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Industry 4.0 and prospects of circular economy: a survey of robotic assembly and disassembly

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Abstract

Despite their contributions to the financial efficiency and environmental sustainability of industrial processes, robotic assembly and disassembly have been understudied in the existing literature. This is in contradiction to their importance in realizing the Fourth Industrial Revolution. More specifically, although most of the literature has extensively discussed how to optimally assemble or disassemble given products, the role of other factors has been overlooked. For example, and among other factors, the choices of the robots involved in implementing the sequence plans, which should ideally be taken into account throughout the whole chain consisting of design, assembly, disassembly, and reassembly, may greatly affect the underlying implications with a considerable impact on the viability and effectiveness of the measures aimed at substantiating, realizing, and strengthening the backbones of a circular economy. Isolating the foregoing operations from the rest of the components of the relevant ecosystems may lead to erroneous inferences toward both the necessity and efficiency of the underlying procedures. In this paper, we try to alleviate these shortcomings by comprehensively investigating the state of the art in robotic assembly and disassembly. We consider and review various aspects of manufacturing and remanufacturing frameworks while particularly focusing on their desirability for supporting a circular economy.

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Notes

  1. Throughout this paper, RAD will be used to refer to Robotic Assembly and Disassembly, in general. Whenever differences are important, the more specific terms Robotic Assembly (RA) or Robotic Disassembly (RD) will be used accordingly.

  2. A family of methods to build a scale model of a part or product in a fast manner based on computer-aided design and three dimensional (3D) printing, or additive manufacturing [116–118].

  3. Arm-like mechanisms consisting of series of links and joints referred to as cross-slides, which are utilized for grasping and manipulation without human intervention [119, 120].

  4. Digital characterization of a place in terms of properties and functionalities, throughout the whole life cycle, i.e., from formation to demolition [121, 122].

  5. The constituents and the level of robustness of different elements of the Building Information Modelling at various stages [123].

  6. Non-deterministic polynomial time.

  7. A product at the end of its useful life.

  8. A special type of neural networks which have convolutions instead of matrix multiplications within at least one of their layers [140, 141].

  9. A machine learning strategy taking simultaneous advantage of reinforcement learning and deep learning, where the former concerns the ability of a computational agent to learn how to make decisions through trial and error, i.e., to figure out what to do in order to optimize an objective function, and the latter helps it to do so based on large unstructured input data, obviating the necessity of manual manipulation of the state space [69, 142].

  10. The number of independent parameters based on which the configuration or state of a mechanical system may be defined [144, 145].

  11. Calculation of the required values of the joint parameters for the end effector (EE) to appear at a certain pose with respectto the base [159, 160].

  12. The maximum (minimum) of a problem is the inverse function to the minimum (maximum) of the inverse problem [161].

  13. The device attached to the end of a robotic arm, which is supposed to perform the main task the robot is aimed at, and interact with the work environment [162, 163].

  14. Analyzing problems using the finite element method, which solves differential equations numerically in two or three state or boundary variables [165, 166].

  15. Programming computers to understand concepts, as well as the nuances associated with the context, from large volumes of documents representing natural human language, as well as organizing and categorizing them [98167].

  16. A system in which a mechanism is monitored or controlled using computer software [168].

  17. Fast, high-precision placement of a wide range of electronic surface mount devices (SMDs), including integrated circuits, resistors and capacitors, as well as through-hole components, onto Printed Circuit Boards (PCBs) utilized in telecommunication, medical, military, automotive, and industrial devices, consumer electronics and computers [169].

  18. A family of discrete dynamic systems utilized for mathematical modeling of distributed systems, where places and transitions are shown as white circles and rectangles, respectively, within a bipartite graph [170].

  19. Enabling computers to obtain useful data from, and about, the environment, i.e., “things,” without requiring human aid, thereby making it possible to acquire information more accurately, rigorously, and extensively [189].

  20. Further involving other elements, such as people, data, and processes, resulting in a more comprehensive concept than the Internet of Things [190].

Abbreviations

3D:

Three-dimensional

AI:

Artificial intelligence

AS:

Assembly sequence

ASP:

Assembly sequence planning

CE:

Circular economy

DLBP:

Disassembly line balancing problem

DS:

Disassembly sequence

DSP:

Disassembly sequence planning

EE:

End-effector

EoL:

End-of-life

HRC:

Human–robot collaboration

OASP:

Optimal assembly sequence planning

ODSP:

Optimal disassembly sequence planning

PCB:

Printed circuit board

RA:

Robotic assembly

RAD:

Robotic assembly and disassembly

RD:

Robotic disassembly

SMD:

Surface mount device

SP:

Sequence planning

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Funding

This work was supported by the European Social Fund via IT Academy program and the Estonian Research Council [grant numbers COVSG24 and PSG605].

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Authors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan

    Morteza Daneshmand & Fatemeh Noroozi

  2. Tawny, Schellingstraße 45, München, 80799, Germany

    Ciprian Corneanu

  3. ENAP, Universite du Quebec, 4570 Henri Julien, Montreal, QC, H2T2C8, Canada

    Fereshteh Mafakheri

  4. Computer Science Department, University of Verona, Ca Vignal 2, room 1-56, VR, 37134, Verona, Italy

    Paolo Fiorini

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  1. Morteza Daneshmand
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Daneshmand, M., Noroozi, F., Corneanu, C. et al. Industry 4.0 and prospects of circular economy: a survey of robotic assembly and disassembly. Int J Adv Manuf Technol 124, 2973–3000 (2023). https://doi.org/10.1007/s00170-021-08389-1

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