9 research outputs found

    Data analytics for energy consumption of digital manufacturing systems using Internet of Things method

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    The topic of ‘Industry 4.0’ has become increasingly popular in manufacturing and academia since it was first published. Under this trending topic, researchers and companies have pointed out many related capabilities required by current manufacturing systems, such as automation, interoperability, consciousness, and intelligence. To achieve these capabilities, data is considered the vitally important connecting media that integrates different manufacturing objects and activities. Additionally, sustainability is one of the most important research areas of Industry 4.0. Although modern digital manufacturing systems are becoming increasingly automated, the issue of sustainability still attracts attention, and is related to many processing factors that are present in a wide variety of systems. As a result, defining the energy consumption behaviour of digital manufacturing systems and discovering more efficient usage methods has been established as a crucial research target. In this paper, data analysis methods are proposed to facilitate better understanding and prediction of the energy consumption of digital production processes under an Internet of Things (IoT) framework. A Selective Laser Sintering (SLS) system is applied as a case study, in which a variety of real-time raw data is collected within machine logs from this ongoing Additive Manufacturing (AM) system. The machine data logs are combined with the product layout data and analysed using three data analysis techniques: linear regression, the decision tree method and the Back-propagation Neural Network method. The future work is introduced in order to complete this research

    Multi-source data analytics for AM energy consumption prediction

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    The issue of Additive Manufacturing (AM) system energy consumption attracts increasing attention when many AM systems are applied in digital manufacturing systems. Prediction and reduction of the AM energy consumption have been established as one of the most crucial research targets. However, the energy consumption is related to many attributes in different components of an AM system, which are represented as multiple source data. These multi-source data are difficult to integrate and to model for AM energy consumption due to its complexity. The purpose of this study is to establish an energy value predictive model through a data-driven approach. Owing to the fact that multi-source data of an AM system involves nested hierarchy, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering techniques and deep learning to integrate the multi-source data that is collected using the Internet of Things (IoT), and then to build the energy consumption prediction model for AM systems. This study aims to optimise the AM system by exploiting energy consumption information. An experimental study using the energy consumption data of a real AM system shows the merits of the proposed approach. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches

    Advanced data analytics for additive manufacturing energy consumption modelling, prediction, and management under Industry 4.0

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    The topic of ‘Industry 4.0’ has become increasingly important in both industry and academia since it was first published. Under this trending topic, many related capabilities required by current manufacturing systems have been pointed out in both academia and industry, such as automation, sustainability, and intelligence. Additive manufacturing (AM) is one of the most popular manufacturing systems in the era of Industry 4.0. Although the AM system tends to become increasingly automated and flexible, the issue of energy consumption still attracts attention. It is related to many attributes in different components of an AM system, which are represented as multiple source data, such as process operation data, working environment data, design-relevant data, and material condition data. How to integrate and analyse the multi-source data for AM energy modelling, prediction, and management has become a crucial research question. This research was structured according to four themes. Firstly, a categorical classification is proposed based on the research gaps between current manufacturing systems and Industry 4.0 requirement. Nine varied applications are generated relying on their classification to provide a roadmap to raise the intelligence level of manufacturing systems to achieve Industry 4.0 requirement. Inspired by this classification, a framework was designed for leading the research of AM energy consumption modelling, prediction, and management. The framework includes four layers, data sensing and collection layer, data pre-process and integration layer, data analytics layer, and knowledge and application layer. This four-layered framework covers the entire knowledge discovery process from data generation to performance presentation. Secondly, due to multi-source data of the AM systems usually involving nested hierarchies, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering and deep learning technologies to integrate the multi-source data which is collected by the Internet of Things (IoT), to model energy consumption iv for AM systems. Multi-source data is analysed and collected. The data collection methods are introduced within the validation of a selective laser sintering (SLS) system. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches. Thirdly, while existing studies reveal that AM energy consumption modelling largely depends on the design-relevant features in practice, it has not been given sufficient attention. Therefore, in this research, design-relevant features are examined with respect to energy consumption prediction based on the study of AM energy modelling. By reviewing the literature of Design for AM and analysing some representative design models, AM design patterns are obtained and listed. Two types of design-relevant features are found, part-design features and process-planning features. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction through a design-relevant data analytics approach. Finally, methods enabling energy consumption management are provided in this research, which includes framework, modelling, prediction and the optimisation. The energy consumption optimisation method is based on particle swarm optimisation (PSO), and driven by deep learning technology, named as deep learning driven particle swarm optimisation (DLD-PSO). The proposed optimisation method aims to reduce the energy utility by optimising the design-relevant features. Deep learning was introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. The approaches proposed in this research were validated with the data collected from the target AM system, and the results reveal their merits. The expected main achievement of this research is to pave the way for AM energy consumption modelling, prediction, and management through the advanced data analytics, which provides a feasibility study for achieving Industry 4.0. As such, it offers great potential as a route to achieve a more practical and generalised implementation of digital and intelligent manufacturing

    Digitization of the work environment for sustainable production

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    Global pandemics, devastating wars and natural disasters with increasing frequency and impact are disrupting previously carefully balanced manufacturing networks. All industrial companies are required to examine their operations and adjust accordingly. The increasing cost of resources require enterprises to re-design their value creation processes to be more sustainable, to optimize the supplier network to become more resilient and to accelerate digitizing of operations to enhance operational effectiveness. This year's WGAB research seminar is themed around Digitization of the work environment for sustainable production and seeks to contribute solutions to the current challenges. The scientific discourse aims to advance the sustainable and data-based organization of value creation processes. Exemplary efforts for the sustainable production of 3D printed footwear and the circular supply chain of energy production will be discussed. With advances in sensory data collection in cyber-physical production systems (CPPS), there are new opportunities for sensing the status of manufacturing systems, which enable advanced data analytics to contribute to a sustainable production. Intelligent processes enable sustainable value creation and bi-directional knowledge exchange between humans and machines. With people at the centre of the CPPS, production systems shall be both adaptive and personalized for every worker. People need to be involved in the technological and organizational changes. Simulating the migration from a linear economy to a circular economy supports the trend of regionalized production networks. Digital assistance systems are tested to back up resilient manufacturing. We would like to thank all authors for their efforts in preparing the contributions, which are valuable inputs to the discourse to solve the current challenges
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