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24 pages, 2535 KiB

Open AccessArticle

Towards a Framework for the Industrial Recommissioning of Residual Energy (IRRE): How to Systematically Evaluate and Reclaim Waste Energy in Manufacturing

by Jannis Eckhoff, Vincent Adomat, Christian Kober, Marc Fette, Robert Weidner and Jens P. Wulfsberg

Machines 2024, 12(9), 594; https://doi.org/10.3390/machines12090594 - 27 Aug 2024

Viewed by 1187

Abstract

The extensive body of research dedicated to optimizing energy consumption and efficiency in the manufacturing sector demonstrates a significant and well-established legacy. Despite a peak of publications in this field over recent years, the subject of reusing residual energy is only infrequently discussed. [...] Read more.

The extensive body of research dedicated to optimizing energy consumption and efficiency in the manufacturing sector demonstrates a significant and well-established legacy. Despite a peak of publications in this field over recent years, the subject of reusing residual energy is only infrequently discussed. Where authors target this topic, research is often exclusively directed towards specialized applications or industries. In this article, an initial attempt of approaching residual energy reclamation in industrial manufacturing in a structured and universal manner is made. By employing a systematic literature review and design science research, a universal tool chain for decomposing individual industrial manufacturing systems to successfully reclaim and reintegrate residual energy is developed. A comprehensive overview of technologies available for energy conversion in industrial scenarios and their corresponding efficiency ranges are presented in the form of a table, called the energy conversion overview (ECO) table. The main contribution poses a multistep sequential framework guiding through identifying, assessing, harnessing, reusing, and validating residual energy in manufacturing systems. As a universal tool, the Industrial Recommissioning of Residual Energy (IRRE) framework is empowering its adopters to systematically approach residual energy recovery in their individual context by a universal tool. The application of both tools is showcased in a case study from the large-aircraft carbon fiber manufacturing industry. Full article

(This article belongs to the Special Issue Intelligent Machine Tools and Manufacturing Technology)

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13 pages, 3858 KiB

Open AccessArticle

Design of a Flexible Skill-Based Process Control System Considering Process-Relevant Properties

by Aleksandra Müller, Steffen Wurm, Phil Willecke, Oliver Petrovic, Werner Herfs and Christian Brecher

Machines 2024, 12(2), 142; https://doi.org/10.3390/machines12020142 - 17 Feb 2024

Cited by 1 | Viewed by 1302

Abstract

The Industry 4.0 research initiative strives to facilitate globally interconnected, flexible, and highly adaptable production systems. The use of skill-based control mechanisms such as OPC UA skills offers the prospect of a straightforward and flexible interchange, as well as the seamless integration of [...] Read more.

The Industry 4.0 research initiative strives to facilitate globally interconnected, flexible, and highly adaptable production systems. The use of skill-based control mechanisms such as OPC UA skills offers the prospect of a straightforward and flexible interchange, as well as the seamless integration of individual participants and processes through standardized interfaces. Furthermore, by enhancing these skills with evaluation parameters pertinent to the processes, such as CO₂ equivalents or the duration of specific skill executions, a foundation is laid for creating a customizable and adaptable composition of processes based on specific production process needs. In this article, the OPC UA skill concept is expanded with process-relevant properties, and a structured procedure for the introduction of skill-based process control is presented. The developed concept was implemented and tested on an industrial use case of glass pane completion. The aim of this publication is to demonstrate the potential of skill-based process control that has an integrated assessment of skills. Full article

(This article belongs to the Special Issue Intelligent Machine Tools and Manufacturing Technology)

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14 pages, 2388 KiB

Open AccessArticle

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

by Florian Oexle, Fabian Heimberger, Alexander Puchta and Jürgen Fleischer

Machines 2024, 12(2), 123; https://doi.org/10.3390/machines12020123 - 9 Feb 2024

Cited by 1 | Viewed by 1466

Abstract

The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine [...] Read more.

The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached. Full article

(This article belongs to the Special Issue Intelligent Machine Tools and Manufacturing Technology)

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28 pages, 12645 KiB

Open AccessArticle

Analytic and Data-Driven Force Prediction for Vacuum-Based Granular Grippers

by Christian Wacker, Niklas Dierks, Arno Kwade and Klaus Dröder

Machines 2024, 12(1), 57; https://doi.org/10.3390/machines12010057 - 12 Jan 2024

Cited by 1 | Viewed by 1795

Abstract

As manufacturing and assembly processes continue to require more adaptable systems for automated handling, innovative solutions for universal gripping are emerging. These grasping systems can enable the handling of wide varieties of shapes, with gripping forces varying with grasped geometries. For the efficient [...] Read more.

