Models to evaluate the performance of high-mix low-volume manual or semi-automatic assembly lines

To address mass customisation demand trends, assembly line flexibility and productivity are critical. Industry 4.0 technologies could support assembly operations to this end. However, clear implementation methodologies are still lacking. This article presents two models for evaluating the most relevant Key Performance Indicators (KPIs) of manual or semi-automatic assembly lines, allowing to maximise the return of investment of any digital technology addition. MATLAB® was used to implement a parametric model, and FlexSim® was employed to build a discrete event simulation model. The models were validated using data of two industrial study cases from a global white goods manufacturer

Intelligent composite layup by the application of low cost tracking and projection technologies

Hand layup is still the dominant forming process for the creation of the widest range of complex geometry and mixed material composite parts. However, this process is still poorly understood and informed, limiting productivity. This paper seeks to address this issue by proposing a novel and low cost system enabling a laminator to be guided in real-time, based on a predetermined instruction set, thus improving the standardisation of produced components. Within this paper the current methodologies are critiqued and future trends are predicted, prior to introducing the required input and outputs, and developing the implemented system. As a demonstrator a U-Shaped component typical of the complex geometry found in many difficult to manufacture composite parts was chosen, and its drapeability assessed by the use of a kinematic drape simulation tool. An experienced laminator's knowledgebase was then used to divide the tool into a finite number of features, with layup conducted by projecting and sequentially highlighting target features while tracking a laminator's hand movements across the ply. The system has been implemented with affordable hardware and demonstrates tangible benefits in comparison to currently employed laser-based systems. It has shown remarkable success to date, with rapid Technology Readiness Level advancement. This is a major stepping stone towards augmenting manual labour, with further benefits including more appropriate automation

BrickPal: Augmented Reality-based Assembly Instructions for Brick Models

The assembly instruction is a mandatory component of Lego-like brick sets.The conventional production of assembly instructions requires a considerable amount of manual fine-tuning, which is intractable for casual users and customized brick sets.Moreover, the traditional paper-based instructions lack expressiveness and interactivity.To tackle the two problems above, we present BrickPal, an augmented reality-based system, which visualizes assembly instructions in an augmented reality head-mounted display. It utilizes Natural Language Processing (NLP) techniques to generate plausible assembly sequences, and provide real-time guidance in the AR headset.Our user study demonstrates BrickPal's effectiveness at assisting users in brick assembly compared to traditional assembly methods. Additionally, the NLP algorithm-generated assembly sequences achieve the same usability with manually adapted sequences.Comment: 9 pages,7 figures. Project URL: https://origami.dance/brickpa

Productivity and flexibility improvement of assembly lines for high-mix low-volume production. A white goods industry case

Las tendencias globales de la personalización e individualización en masa impulsan la producción industrial en serie corta y variada; y por tanto una gran variedad de productos en pequeñas cantidades. Por ello, la customización en masa precisa de sistemas de ensamblaje que sean a la vez altamente productivos y flexibles, a diferencia de la tradicional oposición entre ambas características. La llamada cuarta revolución industrial trae diversas tecnologías habilitadoras que podrían ser útiles para abordar este problema. Sin embargo, las metodologías para implementar el ensamblaje 4.0 todavía no han sido resueltas. De hecho, para aprovechar todas las ventajas potenciales de la Industria 4.0, es necesario contar con un nivel previo de excelencia operacional y un análisis holístico de los sistemas productivos. Esta tesis tiene como objetivo entender y definir cómo mejorar la productividad y la flexibilidad de las operaciones de montaje en serie corta y variada.Esta meta se ha dividido en tres objetivos. El primer objetivo consiste en comprender las relaciones entre la Industria 4.0 y las operaciones de ensamblaje, así como sus implicaciones para los operarios. El segundo objetivo consiste en desarrollar una metodología y las herramientas necesarias para evaluar el rendimiento de diferentes configuraciones de cadenas de ensamblaje. El último objetivo consiste en el diseño de sistemas de ensamblaje que permitan incrementar su productividad al menos un 25 %, produciendo en serie corta y variada, mediante la combinación de puestos de montaje manual y estaciones automatizadas.Para abordar la fase de comprensión y definición del problema, se llevó a cabo una revisión bibliográfica sistemática y se desarrolló un marco conceptual para el Ensamblaje 4.0. Se desarrollaron, verificaron y validaron dos herramientas de evaluación del rendimiento: un modelo matemático analítico y varios modelos de simulación por eventos discretos. Para la verificación, y como punto de partida para los análisis, se ha utilizado un caso de estudio industrial de un fabricante global de electrodomésticos. Se han empleado múltiples escenarios de simulación y técnicas de diseño de experimentos para investigar tres cuestiones clave.En primer lugar, se identificaron los factores más críticos para el rendimiento de líneas de montaje manuales multi-modelo. En segundo lugar, se analizó el rendimiento de líneas de montaje semiautomáticas paralelas con operarios móviles en comparación con líneas semiautomáticas o manuales con operarios fijos, empleando diversos escenarios de demanda en serie corta y variada. Por último, se investigó el uso de trenes milkrun para la logística interna de líneas de ensamblaje multi-modelo bajo la influencia de perturbaciones.Los resultados de las simulaciones muestran que las líneas paralelas con operarios móviles pueden superar a las de operarios fijos en cualquier escenario de demanda, alcanzando como mínimo el objetivo de mejorar la productividad en un 25% o más. También permiten reducir cómodamente el número de operarios trabajando en la línea sin afectar negativamente al equilibrado de la misma, posibilitando la producción eficiente de bajo volumen. Los resultados de las simulaciones de logística interna indican que los milkrun pueden proteger las líneas de ensamblaje de las perturbaciones originadas en procesos aguas arriba.Futuras líneas de investigación en base a los resultados obtenidos en esta tesis podrían incluir la expansión e integración de los modelos de simulación actuales para analizar las cadenas de montaje paralelas con operarios móviles incorporando logística, averías y mantenimiento, problemas de control de calidad y políticas de gestión de los retrabajos. Otra línea podría ser el uso de diferentes herramienta para el análisis del desempeño como, por ejemplo, técnicas de programación de la producción que permitan evaluar el desempeño operacional de diferentes configuraciones de cadenas de montaje con operarios móviles, tanto en términos de automatización como de organización en planta. Podrían incorporarse tecnologías de la Industria 4.0 a los modelos de simulación para evaluar su impacto operacional global ¿como cobots para ensamblaje o para la manipulación de materiales, realidad aumentada para el apoyo cognitivo a los operarios, o AGVs para la conducciónde los trenes milkrun. Por último, el trabajo presentado en esta tesis acerca las líneas de ensamblaje semiautomáticas con operarios móviles a su implementación industrial.<br /

