential prerequisite for optimization and control of production in modern manufacturing environments. For slender bar turning operations, dimensional deviation is one of the most important product quality characteristics due to the low stiffness of part. In this study, radial basis function neural network is employed to investigate dimensional errors in slender bar turning. The relationship between cutting parameters and dimensional errors is firstly described by the proposed model. Simulation is provided to investigate the effects of cutting parameters on dimensional errors. Further, real-time predictive model based on radial basis function neural network is developed to perform the dimensional error monitoring during slender bar turning process. Experiments verify that the proposed in-process predictive system has the ability to monitor efficiently dimensional errors within the range that they have been trained.

scholarly journals A computational method for detecting aspect ratio and problematic features in additive manufacturing

2021 ◽  
Author(s):  
Ruihuan Ge ◽  
Joseph Flynn
Keyword(s):  
Additive Manufacturing ◽  
Aspect Ratio ◽  
Thermal Stresses ◽  
Computational Method ◽  
Thin Wall ◽  
Thin Walls ◽  
Dimensionless Ratio ◽  
Complex Features ◽  
Further Development ◽  
Metal Additive

AbstractIn metal additive manufacturing, geometries with high aspect ratio (AR) features are often associated with defects caused by thermal stresses and other related build failures. Ideally, excessively high AR features would be detected and removed in the design phase to avoid unwanted failure during manufacture. However, AR is scale and orientation independent and identifying features across all scales and orientations is exceptionally challenging. Furthermore, not all high AR features are as easy to recognise as thin walls and fine needles. There is therefore a pressing need for further development in the field of problematic features detection for additive manufacturing processes. In this work, a dimensionless ratio (D1/D2) based on two distance metrics that are extracted from triangulated mesh geometries is proposed. Based on this method, geometries with different features (e.g. thin wall, helices and polyhedra) were generated and evaluated to produce metrics that are similar to AR. The prediction results are compared with known theoretical AR values of typical geometries.By combining this metric with mesh segmentation, this method was further extended to analyse the geometry with complex features. The proposed method provides a powerful, general and promising way to automatically detect high AR features and tackle the relevant defect issues prior to manufacture.

Researches on Milling Machining of Planar Thin Wall Parts

2015 ◽  
Vol 760 ◽  
pp. 457-462
Author(s):  
Ionuţ Gabriel Ghionea ◽  
Ioan Tănase ◽  
Adrian Ghionea
Keyword(s):  
Case Studies ◽  
Cutting Tools ◽  
Fem Analysis ◽  
Experimental Tests ◽  
Cutting Parameters ◽  
Thin Wall ◽  
Machining Parameters ◽  
Thin Walls ◽  
Cutting Force Components ◽  
Force Components

In the paper are presented some experimental and by simulation results obtained in the machining by milling of plane surfaces of thin walls parts. Two case studies are considered: when the thin wall is vertical and then the thin wall is horizontal positioned. Some values of the cutting force components are established using a modern dynamometer and a data acquisition system. The elastic deformation values of the machined part are also determined by experimental tests and simulated in a FEM analysis. Depending on the data that geometrically define the part and the cutting tools, their materials and the cutting parameters, are set values of the cutting forces and powers. There are presented the results of values comparison obtained by measuring during the processes with those established by applying FEM. Both case studies results lead to some remarks and useful recommendations for determining the machining parameters and the needed conditions for the technological system in the processing of parts with thin walls and minimum deformations.

scholarly journals Study on Deformation and Compensation for Micromilled Thin Walls With High Aspect Ratios

2021 ◽  
Author(s):  
Yang Li ◽  
Xiang Cheng ◽  
Siying Ling ◽  
Guangming Zheng ◽  
Lei He
Keyword(s):  
Cutting Force ◽  
Force Measurement ◽  
Dimensional Accuracy ◽  
Cutting Process ◽  
Machining Process ◽  
Thin Wall ◽  
Cutting Parameter ◽  
Thin Walls ◽  
Aspect Ratios ◽  
Wall Deformation

Abstract In order to further improve the dimensional accuracy of micromilled thin walls with high aspect ratios, the machining process should be actively controlled. An active cutting force measurement and cutting parameter compensation device is developed to realize the real-time measurement of radial cutting forces and compensation of radial cutting parameters in thin wall cutting process. Firstly, based on the cantilever beam deformation theory, a mathematical model is established to calculate the deformation and cutting force of thin walls. By measuring the cutting force, the thin wall deformation in the cutting process could be estimated. Then, the obtained incremental thin wall deformation is to be compared with the compensation threshold, which is set at 0.5 μm. If the value of the incremental deformation is less than 0.5 μm, compensation will not be processed. Otherwise, the incremental deformation is used as the compensation value for iterative compensation, until the incremental deformation of the thin wall is less than 0.5 μm. At last, a contrast experiment is carried out. The experimental results show that the introduced device and method are feasible. Machining quality of the thin wall has been obviously improved in dimension precision after the cutting parameter compensations.

scholarly journals Research on on-line ultrasonic testing of small diameter thin wall stainless steel straight welded pipe

2021 ◽  
Vol 1820 (1) ◽  
pp. 012086
Author(s):  
Huaishu Hou ◽  
Ding Lu ◽  
Shiwei Zhang ◽  
Yi Zhang ◽  
Chaolei Cheng
Keyword(s):  
Stainless Steel ◽  
Ultrasonic Testing ◽  
Small Diameter ◽  
Thin Wall ◽  
On Line ◽  
Welded Pipe ◽  
Wall Stainless Steel

