| dc.description.abstract | When mass-producing products in the factory, the production line operators are assigned
to carry out product assembly based on product specifications and product number. However,
when the production line employees assemble products, human operation errors are prone to
cause product rupture or product miss screw , which in turn affects the overall product quality
and manufacturing cost.
Therefore, This paper proposes that the operator wears a finger sleeve with a nine-axis
sensor attached and holds an automatic screwdriver to collect motion sensing data, and then
uses the Kalman filter to filter the sensing data, and performs motion cutting with a zerocrossing
rate. Then use Mahony filter to calculate the error model to correct the sensor data and
calculate the quaternion, use the quaternion as a feature, and then use a one-dimensional
Convolutional neural network to classify (1) upper right screw , (2) lower right screw ,(3) right
diagonal screw,(4) Left diagonal screw,(5) down lock screw ,(6) Raise hand and lay down , total
of six basic assembly actions for operators, The sequence of six assembly actions is used to
judge whether the assembled product is correct.
The experimental results found that the use of Mahony filter to calibrate the sensor and
then calculate the quaternion as the feature for one-dimensional convolutional neural
network 1DCNN can increase the recognition rate by 72%, and the long- and short-term
memory model LSTM can increase the recognition rate by 30%. CNN-LSTM can also increase
the recognition rate by 70%, and the corrected data has an average recognition rate of 94%
in 1DCNN, which is 39% higher than the LSTM model and 3% higher than the CNN-LSTM.
Using this method can prevent the current operator from locking screws and reduce the cost
of operator training, and improve the lack of intelligent management of production line
employees. | en_US |