Technical Library | 2019-03-15 16:26:50.0
While there have been quite dramatic and evident improvements in almost every facet of manufacturing over the last several decades owing to the advent and mass adoption of computer automation and networking, there is one aspect of production that remains stubbornly unaffected. Massive databases track everything from orders, to inventory, to personnel. CAD systems allow for interactive and dynamic 3D rendering and testing, digital troubleshooting, and simulation and analysis prior to mass production. Yet, with all of this computational power and all of this networking capability, one element of production has remained thoroughly and firmly planted in the past. Nearly all manufacturing or assembly procedures are created, deployed, and stored using methodologies derived from a set of assumptions that ceased to be relevant fifty years ago. This set of assumptions, referred to below as the “Paper Paradigm” has been, and continues as the dominant paradigm for manufacturing procedures to this day. It is time for a new paradigm, one that accounts for the vastly different technological landscape of this era, one that provides a simple, efficient interface, deep traceability, and dynamic response to rapidly changing economic forces.This paper seeks to present an alternative. Instead of enhancing and improving on systems that became irrelevant with the invention of a database, instead of propping up an outdated, outmoded and inefficient system with incremental improvements; rewrite the paradigm. Change the underlying assertions to more accurately reflect our current technological capability. Instead of relying on evolutionary improvements, it is time for a revolution in manufacturing instructions.
Technical Library | 2021-06-07 19:06:32.0
The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic equipment is the printed circuit board (PCB) and almost every electronic equipment has a PCB inside it. While waste PCB (WPCB) recycling may result in the recovery of potentially precious materials and the reuse of some components, it is a challenging task because its composition diversity requires a cautious pre-processing stage to achieve optimal recycling outcomes. Our research focused on proposing a method to evaluate the economic feasibility of recycling integrated circuits (ICs) from WPCB. The proposed method can help decide whether to dismantle a separate WPCB before the physical or mechanical recycling process and consists of estimating the IC area from a WPCB, calculating the IC's weight using surface density, and estimating how much metal can be recovered by recycling those ICs. To estimate the IC area in a WPCB, we used a state-of-the-art object detection deep learning model (YOLO) and the PCB DSLR image dataset to detect the WPCB's ICs. Regarding IC detection, the best result was obtained with the partitioned analysis of each image through a sliding window, thus creating new images of smaller dimensions, reaching 86.77% mAP. As a final result, we estimate that the Deep PCB Dataset has a total of 1079.18 g of ICs, from which it would be possible to recover at least 909.94 g of metals and silicon elements from all WPCBs' ICs. Since there is a high variability in the compositions of WPCBs, it is possible to calculate the gross income for each WPCB and use it as a decision criterion for the type of pre-processing.
