Disassembly 4.0: A Review on Using Robotics in Disassembly Tasks as a Way of Automation

Poschmann, Hendrik; Brüggemann, Holger; Goldmann, Daniel

doi:10.1002/cite.201900107

Cited by 67 publications

(33 citation statements)

References 162 publications

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“…This generation task is always difficult due to complicated constraints between components, especially for complex products. For the disassembly sequence generation problem, computer vision techniques have been mainly applied to identify possible assembly structures of the product according to images of the product, to support the follow-up disassembly sequence generation tasks [161]. Another application of computer vision technologies in product disassembly is being used as the visual recognition and navigation system of disassembly robots to support the motion control of disassembly robots.…”

Section: G Disassemblymentioning

confidence: 99%

Computer Vision Techniques in Manufacturing

Zhou¹,

Zhang²,

Konz³

2022

Preprint

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Computer vision techniques have played an important role in promoting the informatization, digitization and intelligence of industrial manufacturing systems. Considering the rapid development of computer vision techniques, we present a comprehensive review of the state-of-the-art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation and 3D modeling. A system framework of computer vision in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, computer vision algorithms, a decision-making module, and an actuator. Applications of computer vision to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data pre-processing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for non-annotated data processing, developing effective data pre-processing mechanisms, customizing computer vision models, and opportunities aroused by 5G.

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Section: G Disassemblymentioning

confidence: 99%

Computer Vision Techniques in Manufacturing

Zhou¹,

Zhang²,

Konz³

2022

Preprint

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“…Chen et al describe a multi-head tool for robot-based disassembly with a drill, grinder, and screwdriver mounted to a standard robot arm and combined with an external flip table [15]. Different approaches to disassembly are presented by Poschmann et al [16]. The authors state that "disassembly has not been in the focus of mainstream robotics research so far" and some of the works in this survey also prefer a destructive approach.…”

Section: Related Workmentioning

confidence: 99%

The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks

Klas

Hundhausen

Gao

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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We introduce a multi-functional robotic gripper equipped with a set of actions required for disassembly of electromechanical devices. The gripper consists of a robot arm with 5 degrees of freedom (DoF) for manipulation and a jaw gripper with a 1-DoF rotation joint and a 1-DoF closing joint. The system enables manipulation in 7 DoF and offers the ability to reposition objects in hand and to perform tasks that usually require bimanual systems. The sensor system of the gripper includes relative and absolute joint encoders, force and pressure sensors to provide feedback about interaction forces, a toolmounted camera for screw detection and precise placement of the tool tip using image-based visual servoing. We present a data-driven method for estimating joint torques based on the output voltage and motor speed. Further, we provide methods for teaching disassembly actions based on human demonstration, their representation as movement primitives and execution based on sensory feedback. We provide quantitative results regarding positioning and torque estimation accuracy, disassembly success rate and qualitative results regarding the successful disassembly of hard disc drives.

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“…televisions (Scholz-Reiter et al 1999), mobile phones (Kopacek and Kopacek 2003), printed circuit boards (Kopacek and Kopacek 2006) or car wheels (Büker et al 2001). So far, however, full automation of the disassembly process has not been established in industry (Poschmann et al 2020). There are only few applications for automated disassembly that are operated on an industrial scale.…”

Section: Introductionmentioning

confidence: 99%

“…However, thanks to advances in the field of artificial intelligence (AI) and robotics, flexible and autonomous production systems are now within reach (Poschmann et al 2020;World Economic Forum and Accenture Strategy 2019;Wurster et al 2021). Furthermore, human-robot-collaboration can help to combine the strengths and compensate the weaknesses of humans and robots in order to improve disassembly productivity.…”

Section: Introductionmentioning

confidence: 99%

“…These developments will further increase the effectiveness and efficiency of robotically performed disassembly tasks and likewise allow robots to adapt to changing requirements when dealing with uncertainty (Vongbunyong et al 2013;Vongbunyong et al 2017;Bdiwi et al 2016). Autonomous disassembly robots can play a vital role in maintaining the required flexibility as a part of an entire disassembly system (Poschmann et al 2020). Poschmann et al (2020) argue that automated disassembly will be part of the industrial state-of-the-art within the next ten years.…”

Section: Introductionmentioning

confidence: 99%

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Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

et al. 2022

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Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases.

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Disassembly 4.0: A Review on Using Robotics in Disassembly Tasks as a Way of Automation

Cited by 67 publications

References 162 publications

Computer Vision Techniques in Manufacturing

Computer Vision Techniques in Manufacturing

The KIT Gripper: A Multi-Functional Gripper for Disassembly Tasks

Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

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