Automated Derivation of Optimal Production Sequences from Product Data

Customer specific, individual products nowadays lead to larger product variance and shorter time to market. This requires efficient production system planning. In addition, due to a larger system complexity, each iteration of the planning process itself gets soaringly complex. Time constraints and complexity, therefore, emphasize the necessity of supporting humans in planning modern production systems. Especially the determination of the production sequence holds immense potential and tends to get even more complex within specific production technologies. Exemplarily, this article focuses on welding sequences. Here, domain knowledge from product development and production planning needs to be holistically integrated. Furthermore, implicit, historic knowledge needs to be formalized and used in today’s planning tasks. This article introduces a methodical approach and a corresponding toolchain to derive optimal production sequences from customer product data which is validated using welding processes. For this, firstly, a reference system is build up consisting of historic product data (e.g. part list, CAD data) and corresponding production system characteristics (e.g. number and specifications of machines). The main aspect is to use similarities between the new product variant and assemblies from the reference system, to determine implications of product specifications on the process sequence. Overall, such restrictions can be displayed using Model-Based Systems Engineering. Relevant information (e.g. weld seam lengths) can be used to compute the optimal weld seam order regarding minimal cycle times, for example. This requires a parametric encoding of product and production system. In a nutshell, this approach covers the automated derivation of an optimal production sequence for new product variants, based on system information and product similarities, to tackle time constraints and complexity by suggesting initial planning drafts

Customized sorting and packaging machine

India is a country which has a cornerstone of agriculture. And as it comes to fruit/vegetable sorting and packaging in India, human labor has been a vital part. With manual hand picking, it is a very laborious task to classify the quality of fruits/vegetables and simultaneously pack them. One leading-edge technology for the fulfilment of this purpose is ‘Image Processing’ technology which is extremely fast and cost-efficient. Our whole idea revolves around the fact that each fruit will be inspected, sort and simultaneously packed. For the same, a low cost automated mechatronic system has designed consisting of a solitary mechanical arrangement, which is controlled and synchronized through electronic components. Fruits/vegetables are sorted as high-quality and low-quality on the basis of physical appearance and weight. For this, a suitable algorithm is designed using the Open CV library. And the sorting is done using Arduino Uno and Raspberry pi. Hence the aim is to develop a sorting and packaging facility that can be established at the very root level itself which will be economically compact and accurate and will give more justice to farmers

Assessing self-organization and emergence in Evolvable Assembly Systems (EAS)

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThere is a growing interest from industry in the applications of distributed IT. Currently, most modern plants use distributed controllers either to control production processes, monitor them or both. Despite the efforts on the last years to improve the implementation of the new manufacturing paradigms, the industry is still mainly using traditional controllers. Now, more than ever, with an economic crisis the costumers are searching for cheap and customized products, which represents a great opportunity for the new paradigms to claim their space in the market. Most of the research on distributed manufacturing is regarding the control and communication infrastructure. They are key aspects for self-organization and there is a lack of study on the metrics that regulate the self-organization and autonomous response of modern production paradigms. This thesis presents a probabilistic framework that promotes self-organization on a multiagent system based on a new manufacturing concept, the Evolvable Assembly Systems/Evolvable Production Systems. A methodology is proposed to assess the impact of self-organization on the system behavior, by the application of the probabilistic framework that has the dual purpose of controlling and explaining the system dynamics. The probabilistic framework shows the likelihood of some resources being allocated to the production process. This information is constantly updated and exchanged by the agents that compose the system. The emergent effect of this self-organization dynamic is an even load balancing across the system without any centralized controller. The target systems of this work are therefore small systems with small production batches but with a high variability of production conditions and products. The agents that compose the system originated in the agent based architecture of the FP7-IDEAS proejct. This work has extended these agents and the outcome has been tested in the IDEAS demonstrators, as the changes have been incorporated in the latest version of the architecture, and in a simulation and more controlled environment were the proposed metric and its influence were assessed

