Name
Abstract | ||
|---|---|---|
Full-scale automated manufacturing is reserved for selected industries and high quantity production of single parts. The majority of consumer manufacturing and industrial component manufacturing remains a manual or, at best, semi-automated process with a large human element. Though advances have been made in computer aided quality control for defective part classification and sorting, these techniques do not address the inefficiency and cost of discarding faulty products at the end of the manufacturing cycle. We present a Deep Learning model for detecting and correcting errors in a sample manufacturing process early in a multi-node assembly chain. Instead of simply classifying individual items into quality groups, our model aims to track the manufacturing process in real-time and if an error is detected, the model makes changes to subsequent assembly steps to recover from the error and save the part. This model and system can be applied to any manufacturing cycle with a human assembly feedback control and allows for product manufacturing to be dynamically altered throughout the process. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.23919/ACC45564.2020.9147715 | 2020 AMERICAN CONTROL CONFERENCE (ACC) |
DocType | ISSN | Citations |
Conference | 0743-1619 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Sundstrom | 1 | 0 | 0.34 |
| Eun-Sol Kim | 2 | 0 | 0.34 |
| Damas W. Limoge | 3 | 0 | 0.34 |
| Vadim Pinskiy | 4 | 0 | 0.34 |
| Matthew C. Putman | 5 | 0 | 0.34 |