Online Semisupervised Learning Approach for Quality Monitoring of Complex Manufacturing Process

<table class="table-group" id="tab7"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Model</td><td class="align_center">Accuracy</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">ParsNet++</td><td class="align_center"><span style="width: 82.1544ptpx;"><svg height="8.69875pt" id="M247" style="vertical-align:-0.3499298pt" version="1.1" viewbox="-0.0498162 -8.34882 82.1544 8.69875" width="82.1544pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 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<div>Classification performance on injection molding dataset of infinity delay next batch prediction scenario.</div>

Complexity

tab7

Table 7

Table 7: Online Semisupervised Learning Approach for Quality Monitoring of Complex Manufacturing Process 