Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Sinergi
Asif Khan
School of Computer Science and Technology, Beijing Institute of Technology
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
SELF-LEARNING OF DELTA ROBOT USING INVERSE KINEMATICS AND ARTIFICIAL NEURAL NETWORKS Zendi Iklima; Muhammad Imam Muthahhar; Asif Khan; Arifiansyah Zody
SINERGI Vol 25, No 3 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.3.001

Abstract

As known as Parallel-Link Robot, Delta Robot is a kind of Manipulator Robot that consists of three arms mounted in parallel. Delta Robot has a central joint constructed as an end-effector represented as a gripper. An Analysis of Inverse Kinematic (IK) used to convert the end-effector trajectory (X, Y) into rotations of stepper motors (ZA, ZB and ZC). The proposed method used Artificial Neural Networks (ANNs) to simplify the process of IK solver. The IK solver generated the datasets contain motion data of the Delta robot. There are 11 KB Datasets consist of 200 motion data used to be trained. The proposed method was trained in 58.78 seconds in 5000 iterations. Using a learning rate (α) 0.05 and produced the average accuracy was 97.48%, and the average loss was 0.43%. The proposed method was also tested to transfer motion data over Socket.IO with 115.58B in 6.68ms.
Defect classification of radius shaping in the tire curing process using Fine-Tuned Deep Neural Network Zendi Iklima; Bugi Nur Rohman; Rahmat Muwardi; Asif Khan; Zody Arifiansyah
SINERGI Vol 26, No 3 (2022)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2022.3.009

Abstract

The curing process or vulcanization process is the final stage of the tire manufacturing process, where the properties of the tire compound change from rubber-plastic material to become elastic by forming cross-links in its molecular structure. The green tire is formed in the curing process, which is placed on the bottom mould. The inside of the green tire surrounds the bladder. The top mould will close to carry out the next curing process. In closing the mould, there is a shaping process of forming a green tire placed on the bladder and given a proportional pressure. Improper or abnormal radius shaping results cause seventy percent of product defects. This paper proposed abnormal detection of radius shaping in the curing process using Fine-tuned Deep Neural Network (DNN). Several DNN models have been examined to analyze an optimized DNN model for abnormal detection of radius shaping in the curing process. The fine-tuned DNN architecture has been exported for the curing system. The DNN was trained with a training accuracy of 97.88%, a validation accuracy of 95%, a testing accuracy of 100%, and a loss of 4.93%.
Co-Authors Arifiansyah Zody Bugi Nur Rohman Muhammad Imam Muthahhar Rachmat Muwardi Zendi Iklima Zody Arifiansyah