Citation: Lin, H.-I.; Wibowo, F.S.;
Singh, A.K. Study on Data-Driven
Approaches for the Automated
Assembly of Board-to-Board
Connectors. Appl. Sci. 2022, 12, 1216.
https://doi.org/10.3390/
app12031216
Academic Editors: Yangquan
Chen, Subhas Mukhopadhyay,
Nunzio Cennamo, M. Jamal Deen,
Junseop Lee and Simone Morais
Received: 8 November 2021
Accepted: 17 January 2022
Published: 24 January 2022
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Article
Study on Data-Driven Approaches for the Automated Assembly
of Board-to-Board Connectors
Hsien-I Lin * , Fauzy Satrio Wibowo and Ashutosh Kumar Singh
College of Mechanical and Electrical Engineering, Graduate Institute of Automation Technology,
National Taipei University of Technology, Taipei 10608, Taiwan; fauzy.satriowibowo@gmail.com (F.S.W.);
ash.singher@gmail.com (A.K.S.)
* Correspondence: sofin@ntut.edu.tw
Abstract:
The mating of the board-to-board (BtB) connector is rugged because of its design complexity,
small pitch (0.4 mm), and susceptibility to damage. Currently, the assembly task of BtB connectors is
performed manually because of these factors. A high chance of damage to the connectors can also
occur during the mating process. Thus, it is essential to automate the assembly process to ensure its
safety and reliability during the mating process. Commonly, the mating procedure adopts a model-
based approach, including error recovery methods, owing to less design complexity and fewer pins
with a high pitch. However, we propose a data-driven approach prediction for the mating process of
the fine pitch 0.4 mm board-to-board connector utilizing a manipulator arm and force sensor. The
data-driven approach uses force data for time series encoding methods such as recurrence plot (RP),
Gramian matrix, k-nearest neighbor dynamic time warping (kNN-DTW), Markov transition field
(MTF), and long short-term memory (LSTM) to compare each of the model prediction performances.
The proposed method combines the RP model with the convolutional neural network (RP-CNN)
to predict the force data. In the experiment, the proposed RP-CNN model used two final layers,
SoftMax and L2-SVM, to compare with the other prediction models mentioned above. The output
of the data-driven prediction is the coordinate alignment of the female board-to-board connector
with the male board-to-board connector based on the value of force. Based on the experiment, the
encoding approach, especially RP-CNN (L2-SVM), outperformed all prediction models as mentioned
above with an accuracy of 86%.
Keywords: connector mating; board-to-board connector; deep learning
1. Introduction
All devices utilize a PCB board to function and incorporate various electronic devices.
One of these components is a board-to-board connector used to connect two wires or
two circuits robustly. Figure 1 illustrates a surface-mounted board-to-board connector
with 20
×
2 pins. Electric connectors use in various applications, such as mobile devices,
tablets, and transportation [
1
]. Furthermore, experts assess the importance of automation
in the assembly line in the industrial sector according to four main points: (1) increasing
production rate, (2) enhancing production speed, (3) optimizing production, (4) assisting
human tasks, and (5) improving cost-effectiveness.
Due to design complexity, fine pitch 0.4 mm board-to-board connectors are assembled
manually in the production units. The industry’s most common challenges faced in the
assembly line are aligning male and female pairs and fast-mating the connectors because of
the complex design. In addition, performing the mating process without damage is one of
the biggest challenges because humans cannot perceive the correct amount of force applied.
Sometimes, this leads to a fault or damage to the pins in the connectors. In addition, the
board-to-board connector assembly relies on a faintly audible clicking sound made by the
connector during successful mating. However, it is not a reliable method to determine
Appl. Sci. 2022, 12, 1216. https://doi.org/10.3390/app12031216 https://www.mdpi.com/journal/applsci