The Real-Time Prediction of Surface Roughness Based on Genetic Wavelet Network

2010 ◽  
Vol 102-104 ◽  
pp. 610-614 ◽  
Author(s):  
Jun Chi ◽  
Lian Qing Chen
Keyword(s):  
Surface Roughness ◽  
Real Time ◽  
Back Propagation ◽  
Cutting Parameters ◽  
Wavelet Network ◽  
Back Propagation Algorithm ◽  
The Real ◽  
Roughness Model ◽  
On Line ◽  
Artificial Network

A methodology based on relax-type wavelet network was proposed for predicting surface roughness. After the influencing factors of roughness model were analyzed and the modified wavelet pack algorithm for signal filtering was discussed, the structure of artificial network for prediction was developed. The real-time forecast on line was achieved by the nonlinear mapping and learning mechanism in Elman algorithm based on the vibration acceleration and cutting parameters. The weights in network were optimized using genetic algorithm before back-propagation algorithm to reduce learning time.The validation of this methodology is carried out for turning aluminum and steel in the experiments and its prediction error is measured less than 3%.

scholarly journals Cosine Error-Free Metrology Tool Path Planning for Thickness Profile Measurements

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Xiangyu Guo ◽  
ChaBum Lee
Keyword(s):  
Path Planning ◽  
Wall Thickness ◽  
Tool Path ◽  
Measuring System ◽  
Profile Measurement ◽  
Thin Wall ◽  
Tool Path Planning ◽  
Thickness Profile ◽  
Thin Walls ◽  
Displacement Sensors

Abstract This paper presents a novel thickness profile measuring system that measures double-sided thin pipe wall surfaces in a non-contact, continuous, cosine error-free, and fast manner. The surface metrology tool path was developed to align the displacement sensors always normal to the double-sided surfaces to remove cosine error. A pair of capacitive-type sensors that were placed on the rotary and linear axes simultaneously scans the inner and outer surfaces of thin walls. Because the rotational error of the rotary axis can severely affect the accuracy in thickness profile measurement, such error was initially characterized by a reversal method. It was compensated for along the rotational direction while measuring the measurement target. Two measurement targets (circular and elliptical metal pipe-type thin walls) were prepared to validate the developed measurement method and system. Not only inner and outer surface profiles but also thin-wall thickness profiles were measured simultaneously. Based on the output data, the circularity and wall thickness variation were calculated. The thickness profile results showed a good agreement with those obtained by a contact-type micrometer (1-µm resolution) at every 6-deg interval. The uncertainty budget for this measuring system with metrology tool path planning was estimated at approximately 1.4 µm.

Improving the Accuracy of Multi-Axis Machines Through On-Line Error Compensation Using Neural Networks

1998 ◽  
Author(s):  
Abderrazak El Ouafi ◽  
Michel Guillot ◽  
Abdellah Bedrouni
Keyword(s):  
Error Compensation ◽  
Error Modeling ◽  
Precision Engineering ◽  
Dimensional Error ◽  
New Approach ◽  
Turning Center ◽  
On Line ◽  
Dynamic Errors ◽  
Compensation Approach ◽  
Compensation Function

Abstract This research is devoted to one of the most fundamental problems in precision engineering: multi-axis machines accuracy. The paper presents a new approach designed to support the implementation of software error compensation of geometric, thermal and dynamic errors for enhancing the accuracy of multi-axis machines. The accuracy of multi-axis machines can be significantly improved using an intelligent integration of sensor information to perform the compensation function. The compensation process consists of the following major steps carried out on-line: continuous monitoring of the machine conditions using position, force, speed and temperature sensors mounted on the machine structure. Error forecasting through sensor fusion. Volumetric error synthesis and software compensation. To improve the effectiveness of error modeling, an artificial neural network is extensively applied. Implemented on a turning center, the compensation approach has enabled improvement of the machine accuracy by reducing the maximum dimensional error from 70 μm initially to less than 4 μm.

A review of mechanical drilling on fiber metal laminates

2020 ◽  
pp. 002199832095774
Author(s):  
Eduardo Pires Bonhin ◽  
Sarah David-Müzel ◽  
Manoel Cléber de Sampaio Alves ◽  
Edson Cocchieri Botelho ◽  
Marcos Valério Ribeiro
Keyword(s):  
Mechanical Properties ◽  
Cutting Parameters ◽  
Burr Formation ◽  
Drilling Process ◽  
Machining Parameters ◽  
Specific Mass ◽  
Fiber Metal Laminates ◽  
Dimensional Error ◽  
Main Method ◽  
Fiber Metal

The use of fiber metal laminates (FML) in aeronautics components has been increased in the last years, mainly due to the gain in mechanical properties combined with low specific mass. However, in the assembly of these materials on the structures to which they will be attached, mechanical screwing is still the main method used, which requires the performance of drilling processes. Something it is very complicated for these materials and can cause damage that compromises the performance. Therefore, this work aims to approach and summarize the evolution of the mechanical drilling process on FML developed in the last years. By the work, the main problems that occur during the drilling of these materials are punctually approached, such as delamination, burr formation, dimensional error, poor roughness, and tool wear. In addition, it is presented how these problems are affected by the machining parameters (cutting parameters, geometry, material/coating tool, and cutting environment), as well as suggestions for minimizing process problems. Thus, the article intends to provide as much information as possible available in the literature, seeking to help researchers gain a comprehensive view of the mechanical drilling of fiber metal laminates.

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