Cyber-Physical Systems for Micro-/Nano-assembly Operations: a Survey

Abstract Purpose of Review Latest requirements of the global market force manufacturing systems to a change for a new production paradigm (Industry 4.0). Cyber-Physical Systems (CPS) appear as a solution to be deployed in different manufacturing fields, especially those with high added value and technological complexity, high product variants, and short time to market. In this sense, this paper aims at reviewing the introduction level of CPS technologies in micro/nano-manufacturing and how these technologies could cope with these challenging manufacturing requirements. Recent Findings The introduction of CPS is still in its infancy on many industrial applications, but it actually demonstrates its potential to support future manufacturing paradigm. However, only few research works in micro/nano-manufacturing considered CPS frameworks, since the concept barely appeared a decade ago. Summary Some contributions have revealed the potential of CPS technologies to improve manufacturing performance which may be scaled to the micro/nano-manufacturing. IoT-based frameworks with VR/AR technologies allow distributed and collaborative systems, or agent-based architectures with advance algorithm implementations that improve the flexibility and performance of micro-/nano-assembly operations. Future research of CPS in micro-/nano-assembly operations should be followed by more studies of its technical deployment showing its implications under other perspectives, i.e. sustainable, economic, and social point of views, to take full advance of all its features

Cyber physical approach and framework for micro devices assembly

The emergence of Cyber Physical Systems (CPS) and Internet-of-Things (IoT) based principles and technologies holds the potential to facilitate global collaboration in various fields of engineering. Micro Devices Assembly (MDA) is an emerging domain involving the assembly of micron sized objects and devices. In this dissertation, the focus of the research is the design of a Cyber Physical approach for the assembly of micro devices. A collaborative framework comprising of cyber and physical components linked using the Internet has been developed to accomplish a targeted set of MDA life cycle activities which include assembly planning, path planning, Virtual Reality (VR) based assembly analysis, command generation and physical assembly. Genetic algorithm and modified insertion algorithm based methods have been proposed to support assembly planning activities. Advanced VR based environments have been designed to support assembly analysis where plans can be proposed, compared and validated. The potential of next generation Global Environment for Network Innovation (GENI) networking technologies has also been explored to support distributed collaborations involving VR-based environments. The feasibility of the cyber physical approach has been demonstrated by implementing the cyber physical components which collaborate to assemble micro designs. The case studies conducted underscore the ability of the developed Cyber Physical approach and framework to support distributed collaborative activities for MDA process contexts

Design of a Cyber Physical Framework for the Assembly of Micro Devices

Micro Devices Assembly (MDA) is an emerging domain with a significant economic potential. Existing methods for assembly of micro devices are tedious and costly. For this reason, it is important to develop a collaborative framework for the field of MDA. In this thesis, a Cyber Physical Framework (CPF) is proposed to support a collaborative approach using software and physical resources for rapid assembly of micro devices in a distributed environment. CPF adopts the cloud computing principles to improve the Quality of Experience (QoE) of users accessing the simulation videos and physical assembly videos. The cloud computing principle enables the distributed engineers/users to access the cyber physical resources from various locations.Computer Science Departmen

Microassembly for complex and solid 3D MEMS by 3D Vision-based control.

International audienceThis paper describes the vision-based methods developed for assembly of complex and solid 3D MEMS (micro electromechanical systems) structures. The microassembly process is based on sequential robotic operations such as planar positioning, gripping, orientation in space and insertion tasks. Each of these microassembly tasks is performed using a posebased visual control. To be able to control the microassembly process, a 3D model-based tracker is used. This tracker able to directly provides the 3D micro-object pose at real-time and from only a single view of the scene. The methods proposed in this paper are validated by an automatic assembly of fives silicon microparts of 400 µm 400 µm 100 µm on 3- levels. The insertion tolerance (mechanical play) is estimated to 3 µm. The weakness of this insertion tolerance allows to obtain solid and complex micro electromechanical structures without any external joining (glue, wending). Promising positioning and orientation accuracies are obtained who can reach 0.3 µm in position and 0.2° in orientation

Augmented reality in support of intelligent manufacturing – A systematic literature review

Industry increasingly moves towards digitally enabled ‘smart factories’ that utilise the internet of things (IoT) to realise intelligent manufacturing concepts like predictive maintenance or extensive machine to machine communication. A core technology to facilitate human integration in such a system is augmented reality (AR), which provides people with an interface to interact with the digital world of a smart factory. While AR is not ready yet for industrial deployment in some areas, it is already used in others. To provide an overview of research activities concerning AR in certain shop floor operations, a total of 96 relevant papers from 2011 to 2018 are reviewed. This paper presents the state of the art, the current challenges, and future directions of manufacturing related AR research through a systematic literature review and a citation network analysis. The results of this review indicate that the context of research concerning AR gets increasingly broader, especially by addressing challenges when implementing AR solutions.No funding was received

Robotic learning of force-based industrial manipulation tasks

Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot and human workspace which imposes an additional financial burden. Secondly, due to the significant re-programming cost in case of changing products, especially in Small and Medium-sized Enterprises (SMEs). Therefore, there is a significant need to reduce the programming efforts required to enable robots to perform various tasks while sharing the same space with a human operator. Hence, the robot must be equipped with a cognitive and perceptual capabilities that facilitate human-robot interaction. Humans use their various senses to perform tasks such as vision, smell and taste. One sensethat plays a significant role in human activity is ’touch’ or ’force’. For example, holding a cup of tea, or making fine adjustments while inserting a key requires haptic information to achieve the task successfully. In all these examples, force and torque data are crucial for the successful completion of the activity. Also, this information implicitly conveys data about contact force, object stiffness, and many others. Hence, a deep understanding of the execution of such events can bridge the gap between humans and robots. This thesis is being directed to equip an industrial robot with the ability to deal with force perceptions and then learn force-based tasks using Learning from Demonstration (LfD).To learn force-based tasks using LfD, it is essential to extract task-relevant features from the force information. Then, knowledge must be extracted and encoded form the task-relevant features. Hence, the captured skills can be reproduced in a new scenario. In this thesis, these elements of LfD were achieved using different approaches based on the demonstrated task. In this thesis, four robotics problems were addressed using LfD framework. The first challenge was to filter out robots’ internal forces (irrelevant signals) using data-driven approach. The second robotics challenge was the recognition of the Contact State (CS) during assembly tasks. To tackle this challenge, a symbolic based approach was proposed, in which a force/torque signals; during demonstrated assembly, the task was encoded as a sequence of symbols. The third challenge was to learn a human-robot co-manipulation task based on LfD. In this case, an ensemble machine learning approach was proposed to capture such a skill. The last challenge in this thesis, was to learn an assembly task by demonstration with the presents of parts geometrical variation. Hence, a new learning approach based on Artificial Potential Field (APF) to learn a Peg-in-Hole (PiH) assembly task which includes no-contact and contact phases. To sum up, this thesis focuses on the use of data-driven approaches to learning force based task in an industrial context. Hence, different machine learning approaches were implemented, developed and evaluated in different scenarios. Then, the performance of these approaches was compared with mathematical modelling based approaches.</div