Quality and productivity driven trajectory optimisation for robotic handling of compliant sheet metal parts in multi-press stamping lines

This paper investigates trajectory generation for multi-robot systems that handle compliant parts in order to minimise deformations during handling, which is important to reduce the risk of affecting the part’s dimensional quality. An optimisation methodology is proposed to generate deformation-minimal multi-robot coordinated trajectories for predefined robot paths and cycle-time. The novelty of the proposed optimisation methodology is that it efficiently estimates part deformations using a precomputed Response Surface Model (RSM), which is based on data samples generated by Finite Element Analysis (FEA) of the handled part and end-effector. The end-effector holding forces, plastic part deformations, collision-avoidance and multi-robot coordination are also considered as constraints in the optimisation model. The optimised trajectories are experimentally validated and the results show that the proposed optimisation methodology is able to significantly reduce the deformations of the part during handling, i.e. up to 12% with the same cycle-time in the case study that involves handling compliant sheet metal parts. This investigation provides insights into generating specialised trajectories for material handling of compliant parts that can systematically minimise part deformations to ensure final dimensional quality

3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds

Closed loop dimensional quality control for an assembly system entails controlling process parameters based on dimensional quality measurement data to ensure that products conform to quality requirements. Effective closed-loop quality control reduces machine downtime and increases productivity, as well as enables efficient predictive maintenance and continuous improvement of product quality. Accurate estimation of dimensional variations on the final part is a key requirement, in order to detect and correct process faults, for effective closed-loop quality control. Nowadays, this is often done by experienced process engineers, using a trial-and-error approach, which is time-consuming and can be unreliable. In this paper, a novel model to estimate process parameters error variations using high-density cloud-of-point measurement data captured by 3D optical scanners is proposed. The proposed model termed as PointDevNet uses 3D convolutional neural networks (CNN) that leverage the deviations of key nodes and their local neighbourhood to estimate the process parameter variations. These process parameters variation estimates are leveraged for root cause isolation as a necessary but currently missing step needed for the development of closed-loop quality control framework. The proposed model is compared with an existing state-of-the-art linear model under different scenarios such as a single and multiple root causes, and the presence of measurement noise. The state-of-the-art model is evaluated under different point selections and results are compared to the proposed model with consideration to an industrial case study involving a sheet metal part, i.e. window reinforcement panel

Energy model for motion planning of 2D-belt press line tending robots

A current trend in production is to reduce energy consumption where possible not only to lower the cost but also to be a more energy efficient entity. This paper presents an energy model to estimate the electrical energy consumption of 2D-belt robots used for material handling in multi-stage sheet metal press lines. An estimation of the energy consumption is computed by the proposed energy model based on the robot components’ specifications, the robot path and trajectory. The proposed model can predict the energy consumption offline by simulation, and thus, before installation, avoiding the need for physical experiments. It is demonstrated that it can be used for predicting potential energy reductions achieved by optimising the motion planning. Additionally, it is also shown how to investigate the energy saving achieved by using mechanical brakes when the robot is idle. This effectively illustrates the usefulness of the proposed energy model

Constructive cooperative coevolution for optimising interacting production stations

Engineering problems have characteristics such as a large number of variables, non-linear, computationally expensive, complex and black-box (i.e. unknown internal structure). These characteristics prompt difficulties for existing optimisation techniques. A consequence of this is that the required optimisation time rapidly increases beyond what is practical. There is a needfor dedicated techniques to exploit the power of mathematical optimisation tosolve engineering problems. The objective of this thesis is to investigate thisneed within the field of automation, specifically for control optimisation ofautomated systems.The thesis proposes an optimisation algorithm for optimising the controlof automated interacting production stations (i.e. independent stations thatinteract by for example material handling robots). The objective of the optimisation is to increase the production rate of such systems. The non-separable nature of these problems due to the interactions, makes them hard to optimise.The proposed algorithm is called the Constructive Cooperative CoevolutionAlgorithm (C3). The thesis presents the experimental evaluation of C3, bothon theoretical and real-world problems. For the theoretical problems, C3 istested on a set of standard benchmark functions. The performance, robustness and convergence speed of C3 is compared with the algorithms. This shows that C3 is a competitive optimisation algorithm for large-scale non-separable problems.C3 is also evaluated on real-world industrial problems, concerning thecontrol of interacting production stations, and compared with other optimisation algorithms on these problems. This shows that C3 is very well-suited for these problems. The importance of considering the energy consumption and equipment wear, next to the production rate, in the objective function is also investigated. This shows that it is crucial that these are considered to optimise the overall performance of interacting production stations

