Multi-source data analytics for AM energy consumption prediction

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

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

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science