Using object detection technology to identify defects in clothing for blind people

Blind people often encounter challenges in managing their clothing, specifically in identifying defects such as stains or holes. With the progress of the computer vision field, it is crucial to minimize these limitations as much as possible to assist blind people with selecting appropriate clothing. Therefore, the objective of this paper is to use object detection technology to categorize and detect stains on garments. The defect detection system proposed in this study relies on the You Only Look Once (YOLO) architecture, which is a single-stage object detector that is well-suited for automated inspection tasks. The authors collected a dataset of clothing with defects and used it to train and evaluate the proposed system. The methodology used for the optimization of the defect detection system was based on three main components: (i) increasing the dataset with new defects, illumination conditions, and backgrounds, (ii) introducing data augmentation, and (iii) introducing defect classification. The authors compared and evaluated three different YOLOv5 models. The results of this study demonstrate that the proposed approach is effective and suitable for different challenging defect detection conditions, showing high average precision (AP) values, and paving the way for a mobile application to be accessible for the blind community.This work has been supported by national funds through FCT—Fundacão para a Ciência e Tecnologia, within the Projects Scope: UIDB/00319/2020, UIDB/05549/2020, UIDP/05549/2020, UIDP/04077/2020, and UIDB/04077/2020

Automated PCB identification and defect-detection system (APIDS)

Ever growing PCB industry requires automation during manufacturing process to produce defect free products. Machine Vision is widely used as popular means of inspection to find defects in PCBs. However, it is still largely dependent on user input to select algorithm set for the PCB under inspection prior to the beginning of the process. Continuous increase in computation power of computers and image quality of image acquisition devices demands new methods for further automation. This paper proposes a new method to achieve further automation by identifying the type of PCB under inspection prior to begin defect inspection process. Identification of PCB is achieved by using local feature detectors SURF and ORB and using the orientation data acquired to transform the PCB image to the reference image for inspection of defects. A close-loop system is produced as a prototype to reflect the practicality of the idea. A Graphical User Interface was developed using MATLAB to present the proposed system. Test data contained 29 PCBs. Each PCB was tested 5 times for camera acquired images and 3 times for database images. The identification accuracy is 98.66% for database images and 100% for images acquired from the camera. The time taken to detect the model of PCB is recorded and is significantly lower for ORB based identification than SURF based. The system is also a close loop system which detects defects in PCB units. The detection of defects has highest accuracy of 92.3% for best controlled environment scenario. With controlled environment, the system could detect defects in PCB pertaining to smallest of components such as SMDs

Envelope-Wavelet Packet Transform for Machine Condition Monitoring

Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the feature vector. This increase in the dimensionality has the tendency to 'over-fit' the training data and could mislead the fault diagnostic model. In this paper a novel technique, envelope wavelet packet transform (EWPT) is proposed in which features are extracted based on wavelet packet transform of the filtered envelope signal rather than the overall vibration signal. It not only reduces the computational overhead in terms of reduced number of wavelet decomposition levels and features but also improves the fault detection accuracy. Analytical expressions are provided for the optimal frequency resolution and decomposition level selection in EWPT. Experimental results with both actual and simulated machine fault data demonstrate significant gain in fault detection ability by EWPT at reduced complexity compared to existing techniques

A HARDWAREINTHELOOP SIMULATOR BASED ON REAL SKODA SUPERB VEHICLE AND RTLAB/CARSIM

This paper describes the design and realization of a hardware-in-the-loop simulator made from a real Skoda Superb vehicle. A combination of RT-LAB and CarSim software is used for real-time control and for handling the sensoric subsystems. The simulator provides almost realistic testing of driving cycles with on-line visualization. This unique device can be used in various fields of research

Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08, 2023 : programme

In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: – Precision engineering and measurement technology Nanofabrication – Industry 4.0 and digitalisation in mechanical engineering – Mechatronics, biomechatronics and mechanism technology – Systems engineering – Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university