scholarly journals A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

2020 ◽  
Author(s):  
Filmon Yacob ◽  
Daniel Semere
Keyword(s):  
Neural Network ◽  
Source Identification ◽  
Point Cloud ◽  
Machining Process ◽  
Skin Model ◽  
Machining Processes ◽  
Cloud Data ◽  
Actual Surface ◽  
Variation Propagation ◽  
Analytical Approaches

Abstract Variation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source identification techniques using Skin Model Shapes is unclear. This paper proposes a multilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.

scholarly journals Learning Collision Situation to Convolutional Neural Network Using Collision Grid Map Based on Probability Scheme

2020 ◽  
Vol 10 (2) ◽  
pp. 617
Author(s):  
Jo ◽  
Moon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Probability Method ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Location Data ◽  
Grid Map ◽  
Permutation Problem

In this paper, a Collision Grid Map (CGM) is proposed by using 3d point cloud data to predict the collision between the cattle and the end effector of the manipulator in the barn environment. The Generated Collision Grid Map using x-y plane and depth z data in 3D point cloud data is applied to a Convolutional Neural Network to predict a collision situation. There is an invariant of the permutation problem, which is not efficiently learned in occurring matter of different orders when 3d point cloud data is applied to Convolutional Neural Network. The Collision Grid Map is generated by point cloud data based on the probability method. The Collision Grid Map scheme is composed of a 2-channel. The first channel is constructed by location data in the x-y plane. The second channel is composed of depth data in the z-direction. 3D point cloud is measured in a barn environment and created a Collision Grid Map. Then the generated Collision Grid Map is applied to the Convolutional Neural Network to predict the collision with cattle. The experimental results show that the proposed scheme is reliable and robust in a barn environment.

scholarly journals Asphalt Pavement Friction Coefficient Prediction Method Based on Genetic-Algorithm-Improved Neural Network(GAI-NN) Model

2021 ◽  
Author(s):  
Zhaoyun Sun ◽  
Xueli Hao ◽  
Wei Li ◽  
Ju Huyan ◽  
Hongchao Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Friction Coefficient ◽  
Point Cloud ◽  
Spline Interpolation ◽  
Asphalt Pavement ◽  
3D Point Cloud ◽  
Cloud Data ◽  
Pavement Surface ◽  
Pavement Friction

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a Genetic-Algorithm-Improved Neural Network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover missing data and perform denoising. In particular, these data were filled using cubic-spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed via modification of the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.

scholarly journals Variation propagation modeling in multistage machining processes considering form errors and N-2-1 fixture layouts

2021 ◽  
Author(s):  
Filmon Yacob ◽  
Daniel Semere ◽  
Nabil Anwer
Keyword(s):  
Machining Process ◽  
Quality Prediction ◽  
Machining Processes ◽  
Variation Reduction ◽  
Propagation Modeling ◽  
Form Errors ◽  
Part Quality ◽  
Fixture Layout ◽  
Variation Propagation ◽  
Cad Cam

AbstractVariation propagation modeling of multistage machining processes enables variation reduction by making an accurate prediction on the quality of a part. Part quality prediction through variation propagation models, such as stream of variation and Jacobian-Torsor models, often focus on a 3-2-1 fixture layout and do not consider form errors. This paper derives a mathematical model based on dual quaternion for part quality prediction given parts with form errors and fixtures with N-2-1 (N>3) layout. The method uses techniques of Skin Model Shapes and dual quaternions for a virtual assembling of a part on a fixture, as well as conducting machining and measurement. To validate the method, a part with form errors produced in a two-stationed machining process with a 12-2-1 fixture layout was considered. The prediction made following the proposed method was within 0.4% of the prediction made using a CAD/CAM simulation when form errors were not considered. These results validate the method when form errors are neglected and partially validated when considered.

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