Application of machine learning algorithm in the sheet metal industry : an exploratory case study

This study solved a practical problem in a case in the sheet metal industry using machine learning and deep learning algorithms. The problem in the case company was related to detecting the minimum gaps between components, which were produced after the punching operation of a metal sheet. Due to the narrow gaps between the components, an automated sheer machine could not grip the rest of the sheet skeleton properly after the punching operation. This resulted in some of the scraped sheet on the worktable being left behind, which needed a human operator to intervene. This caused an extra trigger to the production line that resulted in a break in production. To solve this critical problem, the relevant images of the components and the gaps between them were analyzed using machine learning and deep learning techniques. The outcome of this study contributed to eliminating the production bottleneck by optimizing the gaps between the punched components. This optimization process facilitated the easy and safe movement of the gripper machine and contributed to minimizing the sheet waste.© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed

A Methodology for Data-Informed Process Control in Progressive Die Sheet Metal Forming

This thesis investigates the coupled relationship between the strip transfer and forming operations in progressive die sheet metal forming, including the effects of the strip layout geometry, and its effect on the process speed and accuracy. Servo-actuated strip lifters and feeder are considered to assist in minimizing the dynamic response of the strip during the transfer process. A methodology is proposed for identifying suitable trajectories to prescribe the motion of active strip lifters and feeder to obtain consistent part quality without risk of process failures for a progressive die operation. Multiple iterations of a finite element (FE) model were constructed in LS-DYNA to simulate a progressive die operation. Various FE analysis techniques were used to reduce the computational cost of the simulations to allow for enough data to be generated for machine learning applications. Both explicit and implicit time-integration schemes were considered in iterations of the FE model. Both single and dual carrier strip layouts were considered. The results of the FE simulations suggest that the single carrier strip layouts produce larger predicted dynamic displacements and rotations of the work-piece as compared to the dual carrier strip layouts during strip transfer. Furthermore, the single carrier strip layout is shown to be susceptible to strip misalignment. The final version of the FE model utilized geometry based on a demonstrator tool being deployed at the Technische Universität München. A total of 1000 simulations were generated, 500 each for the ‘I’ and ‘O’ stretch-web types using a single carrier strip layout. Each simulation considered a unique permutation of control inputs sampled from the set of possible strokes rates and trajectories for the lifters and feeder. Cubic splines were used to generate the trajectories for the strip lifter and feeder by varying the position of two knots used to define the shape of the spline. The results from the 1000 simulations indicate that in general the ‘S’ stretch-web produces a larger variance in the predicted dynamic response and ‘work-piece placement as compared to the ‘I’ stretchweb. Furthermore, the stroke rate and lifter trajectory were shown to have a large influence on the overshooting of the work-pieces during strip transfer and the probability of whether tooling collisions occurred. Multiple machine learning models were trained on the data generated by the final FE model. Two types of classifiers were constructed using neural network and XGBoost architectures. The first classifier predicts whether the clearance between the strip and binder are within a specified tolerance (to prevent collision with the tooling) during strip transfer. The second classifier predicts whether the placement accuracy of the work-piece on the forming die after strip transfer is within a specified tolerance. A range of tolerances were considered when labeling the data for both classifiers. Nestedcross fold validation was used to select the hyperparameter tuning and model selection. The machine learning classifiers were used to test all possible control inputs using a ‘minimum feed clearance’ of 10 mm and a maximum ‘work-piece placement error of 0.11 mm. The maximum stroke rate at which a given pair of lifter and feeder trajectories can operate was identified for all permutations. Five permutations that achieved the highest predicted stroke rate were simulated for an additional five strokes. The classifiers showed a reasonable ability to predict the ‘minimum feed clearance’ and ‘workpiece placement in the extended FE simulations for the selected trajectories, but, was unable to account for the strip misalignment which occurred after several strokes in all simulations. This research successfully demonstrates a methodology for using machine learning models trained on FE simulations to predict process outcomes of a progressive die operation with variable feeder and lifter trajectories. The FE simulations used to train the machine learning models were generated by adopting computationally-effective FE modelling techniques in a single press stroke model. The machine learning models were shown to reasonably predict the process outcomes of novel input permutations in a multi-stroke FE simulation. One of the largest constraints in this research is the FE simulation time which limited the model complexity that could be considered in the training set generation. Furthermore, the demonstration of the machine learning predictions for a multi-stroke process was limited due to the susceptibility of the single carrier strip layout to misalign after strip progression. Future work should consider the use of dual carrier strip layouts for the generation of the training data. Alternative approaches may also be considered, such as a machine learning framework for directly predicting the forward dynamics of the progressive die operation or a co-simulation approach in which a robust controller interacts directly with the FE simulation

An analytical cost estimation approach for generic sheet metal 3D models

This paper defines a systematic workflow for production cost estimation of sheet metal stamped components. The approach represents a solution toward the adoption of Design to Cost methods during early product design. It consists in a sequence of steps that, starting from a 3D CAD model with annotations (material, roughness and tolerances) and production information (batch and production volume) leads to the manufacturing cost through an analytic cost breakdown (raw material, stamping and accessory processes, setup and tooling). The calculation process mainly consists in a first step where geometric algorithms calculate the sheet metal blank (dimensions, shape, thickness) and specific product features (e.g. flanges, louvers, embossing, etc.). The following steps allow to calculate the raw material, the stamping process and the process-related parameters, which are the manufacturing cost drivers (e.g. press, stamping rate/sequence/force and die dimensions/weight). The manufacturing cost is the sum of the previous calculated items. Testing the approach for three different components, the average absolute deviation measured between the estimated and actual cost was less than 10% and such a result looks promising for adopting this method for evaluating alternative design solutions

Closed-loop control of product properties in metal forming

Metal forming processes operate in conditions of uncertainty due to parameter variation and imperfect understanding. This uncertainty leads to a degradation of product properties from customer specifications, which can be reduced by the use of closed-loop control. A framework of analysis is presented for understanding closed-loop control in metal forming, allowing an assessment of current and future developments in actuators, sensors and models. This leads to a survey of current and emerging applications across a broad spectrum of metal forming processes, and a discussion of likely developments.Engineering and Physical Sciences Research Council (Grant ID: EP/K018108/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cirp.2016.06.00

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Recent advances and applications of machine learning in metal forming processes

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Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics