Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting

Overcoming the Challenges of Big Data Analytics Adoption for Small and Medium Sized Enterprises in the Manufacturing Industry

Advanced manufacturing technologies that enable big data analytics can boost productivity, increase efficiency, and enhance innovation. However, small and medium sized factories face unique challenges when implementing that technology. Plant managers of small and medium sized enterprises (SMEs) are often unsure of how to overcome those challenges in order to reap the benefits of big data analytics. This project examined the opportunities that have arisen due to the Fourth Industrial Revolution, also known as Industry 4.0; how small and medium sized manufacturers in the United States can move from traditional methods of manufacturing to advanced manufacturing, and how the additional data generated can enhance decision-making, specifically for plant managers. An investigation of the factors affecting big data analytics adoption in manufacturing SMEs was conducted, and case studies were examined in order to identify the unique challenges that exist and provide recommendations. The results of the investigation suggest that production managers should prioritize a specific area to focus on, use a big data lifecycle management framework, seek help to build and secure their operation systems, train and encourage employees, and collaborate with others in the industry

Enhancement of Jaibot: Developing Safety and Monitoring Features for Jaibot Using IoT Technologies

The Hilti Jaibot, a state-of-the-art construction site drilling robot, has demonstrated remarkable productivity gains while also underscoring the need for improved safety and monitoring capabilities. This study aims to address this need by harnessing Internet of Things (IoT) technologies and predictive maintenance methodologies. The proposed enhancements encompass a comprehensive sensor and camera integration to monitor the robot's environment, coupled with the development of a Long Short-Term Memory (LSTM) predictive maintenance algorithm to preemptively identify operational issues. These improvements enable the Jaibot to autonomously detect and mitigate risks, such as obstacles and human activity, while providing real-time safety alerts to operators. Incorporating quantitative results from our predictive model, which successfully predicts three output variables (X, Y, and Z) using three input variables, we observed varying RMSE and MAPE values. Specifically, X exhibited an RMSE of 77.80% and a MAPE of 242.20%, while Y showed an RMSE of 31.10% and a MAPE of 69.70%, and Z had an RMSE of 34.53% and a MAPE of 82.74%. Notably, Y and Z data displayed high MAPE values, potentially attributed to data inconsistency. To enhance accuracy in our predictive model, we propose the utilization of more complex models and increased data volumes, which may mitigate the observed inconsistencies and lead to improved overall model performance. These findings from our quantitative analysis provide valuable insights for the integration of predictive maintenance algorithms into the Hilti Jaibot and lay the foundation for future advancements in robotic construction, emphasizing the pivotal role of IoT technology and predictive maintenance in shaping the industry's trajectory

Bike Sharing Network Design with Service Levels: The Case of Montreal City

The rapid growth of urbanization and use of motor vehicles in the recent decades has led to many social and economic problems such as: rising fuel prices, energy crises, environmental problems and traffic congestion. All these problems together have decreased the quality of life of people all around the world. In recent years, municipal planners have increasingly focused on extending policies to promote a culture of using bicycles instead of cars. In many cases, urban planners try to build the infrastructure needed to increase the usage of bicycles and one of the measures that has been widely used by them in recent years is bike sharing programs. In this study, we design a bike sharing network considering the objectives of users and system designers simultaneously. From the customers’ view point, walking short distances before picking up and after dropping off a bike would be a preference and they will be satisfied when they find available bikes or empty docks in the system. From the system designer’s perspective, the objective is to achieve these service levels with the minimum network design cost. To achieve this, we develop a mixed integer linear programming model to minimize the cost of opening stations and transportation costs. We consider the pickup and drop off service level constraints in determining the location, dock capacity and demand allocation to the bike stations. A Mixed Integer Linear Programming model is developed and solved using CPLEX Software. In order to validate the network design solutions, we simulate the results of small to medium size instances in Arena. To solve the larger instances of the problem, a Genetic Algorithm is proposed that uses a heuristic method to generate a part of initial solutions and improves the solutions in its stochastic iterations and reaches near-optimal solutions in a reasonable amount of time. The proposed method is illustrated using the city of Montreal as case study

Proposta de Implementação de um Sistema de Gestão de Resíduos para Promover a Otimização do Sistema de Picking de uma Linha de Produção de Automóveis