Disassembly sequence planning validated thru augmented reality for a speed reducer

The lifecycle of a product is getting shorter in today’s market realities. Latest developments in the industry are heading towards achieving products that are easy to recycle, by developing further technological advances in raw materials ought to include input from End of Life (EOL) products so a reduction of natural harm could be achieved, hence reducing the overall production environmental footprint. Therefore, the approach taken as a design for environment, a key request nowadays in order to develop products that would ease the reverse manufacturing process leading to a more efficient element recycling for later use as spare parts or remanufacturing. The methodology proposed compares three probable disassembly sequences following a comparison of literature-found procedures between genetic algorithms and as a “state space search” problem, followed by a hybrid approach developed by the authors. Time and evaluation of these procedures reached to the best performing sequence. A subsequent augmented reality disassembly simulation was performed with the top-scored operation sequence with which the user is better able to familiarize himself with the assembly than a traditional paper manual, therefore enlightening the feasibility of the top performing sequence in the real world

Using virtual reality and 3D industrial numerical models for immersive interactive checklists

At the different stages of the PLM, companies develop numerous checklist-based procedures involving prototype inspection and testing. Besides, techniques from CAD, 3D imaging, animation and virtual reality now form a mature set of tools for industrial applications. The work presented in this article develops a unique framework for immersive checklist-based project reviews that applies to all steps of the PLM. It combines immersive navigation in the checklist, virtual experiments when needed and multimedia update of the checklist. It provides a generic tool, independent of the considered checklist, relies on the integration of various VR tools and concepts, in a modular way, and uses an original gesture recognition. Feasibility experiments are presented, validating the benefits of the approach

Human behavior understanding for worker-centered intelligent manufacturing

“In a worker-centered intelligent manufacturing system, sensing and understanding of the worker’s behavior are the primary tasks, which are essential for automatic performance evaluation & optimization, intelligent training & assistance, and human-robot collaboration. In this study, a worker-centered training & assistant system is proposed for intelligent manufacturing, which is featured with self-awareness and active-guidance. To understand the hand behavior, a method is proposed for complex hand gesture recognition using Convolutional Neural Networks (CNN) with multiview augmentation and inference fusion, from depth images captured by Microsoft Kinect. To sense and understand the worker in a more comprehensive way, a multi-modal approach is proposed for worker activity recognition using Inertial Measurement Unit (IMU) signals obtained from a Myo armband and videos from a visual camera. To automatically learn the importance of different sensors, a novel attention-based approach is proposed to human activity recognition using multiple IMU sensors worn at different body locations. To deploy the developed algorithms to the factory floor, a real-time assembly operation recognition system is proposed with fog computing and transfer learning. The proposed worker-centered training & assistant system has been validated and demonstrated the feasibility and great potential for applying to the manufacturing industry for frontline workers. Our developed approaches have been evaluated: 1) the multi-view approach outperforms the state-of-the-arts on two public benchmark datasets, 2) the multi-modal approach achieves an accuracy of 97% on a worker activity dataset including 6 activities and achieves the best performance on a public dataset, 3) the attention-based method outperforms the state-of-the-art methods on five publicly available datasets, and 4) the developed transfer learning model achieves a real-time recognition accuracy of 95% on a dataset including 10 worker operations”--Abstract, page iv

A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition

Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart Factories in Industry 4.0

In future industry, defined as Industry 4.0, production planning and control strategies will be executed by human beings backed by computational tools for decision making; One of these tools is shop floor simulation, and a natural scenario to learn about how to use it for productive processes design and control are the Learning Factories. In this chapter, shop floor simulation is identified as a tool for planning and controlling production, also a state of the art about its implementation is exposed in academic and industrial environments. In addition, the trends in the construction of the Learning Factories are shown, and some aspects about how they can be used for shop floor simulation. This work also proposes the realization of a digital model in EAFIT University Learning Factory as a first step towards digital learning factory