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%.
Highlights
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
Hou [18] proposed fuzzy logic-driven variable time scale prediction-based reinforcement learning to improve the efficiency of a reinforcement learning algorithm in the PIH assembly process, which successfully decreases the assembly time by about 44% compared to the Deep-Q Learning approach [19]
The following section will explain each fundamental theory related to our research, including the various deep learning techniques utilized on our dataset for the prediction to assemble the boardto-board connector
Summary
All devices utilize a PCB board to function and incorporate various electronic devices. 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. 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. 22 ooff 2244 determine whether the process is a successful insertion In addition to these challenges, there are various other constraints such as (1) the position of the male and female boardto-wdbehotaeertrhdmecrinotnehnewephctreootrhcsesrosnthistehaepsrduoecvecisecses,ifsu(2al)isonubscesctreatsicoslfenus.lsIiunrsareodrutdinoitdnioi.nnIgntotahdtehdceitosienoncehtcaotlolterh,ne(gs3e)sct,htaehlelserieznega,erse, anvdtha(er4iro)eutahsreeontvhuaemrriobcueorsnosotftrhpaeinr tscs,oasnsusctrhaisiansitns(c1sr)uetachsheeaspstoh(s1eit)ictoohnmeopfloetshxiiettiyomnoafoletfhatehnemdmafetaimnlegaalpenrdboocfaesmrds.a-tleo-bbooaarrddcton-bnoeacrtdorcsoonnntehcetodrsevoincet,h(e2)doebvsitcaec,le(2s)soubrrsotaucnledsinsgurtrhoeucnodninnegctohre, (c3o)ntnhecstiozre,,(a3n) dth(e4)stihze, naunmd b(4e)rtohfepniunsm, baserthoifspinincsr,easetshitsheinccormeapselesxtihtye ocof mthpelmexaityinogfpthroecmesast.ing process.
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