Um sistema de picking consiste na recolha de peças/produtos e o seu transporte a fim de satisfazer ordens de produção ou de encomendas. Estes sistemas devem ter aptidão para responder aos diversos desafios impostos pela constante evolução dos sistemas de produção, que procuram de forma constante obter um maior grau de flexibilidade para os seus processos a fim de produzir de forma contínua uma maior variedade de produtos. A contínua procura pela melhoria dos sistemas de picking, identifica-se como um importante processo para alcançar sucesso numa empresa. Este trabalho teve como objetivo o estudo de três sistemas de gestão de resíduos a fim de analisar qual o sistema ideal a implementar no sistema de picking da empresa Stellantis, Mangualde, de forma a acrescentar valor às operações realizadas e aumentar a eficiência dos seus processos. A fim de atingir o objetivo estipulado, foi definida uma metodologia que consistiu na realização de uma pesquisa descritiva para a execução de uma revisão bibliográfica, seguida de uma caracterização da empresa em questão, realização de um levantamento de problemas observados e na redação de um documento com as propostas de melhoria. Após a redação, foi realizado um estudo sobre as mesmas a fim de determinar qual das propostas é a mais indicada para o sistema de picking em questão. Consoante a análise realizada sobre os sistemas em causa, foi determinado que a implementação da conjugação dos sistemas de separação de resíduos amovível e o de aspiração centralizado é que poderá ser mais vantajoso para o sistema de picking. A conjugação dos dois sistemas contribui para a otimização das atividades realizadas no sistema de picking ao erradicar tempos de deslocação para a correta separação de resíduos durante as atividades de picking. É, também, possível otimizar o tempo de recolha de resíduos, reduzindo cerca de 53,27% do mesmo, e diminuir o número de contentores de resíduos necessários, otimizando assim o layout do sistema de picking.A picking system consists in the retrieval of pieces/ products and its transport to satisfy production or delivery orders. These systems must be qualified to face the many challenges that are imposed by the constant evolution of the production systems, that constantly seek a higher level of flexibility for their processes to manufacture a higher variety of products in a continuous way. The constant seek for improvement of the picking systems, is identified as an important process to achieve success as a company. The goal for this report was to study three residual management systems and analyse which one is the ideal for implementing in the Stellantis, Mangualde picking system, to increase value in its operations and in the efficiency of its processes. To achieve the stipulated goal, a methodology was defined that consisted in an execution of descriptive research to write bibliographic review, in a characterization of the company in question, in a withdrawal of observed problematic situations and the writing of a document that contained improvement proposals. After the writing of said document, it was executed a study about said proposals to determine which one is the most indicated for the picking system in question. According to the analysis made of the systems in question, it was determined that an implementation of a conjugation of the removable containers for correct separation of residuals system and the centralized vacuum system is the way that could bring more benefits to the picking system, contributing to the optimization of activities executed in said system. The conjugation of both systems contributes for the optimization of the activities executed in the picking system by eliminating the time of dislocations for the correct separation of residuals during picking activities. Also, it is possible to optimize the time of the retrieval of residuals, reducing 53,27% of its time and reducing the number of containers necessary, also optimizing the layout of the picking system

Optimización de Rutas basadas en Soft Computing para Movilidad Inteligente

La movilidad y transporte de pasajeros y mercancías es uno de los principales desafíos para el desarrollo de islas, ciudades y territorios. La prosperidad, competitividad y sostenibilidad de múltiples áreas económicas se ven afectadas por la movilidad. El crecimiento de la población, la capacidad limitada de los sistemas e infraestructuras de transporte y el impacto medioambiental del transporte fuerza a los territorios en el desarrollo de una movilidad sostenible y efectiva. En este complejo escenario, un territorio con una gestión del transporte y movilidad sostenible y eficiente ofrece a los ciudadanos una mejor calidad de vida. La transformación digital y las TIC impulsan la mejora de los servicios de movilidad para los ciudadanos, ayudan a gestionar correctamente la demanda en las redes de transporte y generan valor económico y ambiental. El surgimiento de la movilidad inteligente integra el sistema de transporte, las infraestructuras y las tecnologías para hacer que el transporte de pasajeros y mercancías sea eficiente, accesible, más seguro y limpio. Por lo tanto, las estrategias de movilidad inteligente deben ser capaces de proporcionar beneficios económicos y ambientales tangibles y mejorar la calidad del transporte de mercancías y pasajeros. Significa tomar acciones en múltiples frentes; gestión eficiente de la carga y la movilidad de pasajeros, reducción del impacto medioambiental, mejora de la planificación y la eficiencia del transporte público, reducción de la congestión, optimización del uso de la infraestructura física, entre otros. Una de las operaciones clave para los servicios de movilidad es la planificación de rutas. Esta actividad operativa incluye principalmente dos modos de transporte, mercancías y pasajeros. La mayoría de los transportes de mercancías y pasajeros se realizan a través de transporte por carretera. Las decisiones tomadas con respecto a las operaciones de planificación de rutas afectan económica y ambientalmente, y en general a la calidad de vida de los ciudadanos en los territorios en los que se desarrollan. Las operaciones de planificación de rutas se pueden optimizar para mejorar diferentes aspectos como la calidad del servicio, costes y flexibilidad del mismo, consumo de energía, impacto medioambiental, sostenibilidad, entre otros. La tarea de abordar las operaciones de planificación de rutas da lugar a la aparición de complejos problemas de optimización combinatoria que requieren considerar múltiples requisitos, restricciones, fuentes de información, entre otros. En la mayoría de los casos, estos problemas de optimización se clasifican como NP-duros con respecto a su complejidad computacional. Esta clase de problemas requiere enfoques de optimización eficientes y estrategias inteligentes para obtener soluciones de alta calidad y evitar grandes tiempos de cálculo. En este sentido, los enfoques de optimización aproximados, como las heurísticas y metaheurísticas, y las técnicas inteligentes inherentes a la Inteligencia Artificial y la Soft Computing han demostrado ser métodos efectivos y eficientes para resolver complejos problemas de planificación de rutas. Esta tesis presentada en la modalidad de compendio de publicaciones tiene como objetivo diseñar, implementar y validar procedimientos de optimización simples, eficientes y flexibles basados ​​en Inteligencia Artificial y Soft Computing dedicados a mejorar las soluciones de planificación de rutas en los contextos de transporte de mercancías, planificación personalizada de rutas turísticas y transporte eco-eficiente de residuos reciclables. Se han propuesto varios enfoques de solución para resolver problemas como Vehicle Routing Problem with Time Windows, Periodic Vehicle Routing Problem with Time Windows, Team Orienteering Problem with Time Windows, Tourist Trip Design Problem y variantes del mundo real y nuevas extensiones de los problemas mencionados. La calidad del servicio, la orientación al cliente, la imprecisión e incertidumbre en la información y la ecoeficiencia son criterios considerados en los problemas de planificación de rutas identificados. Los experimentos computacionales han demostrado que los métodos y técnicas propuestos son adecuados para obtener soluciones de alta calidad en tiempos computacionales cortos y pueden incorporarse como módulos en sistemas de transporte inteligentes