Automatic disassembly and recovery device for mobile phone circuit board CPU based on machine vision

He, Cheng; Jin, Zhongyan; Gu, Runwei; Qu, Huaying

doi:10.1088/1742-6596/1684/1/012137

Cited by 8 publications

(4 citation statements)

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“…For the automatic disassembly of target components, Li et al 8 designed a prototype of an automatic disassembly system for EOL lithium-ion pouch cells, which could realize the separation of various materials of lithium-ion pouch cells, efficient recovery, and secondary environmental pollution reduction. He et al 9 designed an old mobile phone circuit board CPU disassembly recovery device based on machine vision. However, the above disassembly equipment still has significant limitations for achieving complex disassembly tasks.…”

Section: Research On Automatic Disassemblymentioning

confidence: 99%

Multi-domain Mapping design Method of Automatic Disassembly Equipment for Small Electromechanical Products

Yang,

Zhang

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The design of automatic disassembly equipment for small end-of-life (EOL) electromechanical products is difficult and time-consuming. To deal with these problems, a novel automatic disassembly equipment design method for small EOL electromechanical products based on axiomatic design and modular design is presented. To realize the mapping from disassembly knowledge to the functional requirements of automatic disassembly equipment, a transfer mapping model from the structure to disassembly methods of small EOL electromechanical products is constructed based on polychromatic sets, which are regarded as the key information of the customer domain. Furthermore, a multi-domain mapping model of the automatic disassembly equipment is constructed through axiomatic design, and the mapping from the customer domain to the functional domain and then to the structure domain in the design process of automatic disassembly equipment is gradually realized. Based on the information in the structure domain, the related modules of the automatic disassembly equipment are divided and designed. Finally, taking a power strip and a set-top box as the experimental objects, the proposed method is applied to the design of automatic disassembly equipment. The results prove that the proposed method improves the design efficiency of automatic disassembly equipment and has strong universality.

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Section: Research On Automatic Disassemblymentioning

confidence: 99%

Multi-domain Mapping design Method of Automatic Disassembly Equipment for Small Electromechanical Products

Yang,

Zhang

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Cheng et al proposed a device based on machine vision to automatically disassemble as well as recycle the CPU on the mobile phone circuit board. The device could achieve stable and reliable CPU disassembly and recovery [18]. Tang et al proposed a multi-scale feature Deep Forest recognition model for smart recycling equipment, which can effectively improve the prediction accuracy and reduce the time cost [19].…”

Section: Relevant Research On Recycling End-of-life Mobile Phonesmentioning

confidence: 99%

Detection Method of End-of-Life Mobile Phone Components Based on Image Processing

Zhang²,

Feng³

2022

Sustainability

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The number of end-of-life mobile phones is increasing every year, which includes parts that have high reuse values and various dangerous and toxic compounds. An intellectualized and automatic upgrade of the disassembly process of the end-of-life mobile phones would enhance the recycling value as well as efficiency. It would reduce the pollution in the environment. The detection of end-of-life mobile phone parts plays a critical role in automatic disassembly and recycling. This study offers an image processing-based approach for identifying important parts of mobile phones that are nearing the end of their useful lives. An image enhancement approach has been utilized for generating disassembly datasets of end-of-life mobile phones from several brands and models, and different retirement states. The YOLOv5m detection model is applied to train as well as validate the detection model on the customized datasets. According to the results, the proposed approach allows the intelligent detection of battery, camera, mainboard and screw. In the validation set, the Precision, Recall and mAP@.5 are 99.4%, 98.4% and 99.3%, respectively. Additionally, several path planning algorithms are utilized for the disassembly plan of screws which indicates that the genetic algorithm’s use increases the efficiency of disassembly.

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“…Moreover, the hallmark of smart disassembly lies in its targeted approach. Particularly, the retrieval of valuable ECs appears more suitable, considering the high costs associated with smart assembly systems, which are designed to handle one circuit board at a time[90,91]. The strengths of smart disassembly lie in its automation and the precise extraction of specific ECs.…”

mentioning

confidence: 99%

Discarded e-waste/printed circuit boards: a review of their recent methods of disassembly, sorting and environmental implications

Oke,

Potgieter

2024

J Mater Cycles Waste Manag

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The improper disposal of discarded electronic and electrical equipment raises environmental and health concerns, spanning air pollution to water and soil contamination, underscoring the imperative for responsible management practises. This review explores the complex composition of discarded printed circuit boards (DPCBs), crucial components in electronic devices. Comprising substrates, electronic elements and solder, DPCBs showcase a heterogeneous structure with metal (30.0–50.0%) and non-metal (50.0–70.0%) fractions. Notably abundant in precious metals such as Au, Ag, and Pd, DPCBs offer a compelling avenue for recycling initiatives. The inclusion of heavy metals and flame retardants adds complexity, necessitating environmentally sound disposal methods. Ongoing research on smart disassembly, utilising 3D image recognition technology, underscores the importance of accurate identification and positioning of electronic components (ECs). The targeted approach of smart disassembly, centred on valuable components, highlights its significance, albeit with challenges in equipment costs and capacity limitations. In mechanical disassembly, techniques such as grinding and heat application are employed to extract ECs, with innovations addressing gas emissions and damage induced by overheating. Chemical disassembly methods, encompassing epoxy resin delamination and tin removal, present promising recovery options, whilst the integration of chemical and electrochemical processes shows potential. Efficient sorting, encompassing both manual and automated methods, is imperative post-disassembly, with smart sorting technologies augmenting accuracy in the identification and categorisation of ECs. In addition, explorations into NH3/NH4+ solutions for selective metal recovery underscore challenges and stress the necessity for meticulous process optimisation in environmentally sustainable PCB recycling. Challenges and future perspectives have also been expounded.

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Automatic disassembly and recovery device for mobile phone circuit board CPU based on machine vision

Cited by 8 publications

References 0 publications

Multi-domain Mapping design Method of Automatic Disassembly Equipment for Small Electromechanical Products

Multi-domain Mapping design Method of Automatic Disassembly Equipment for Small Electromechanical Products

Detection Method of End-of-Life Mobile Phone Components Based on Image Processing

Discarded e-waste/printed circuit boards: a review of their recent methods of disassembly, sorting and environmental implications

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