Therblig-embedded value stream mapping method for lean energy machining

To improve energy efficiency, extensive studies have focused on the cutting parameters optimization in the machining process. Actually, non-cutting activities (NCA) occur frequently during machining and this is a promising way to save energy through optimizing NCA without changing the cutting parameters. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes (EW) in NCA. To fill this gap, a novel Therblig-embedded Value Stream Mapping (TVSM) method is proposed to improve the energy transparency and clearly show and reduce the EW in NCA. The Future-State-Map (FSM) of TVSM can be built by minimizing non-cutting activities and Therbligs. By implementing the FSM, time and energy efficiencies can be improved without decreasing the machining quality, which is consistent with the goal of lean energy machining. The method is validated by a machining case study, the results show that the total energy is reduced by 7.65%, and the time efficiency of the value-added activities is improved by 8.12% , and the energy efficiency of value-added activities and Therbligs are raised by 4.95% and 1.58%, respectively. This approach can be applied to reduce the EW of NCA, to support designers to design high energy efficiency machining processes during process planning

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

The 9th International Conference on Digital Enterprise Technology – Intelligent Manufacturing in the Knowledge Economy Era

Digital Enterprise Technology (DET) is “the collection of systems and methods for the digital modelling, simulation and optimization of the collaborative product development, factory and manufacturing processes planning, along their lifecycle”. The main aim of DET 2016 is to provide an international forum for the exchange of leading edge scientific knowledge and industrial experiences, regarding the development, integration and applications of the various aspects of Digital Enterprise Technologies, in the global manufafturing of the knowledge economy era. The guiding idea of the conference is to find a common understanding of employing Digital Enterprise Technologies in the factories of the future, moving from automated, to flexible, digital, sustainable, smart and intelligent manufacturing

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Digital technologies review for manufacturing processes

It is apparent the industrial processes transformations caused by industry 4.0 are in advance in some countries like China, Japan, Germany and United States. But, in return, the developing countries, as the emergent Brazil, seem like to have a long way to achieve digital era. Considering manufacturing processes as the starting point the rise of industry 4.0, this research aims to show a review about the most important technologies used in smart manufacturing, including the main challenges to implement it at Brazil. The papers were collected from Web of Science (WoS), comprising 114 articles and 2 books to underpin this study. This exploratory research resulted in the presentation of some challenges faced by Brazilian industry to join the new industrial era, such as poor technological infrastructure, besides lack of investment in technologies and training of qualified people. Even though the primary motivation of this research was to present a panorama of smart manufacturing for Brazil, this study results contributes to the most of emergent countries, bringing together general concepts and addressing practical applications developed by several researchers from the international academic community

Energy Driven Process Planning and Machine Tool Dynamic Behavior Assessment

AbstractThe current work outlines an approach to close the loop between process planning and machine tool dynamic modeling by addressing the problem of energy efficiency across the process design and realization chains, from the process settings and pallet configuration to the machine tool design and usage phases. The proposed closed loop approach consists of an off-line and on-line component enabling the process and equipment dynamic and energy assessment over time. The benefits of the approach have been evaluated against an industrial case study related to the automotive industry

Intelligent energy management using data mining techniques at Bosch Car Multimedia Portugal facilities

The fusion of emerged technologies such as Artificial Intelligence, cloud computing, big data, and the Internet of Things in manufacturing has pioneered this industry to meet the fourth stage of the industrial revolution (industry 4.0). One major approach to keeping this sector sustainable and productive is intelligent energy demand planning. Monitoring and controlling the consumption of energy under industry 4.0, directly results in minimizing the cost of operation and maximizing efficiency. To advance the research on the adoption of industry 4.0, this study examines CRISP-DM methodology to project data mining approach over data from 2020 to 2021 which was collected from industrial sensors to predict/forecast future electrical consumption at Bosch car multimedia facilities located at Braga, Portugal. Moreover, the influence of indicators such as humidity and temperature on electrical energy consumption was investigated. This study employed five promising regression algorithms and FaceBook prophet (FB prophet) to apply over data belonging to two HVAC (heating, ventilation, and air conditioning) sensors (E333, 3260). Results indicate Random Forest (RF) algorithms as a potential regression approach for prediction and the outcome of FB prophet to forecast the demand of future usage of electrical energy associated with HVAC presented. Based on that, it was concluded that predicting the usage of electrical energy for both data points requires time series techniques. Where "timestamp" was identified as the most effective feature to predict consume of electrical energy by regression technique (RF). The result of this study was integrated with Intelligent Industrial Management System (IIMS) at Bosch Portugal.- (undefined

Tool flow management in batch manufacturing systems for cylindrical components

The objective of the research is to study the design of and operating strategies for advanced tool flow systems in highly automated turning systems. A prototype workstation has been built to aid this process. The thesis consists of three main parts. In the first part the current flexible manufacturing technology is reviewed with emphasis laid on tool flow and production scheduling problems. The 'State-of-the-Art' turning systems are studied, to highlight the requirement of the computer modelling of tool flow systems. In the second part, the design of a computer model using fast modelling algorithms is reported. The model design has concentrated on the tool flow system performance forecasting and improving. Attention has been given to the full representation of highly automatic features evident in turning systems. A number of contemporary production scheduling rules have been incorporated into the computer model structure, with the objectives of providing a frontend to the tool flow model, and to examine the tool flow problems interactively with the production scheduling rules. The user-interface of the model employs conversational type screens for tool flow network specification and data handling, which enhances its user friendliness greatly. An effective, fast, and easy to handle data base management system for tool, part, machine data entries has been· built up to facilitate the model performance. The third part of the thesis is concerned with the validation and application of the model with industry supplied data to examine system performance, and to evaluate alternative strategies. Conclusions drawn from this research and the recommendations for further work are finally indicated