Constructive cooperative coevolution for optimising interacting production stations

Engineering problems have characteristics such as a large number of variables, non-linear, computationally expensive, complex and black-box (i.e. unknown internal structure). These characteristics prompt difficulties for existing optimisation techniques. A consequence of this is that the required optimisation time rapidly increases beyond what is practical. There is a needfor dedicated techniques to exploit the power of mathematical optimisation tosolve engineering problems. The objective of this thesis is to investigate thisneed within the field of automation, specifically for control optimisation ofautomated systems.The thesis proposes an optimisation algorithm for optimising the controlof automated interacting production stations (i.e. independent stations thatinteract by for example material handling robots). The objective of the optimisation is to increase the production rate of such systems. The non-separable nature of these problems due to the interactions, makes them hard to optimise.The proposed algorithm is called the Constructive Cooperative CoevolutionAlgorithm (C3). The thesis presents the experimental evaluation of C3, bothon theoretical and real-world problems. For the theoretical problems, C3 istested on a set of standard benchmark functions. The performance, robustness and convergence speed of C3 is compared with the algorithms. This shows that C3 is a competitive optimisation algorithm for large-scale non-separable problems.C3 is also evaluated on real-world industrial problems, concerning thecontrol of interacting production stations, and compared with other optimisation algorithms on these problems. This shows that C3 is very well-suited for these problems. The importance of considering the energy consumption and equipment wear, next to the production rate, in the objective function is also investigated. This shows that it is crucial that these are considered to optimise the overall performance of interacting production stations

Multi-Robot Motion Planning Optimisation for Handling Sheet Metal Parts

Motion planning for robot operations is concerned with path planning and trajectory generation. In multi-robot systems, i.e. with multiple robots operating simultaneously in a shared workspace, the motion planning also needs to coordinate the robots' motions to avoid collisions between them. The multi-robot coordination decides the cycle-time for the planned paths and trajectories since it determines to which extend the operations can take place simultaneously without colliding. To obtain the quickest cycle-time, there needs to bean optimal balance between, on the one hand short paths and fast trajectories, and on the other hand possibly longer paths and slower trajectories to allow that the operations take place simultaneously in the shared workspace. Due to the inter-dependencies, it becomes necessary to consider the path planning, trajectory generation and multi-robot coordination together as one optimisation problem in order to find this optimal balance.This thesis focusses on optimising the motion planning for multi-robot material handling systems of sheet metal parts. A methodology to model the relevant aspects of this motion planning problem together as one multi-disciplinary optimisation problem for Simulation based Optimisation (SBO) is proposed. The identified relevant aspects include path planning,trajectory generation, multi-robot coordination, collision-avoidance, motion smoothness, end-effectors' holding force, cycle-time, robot wear, energy efficiency, part deformations, induced stresses in the part, and end-effectors' design. The cycle-time is not always the (only) objective since it is sometimes equally/more important to minimise robot wear, energy consumption, and/or part deformations. Different scenarios for these other objectives are therefore also investigated. Specialised single- and multi-objective algorithms are proposed for optimising the motion planning of these multi-robot systems. This thesis also investigates how to optimise the velocity and acceleration profiles of the coordinated trajectories for multi-robot material handling of sheet metal parts. Another modelling methodology is proposed that is based on a novel mathematical model that parametrises the velocity and acceleration profiles of the trajectories, while including the relevant aspects of the motion planning problem excluding the path planning since the paths are now predefined.This enables generating optimised trajectories that have tailored velocity and acceleration profiles for the specific material handling operations in order to minimise the cycle-time,energy consumption, or deformations of the handled parts.The proposed methodologies are evaluated in different scenarios. This is done for real world industrial case studies that consider the multi-robot material handling of a multi-stage tandem sheet metal press line, which is used in the automotive industry to produce the cars' body panels. The optimisation results show that significant improvements can be obtained compared to the current industrial practice

Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014

Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; Simulation-based optimisation often considers computationally expensive problems. Successfully optimising such large scale and complex problems within a practical timeframe is a challenging task. Optimisation techniques to fulfil this need to be developed. A technique to address this involves decomposing the considered problem into smaller subproblems. These subproblems are then optimised separately. In this paper, an efficient algorithm for simulation-based optimisation is proposed. The proposed algorithm extends the cooperative coevolutionary algorithm, which optimises subproblems separately. To optimise the subproblems, the proposed algorithm enables using a deterministic algorithm, next to stochastic genetic algorithms, getting the flexibility of using either type. It also includes a constructive heuristic that creates good initial feasible solutions to reduce the number of fitness calculations. The extension enables solving complex, computationally expensive problems efficiently. The proposed algorithm has been applied on automated sheet metal press lines from the automotive industry. This is a highly complex optimisation problem due to its non-linearity and high dimensionality. The optimisation problem is to find control parameters that maximises the line's production rate. These control parameters determine velocities, time constants, and cam values for critical interactions between components. A simulation model is used for the fitness calculation during the optimisation. The results show that the proposed algorithm manages to solve the press line optimisation problem efficiently. This is a step forward in press line optimisation since this is to the authors’ knowledge the first time a press line has been optimised efficiently in this wa

Coverage path planning with targetted viewpoint sampling for robotic free-from surface inspection

Surface metrology systems are increasingly used for inspecting dimensional quality in manufacturing. The gauge of these measurement systems is often mounted as an end-effector on robotic systems to exploit the robots’ high degrees of freedom to reposition the gauge to different viewpoints. With this repositioning flexibility, a planning methodology becomes necessary in order to carefully plan the viewpoints, as well as the optimal sequence and quickest path to move the gauge to each viewpoint. This paper investigates coverage path planning for robotic single-sided dimensional inspection of free-form surfaces. Reviewing existing feasible state-of-the-art methodologies to solve this problem led to identifying an unexplored opportunity to improve the coverage path planning, specifically by replacing random viewpoint sampling strategy. This study reveals that a non-random targetted viewpoint sampling strategy significantly contributes to solution quality of the resulting planned coverage path. By deploying optimisation during the viewpoint sampling, an optimal set of admissible viewpoints can be obtained, which consequently significantly shortens the cycle-time for the inspection task. Results that evaluate the proposed viewpoint sampling strategy for two industrial sheet metal parts, as well as a comparison with the state-of-the-art are presented. The results show up to 23.8% reduction in cycle-time for the inspection task when using targetted viewpoints sampling

Aspects of Optical Coherence Tomography (OCT) in Healthy Eyes and Eyes with Retinal Diseases

Optical coherence tomography (OCT) is a technique in which cross-sectional images from intraocular tissue can be obtained. The quantitative and qualitative examinations are used for evaluating retinal diseases. Conventional OCT (Stratus) is mainly used, but the new Spectral domain (Cirrus) OCT, which has improved technology, may provide more reliable measurements. The aim of the study was to collect normal values of macular thickness in children and adults and to evaluate the effect of age and/or gender, to compare measurement variability in healthy eyes and eyes with age-related macular degeneration (AMD), to compare Stratus and Cirrus OCT and to study the effect of cataract surgery on macula. Sixty-seven healthy adults and 56 children, 30 patients with AMD, 34 patients with diabetes and cataract and 35 healthy controls were included. The quantitative maps in Stratus and Cirrus were used and manual correction of foveal location was evaluated. Qualitative OCT was compared to fluorescein angiography (FA) after cataract surgery. The mean values of macular thickness in Stratus OCT were 207µm in adults and 204 µm in children. The measurement variability was low. Macular thickness decreased with age in adults, but not in children. No correlation with gender was found. In eyes with wet AMD, there were small differences in measurement variability comparing Stratus and Cirrus OCT. After manual correction in Cirrus OCT, the coefficients of repeatability were improved to values close to the repeatability in normal eyes. Two thirds of the diabetic and half of the control eyes showed leakage on FA after cataract surgery. Qualitative OCT corresponded poorly to FA in diabetic eyes. A thicker macula, assessed with OCT, was often observed without any obvious effect on visual acuity.  OCT was as good as FA in revealing clinically relevant changes in macula after surgery, and was the technique recommended for follow-up