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The purpose of this paper is to develop a new methodology to find out the best robotic assembly sequence amongst feasible robotic sequences.

The feasible robotic assembly sequences were generated based on the assembly constraints and later and artificial immune system (AIS) was implemented to find out the best assembly sequence.

The paper reveals the best assembly sequence.

Robotic assembly has expanded the process capabilities in the manufacturing world because of the fact that it is faster, more efficient, precise and cost‐effective process than any conventional mechanized process. Since a robotic system is a cost‐intensive one it is necessary to find out the correct and optimal sequence with the constraints of the process in mind while dealing with assembled products with large number of parts.

The balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.

This research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.

Theory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.

The additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.

For the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.

This paper aims to present an optimization algorithm combined with the impedance control strategy to optimize the robotic dual peg-in-hole assembly task, and to reduce the assembly time and smooth the contact forces during assembly process with a small number of experiments.

Support vector regression is used to predict the fitness of genes in evolutionary algorithm, which can reduce the number of real-world experiments. The control parameters of the impedance control strategy are defined as genes, and the assembly time is defined as the fitness of genes to evaluate the performance of the selected parameters.

The learning-based evolutionary algorithm is proposed to optimize the dual peg-in-hole assembly process only requiring little prior knowledge instead of modeling for the complex contact states. A virtual simulation and real-world experiments are implemented to demonstrate the effectiveness of the proposed algorithm.

The proposed algorithm is quite useful for the real-world industrial applications, especially the scenarios only allowing a small number of trials.

The paper provides a new solution for applying optimization techniques in real-world tasks. The learning component can solve the data efficiency of the model-free optimization algorithms.

High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time through rate. Some researchers proposed methods such as design-of-experiments (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore, the existing methods have some limitations. This paper aims to improve the process parameter optimization method for complex robotic assembly process.

In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors is investigated. An online parameter optimization method is also proposed.

Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first, and the optimal parameters in production should be changed once the initial tool position is compensated. Experimental results show that the proposed method is very promising in reducing the cycle time in assembly processes.

The proposed method is practical without any limitation.

The proposed technique is implemented and tested using a real industrial application, a valve body assembly process. Hence, the developed method can be directly implemented in production.

This paper provides a technique to improve the assembly efficiency by compensating the initial part location errors. An online parameter optimization method is also proposed to automatically perform the parameter optimization process without human intervention. Compared with the results using other methods, the proposed technology can greatly reduce the assembly cycle time.

The paper describes analyses which have been developed to determine the cost of assembly using a multi‐arm assembly robot fed by a wide variety of different feeding systems and for a wide range of product styles, mixes, and batch sizes.

This paper describes how force‐guided robot can be implemented in the automated assembly of mobile phone. A case study was carried out to investigate the assembly operations and strategies involved. Force‐guided robot was developed and implemented in the real environment. Proportional‐based external force control with hybrid framework was developed and implemented to perform the compliant motion. In order to perform assembly operations, three basic force‐guided robotic skills are identified. These are stopping, alignment and sliding skills, where the motions are guided by the force feedback. The force‐guided robotic skills are combined and reprogrammed with fine motion planning to perform notch‐locked assembly. The system is optimized for high assembly speed while considering the constraints and limitations involved.

The purpose of this paper is to describe the measurement‐assisted assembly of aero‐engine fabricated components and evaluate its capability.

The system described in this paper uses in‐process measurement sensors to determine the component's exact location prior to the assembly operation. The core of the system is a set of algorithms capable of best fitting measurement data to find optimal assembly of components.

The paper demonstrates that with a combination of non‐contact metrology systems and mathematical processing, standard industrial robot can be used to assemble fabricated components. Scanning parts after it has been picked up was very effective as it compensates for possible components deformation during previous manufacturing processes and robot handling errors.

The paper introduces techniques for compensating the deformation that occurs in aero‐engine fabricated components and potential component handling errors. The developed system reduces the reliance on part holding fixtures and instead uses a laser‐guided robot. This ensures that the system is highly flexible and re‐configurable.

To design a reconfigureable flexible fixture for the assembly of a set of sheet metal automotive body parts. Reconfigureable fixturing permits different parts to be grasped for assembly by a fixture without the need to conduct costly redesign and fabrication of hardware fixtures, which is an industry standard in widespread use in industry. While somewhat more complex than fixtures in current use, reconfigureable fixtures provide one solution to the problem of costly redesign of fixtures due to changes in dimensions, or geometry of parts to be assembled.

