Multi-source data analytics for AM energy consumption prediction

Qin, Jian; Liu, Ying; Grosvenor, Roger Ivor

doi:10.1016/j.aei.2018.10.008

Cited by 40 publications

(24 citation statements)

References 38 publications

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“…The employment of Industrial AI towards process optimization in manufacturing is gaining rapid traction, enabling smarter, more efficient data-driven decision-making by leveraging both historical and real-time data. In this regard, the main emphasis has been put into energy consumption prediction and optimization problems [41], production efficiency [71] and demand forecasting [24]. Thus, the application of Industrial AI for process optimization can contribute to make For data-driven applications, real-time capability is crucial to turn new AI predictions and insights into actionable knowledge at both the level of the processes and of the overall smart factory operations in a timely manner, adequate for the increasingly real-time economy.…”

Section: ) Process Optimizationmentioning

confidence: 99%

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

et al. 2020

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Section: ) Process Optimizationmentioning

confidence: 99%

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

et al. 2020

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“…In our previous work (Qin et al, 2018), an AM energy consumption modelling approach was proposed, in which multi-source data, such as working environmental data, process operation data, design-relevant data, and material condition data, was sensed and collected. The resulting dataset contained 12 variables, including the average height of a single part, the total height of the build, the filling degree, and the number of parts built in each individual process.…”

Section: Design-relevant Impacts On Am Energy Consumptionmentioning

confidence: 99%

“…It is noticed that a different system may involve different attributes. More details of multi-source data analytics for AM energy consumption has been introduced and discussed in our previous work (Qin et al, 2018). The first deep learning-based prediction model * is built using the design-relevant data and historical process data ( ), shown as follows:…”

Section: Am Energy Consumption Modelling Based On the Design-relevantmentioning

confidence: 99%

“…Due to these two datasets are classified into two levelled datasets (Qin et al, 2018), the merged neural network * is used for integrating the predicted process data and design-relevant data and predicting the unit energy consumption ( ), which is donated as equation 2. The details of the merged neural network ( * ) are explained in the previous paper (Qin et al, 2018). This energy consumption modelling is based on deep learning techniques, and two neural networks are applied in the approach.…”

Section: Am Energy Consumption Modelling Based On the Design-relevantmentioning

confidence: 99%

“…Similar to DfMA, DfAM includes numerous design-relevant aspects, such as material design, part geometry design, and process planning, each of which involves many design-relevant features (Gibson et al, 2010). These features also play into the factors, that impact AM energy consumption, which are decided before starting the producing process by part designers and process operators (Qin et al, 2018). Modelling the design-relevant data of AM systems to predict energy consumption, and then reducing it by optimising the designs and decisions of part designers and process operators have become a crucial research topic for improving AM systems (Baumers et al, 2011b;Panda et al, 2016;Watson and Taminger, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation

Qin

Liu

Grosvenor

et al. 2020

Journal of Cleaner Production

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The additive manufacturing (AM) process is characterised as a high energy-consuming process, which has a significant impact on the environment and sustainability. The topic of AM energy consumption modelling, prediction, and optimisation has then become a research focus in both industry and academia. This issue involves many relevant features, such as material condition, process operation, part and process design, working environment, and so on. 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 study, design-relevant features are firstly examined with respect to energy modelling. These features are typically determined by part designers and process operators before production. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction modelling through a design-relevant data analytics approach. Based on the new modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant data collected from a real-world AM system in production, a case study is presented to validate the proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been carried out to guide part designers and process operators to revise their designs and decisions in order to reduce the energy consumption of the designated AM system under study.

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A multisource data‐driven approach for carbon footprint analysis of remanufacturing systems

Yan,

He,

Zhang

2023

Energy Science & Engineering

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The remanufacturing industry is experiencing significant growth, resulting in a substantial increase in its carbon footprint. Unfortunately, this issue is not receiving adequate attention at present. Moreover, existing methods for analyzing carbon footprints in remanufacturing systems are inefficient and inaccurate due to the complex relationship between carbon emissions and the dynamic nature of the systems. In this paper, we propose a multisource data‐driven approach for carbon footprint analysis of remanufacturing systems. First, we examine the relationship between carbon emissions and the uncertainty surrounding end‐of‐life (EOL) product components, as well as the corresponding resource recovery strategies, such as reuse, recycle, and remanufacture. This analysis allows us to define the carbon footprint boundary of remanufacturing systems. Next, we establish a carbon footprint model for recycling strategies using recycling data. Additionally, we propose a carbon footprint model for remanufacture strategies by utilizing the back propagation neural network based on particle swarm optimization algorithm and data from the materials flow, energy flow, and waste flow of these strategies. By combining these models, we can accurately calculate the carbon footprint of remanufacturing systems. To demonstrate the effectiveness of our approach, we present a case study on the remanufacturing of an EOL automobile engine. This case study showcases how our methodology can be utilized as a valuable tool for analyzing carbon footprints and identifying strategies to reduce carbon emissions in remanufacturing systems.

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Multi-source data analytics for AM energy consumption prediction

Cited by 40 publications

References 38 publications

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation

A multisource data‐driven approach for carbon footprint analysis of remanufacturing systems

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