Achieving Sustainability in Manufacturing Using Robotic Methodologies

Ogbemhe, John; Mpofu, Khumbulani; Tlale, N.S.

doi:10.1016/j.promfg.2017.02.056

Cited by 32 publications

(17 citation statements)

References 7 publications

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“…In the definition of the sustainable economic model, natural resources such as energy and material are considered as limited resources. Industrial robots can be utilized as a potential solution for the issue by increasing the productivity and reducing the wastes which results in the reduction in emission and contributing to sustainability [1]. In the past decade, designing more precise and accurate robots has been a point of research interest of many scientists.…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Azizi

2020

Complexity

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Industrial robots have a great impact on increasing the productivity and reducing the time of the manufacturing process. To serve this purpose, in the past decade, many researchers have concentrated to optimize robotic models utilizing artificial intelligence (AI) techniques. Gimbal joints because of their adjustable mechanical advantages have been investigated as a replacement for traditional revolute joints, especially when they are supposed to have tiny motions. In this research, the genetic algorithm (GA), a well-known evolutionary technique, has been adopted to find optimal parameters of the gimbal joints. Since adopting the GA is a time-consuming process, an artificial neural network (ANN) architecture has been proposed to model the behavior of the GA. The result shows that the proposed ANN model can be used instead of the complex and time-consuming GA in the process of finding the optimal parameters of the gimbal joint.

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Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Azizi

2020

Complexity

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“…Recent decades have seen explosive growth in the robotics industry [1][2][3]. The implementation of robots into domestic and medical contexts requires addressing the potential for conventional rigid designs to harm human operators or bystanders.…”

Section: Introductionmentioning

confidence: 99%

Performance Optimization of Polymer Fibre Actuators for Soft Robotics

et al. 2020

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Analytical modeling of soft pneumatic actuators constitutes a powerful tool for the systematic design and characterization of these key components of soft robotics. Here, we maximize the quasi-static bending angle of a soft pneumatic actuator by optimizing its cross-section for a fixed positive pressure inside it. We begin by formulating a general theoretical framework for the analytical calculation of the bending angle of pneumatic actuators with arbitrary cross-sections, which is then applied to an actuator made of a circular polymer tube and an asymmetric patch in the shape of a hollow-cylinder sector on its outer surface. It is shown that the maximal bending angle of this actuator can be achieved using a wide range of patches with different optimal dimensions and approximately the same cross-sectional area, which decreases with pressure. We also calculate the optimal dimensions of thin and small patches in thin pneumatic actuators. Our analytical results lead to clear design guidelines, which may prove useful for engineering and optimization of the key components of soft robotics with superior features.

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“…The full benefits of industrial robots can only be achieved through proper trajectory planning. The problem of trajectory planning is relevant for appropriate use of the costly robotic systems to mitigate undesirable effects such as vibration and even wear on the mechanical structure of the system [5]- [7]. Rough and jerky motions cause increased wear and fluctuations in the robot by the excitation of resonance in the manipulator.…”

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Scheme for Robotic Welding Along Complex Joints Using a Hybrid Multi-Objective Genetic Algorithm

2019

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The problem of trajectory planning is relevant for the proper use of costly robotic systems to mitigate undesirable effects such as vibration and even wear on the mechanical structure of the system. The objective of this study is to design trajectories that are devoid of collision, velocity, acceleration, jerk and snap discontinuities so that the cycle time required to complete the process can be reduced. The trajectory design was constructed for all the six joints, using a 9 th order Bezier curve to accommodate the ten boundary conditions required to satisfy the continuity constraints for joints displacement, velocity, acceleration, jerk and snap. The scheme combines the multi-objective genetic algorithm and the multi-objective goal attainment algorithm to solve the problem of total tracking error reduction during arc welding. The use of a hybrid multi-objective algorithm shows an improved average spread, average distance, number of iteration and computational time. Also, it can be concluded from the constraints studied, that the optimal path in terms of the robots dynamic constraints can achieve the expected tracking ability in terms of the optimal joint angles, velocities, acceleration, jerk, snap and torque.

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Achieving Sustainability in Manufacturing Using Robotic Methodologies

Cited by 32 publications

References 7 publications

Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Performance Optimization of Polymer Fibre Actuators for Soft Robotics

Optimal Trajectory Scheme for Robotic Welding Along Complex Joints Using a Hybrid Multi-Objective Genetic Algorithm

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