We propose a novel reconfigureable fixture for robotic assembly of a number of different parts. Motivated by the marine organism, O. vulgaris, commonly referred to as an octopus, which grasps different objects or prey using suction cups, the proposed fixture has three fingers, each equipped with a suction cup, to facilitate the grasping process and increase grasp flexibility. Using this design approach, the fixture is sufficiently general in design to grasp several different parts. To position the suction cups located on the flexible fixture, two linkage‐based mechanisms are employed. Pneumatic cylinders and electric motors are used as actuators. A prototype flexible fixture has been built and experimental results with this prototype confirm the effectiveness of the proposed flexible fixture. Software has been developed to calculate the relative positions and angles in the mechanism as required for reconfiguration.

The proposed reconfigureable fixture, used as an end‐of‐arm tool, permits each of a set of four sheet metal parts to be successfully grasped permitting assembly of these four components, in a robotic assembly work cell.

The proposed flexible fixture is a simple proof‐of‐concept device that is suitable for a laboratory setting. We do not consider part localization of parts when grasped by the reconfigureable fixture.

Assembly operations, in industrial manufacturing operations, are typically heavily reliant on hardware fixtures devices to orient and clamp parts together during assembly operations. While of great importance in such operations, hardware fixtures are very costly to design and build. Further, fixtures are designed for use with parts of specific dimensions and geometry, hence cannot be used to grasp or orient parts with even very small differences in dimensions or geometry. Typically, if parts with different dimensions or geometry are to be assembled, new hardware fixtures must be designed and manufactured to grasp and orient these parts. This lack of flexibility leads to substantial manufacturing costs associated with fixturing. Reconfigureable fixtures permit parts with different geometries to be grasped and oriented for assembly.

Reconfigureable fixtures for use in the automotive manufacturing sector is an important development due to the highly competitive nature of this industry. Rapid introduction of new models of vehicles is greatly facilitated through the use of reconfigureable fixtures which can be reprogrammed to grasp parts of different geometries required for new vehicle models.

Industrial robots are extensively used in the robotic assembly of rigid objects, whereas the assembly of flexible objects using the same robot becomes cumbersome and challenging due to transient disturbance. The transient disturbance causes vibration in the flexible object during robotic manipulation and assembly. This is an important problem as the quick suppression of undesired vibrations reduces the cycle time and increases the efficiency of the assembly process. Thus, this study aims to propose a contactless robot vision-based real-time active vibration suppression approach to handle such a scenario.

A robot-assisted camera calibration method is developed to determine the extrinsic camera parameters with respect to the robot position. Thereafter, an innovative robot vision method is proposed to identify a flexible beam grasped by the robot gripper using a virtual marker and obtain the dimension, tip deflection as well as velocity of the same. To model the dynamic behaviour of the flexible beam, finite element method (FEM) is used. The measured dimensions, tip deflection and velocity of a flexible beam are fed to the FEM model to predict the maximum deflection. The difference between the maximum deflection and static deflection of the beam is used to compute the maximum error. Subsequently, the maximum error is used in the proposed predictive maximum error-based second-stage controller to send the control signal for vibration suppression. The control signal in form of trajectory is communicated to the industrial robot controller that accommodates various types of delays present in the system.

The effectiveness and robustness of the proposed controller have been validated using simulation and experimental implementation on an Asea Brown Boveri make IRB 1410 industrial robot with a standard low frame rate camera sensor. In this experiment, two metallic flexible beams of different dimensions with the same material properties have been considered. The robot vision method measures the dimension within an acceptable error limit i.e. ±3%. The controller can suppress vibration amplitude up to approximately 97% in an average time of 4.2 s and reduces the stability time up to approximately 93% while comparing with control and without control suppression time. The vibration suppression performance is also compared with the results of classical control method and some recent results available in literature.

The important contributions of the current work are the following: an innovative robot-assisted camera calibration method is proposed to determine the extrinsic camera parameters that eliminate the need for any reference such as a checkerboard, robotic assembly, vibration suppression, second-stage controller, camera calibration, flexible beam and robot vision; an approach for robot vision method is developed to identify the object using a virtual marker and measure its dimension grasped by the robot gripper accommodating perspective view; the developed robot vision-based controller works along with FEM model of the flexible beam to predict the tip position and helps in handling different dimensions and material types; an approach has been proposed to handle different types of delays that are part of implementation for effective suppression of vibration; proposed method uses a low frame rate and low-cost camera for the second-stage controller and the controller does not interfere with the internal controller of the industrial robot.