As manufacturing and assembly processes continue to require more adaptable systems for automated handling, innovative solutions for universal gripping are emerging. These grasping systems can enable the handling of wide varieties of shapes, with gripping forces varying with grasped geometries. For the efficient usage of handling systems, precise offline and online prediction models for resulting grasping forces for different objects are necessary. In previous research, a flexible vacuum-based granular gripper was developed, for which no option for predicting gripping forces is currently available. Various gripping force prediction methodologies within the current state of the art are examined and evaluated. For an assessment of grasping forces of previously untested objects for the examined gripper with limited data and low computational effort, two methodologies are proposed. An analytical, 2D-geometry-derived gripper-specific metric for geometries is compared to a methodology based on similarities of objects to a small existing dataset. The applicability and prediction quality for different object types is analyzed through validation experiments. Gripping force estimations are possible with both methodologies, with individual weaknesses towards geometric features such as air permeabilities. With further development, robust predictions of gripping forces could be achieved for a wide range of unknown object geometries with limited experimental effort. Full article

(This article belongs to the Special Issue Intelligent Machine Tools and Manufacturing Technology)

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16 pages, 38420 KiB

Open AccessArticle

Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models

by Robin Ströbel, Yannik Probst, Samuel Deucker and Jürgen Fleischer

Machines 2023, 11(11), 1015; https://doi.org/10.3390/machines11111015 - 8 Nov 2023

Cited by 6 | Viewed by 1929

Abstract

The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a [...] Read more.

The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically. Full article

(This article belongs to the Special Issue Intelligent Machine Tools and Manufacturing Technology)

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20 pages, 4839 KiB

Open AccessArticle

Precision Face Milling of Maraging Steel 350: An Experimental Investigation and Optimization Using Different Machine Learning Techniques

by Adel T. Abbas, Mohamed O. Helmy, Abdulhamid A. Al-Abduljabbar, Mahmoud S. Soliman, Ali S. Hasan and Ahmed Elkaseer

Machines 2023, 11(11), 1001; https://doi.org/10.3390/machines11111001 - 30 Oct 2023

Cited by 3 | Viewed by 1542

Abstract

Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively [...] Read more.

Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively examines the impacts of face milling variables on maraging steel’s surface quality, cutting temperature, energy consumption, and material removal rate (MRR). An experimental analysis was conducted, and the gathered data were utilized for training and testing five machine learning (ML) models: support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), random forest, and XGBoost. Each model aimed to predict the outcomes of different machining parameters efficiently. XGBoost emerged as the most effective, delivering an impressive 98% prediction accuracy across small datasets. The study extended into applying a genetic algorithm (GA) for optimizing XGBoost’s hyperparameters, further enhancing the model’s predictive accuracy. The GA was instrumental in multi-objective optimization, considering various responses, including surface roughness and energy consumption. The optimization process evaluated different weighting methods, including equal weights and weights derived from the analytic hierarchy process (AHP) based on expert insights. The findings indicate that the refined XGBoost model, augmented by GA-optimized hyperparameters, provides highly accurate predictions for machining parameters. This outcome holds significant implications for industries engaged in the machining of maraging steel, offering a pathway to optimized operational efficiency, reduced costs, and enhanced product quality amid the material’s machining challenges. Full article

(This article belongs to the Special Issue Intelligent Machine Tools and Manufacturing Technology)

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25 pages, 8663 KiB

Open AccessArticle

Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life

by Shulian Xie, Feng Xue, Weimin Zhang and Jiawei Zhu

Machines 2023, 11(10), 923; https://doi.org/10.3390/machines11100923 - 25 Sep 2023

Cited by 3 | Viewed by 1907

Abstract

As the reliability, availability, maintainability, and safety of industrial equipment have become crucial in the context of intelligent manufacturing, there are increasing expectations and requirements for maintenance policies. Compared with traditional methods, data-driven Predictive Maintenance (PdM), a superior approach to equipment and system [...] Read more.

As the reliability, availability, maintainability, and safety of industrial equipment have become crucial in the context of intelligent manufacturing, there are increasing expectations and requirements for maintenance policies. Compared with traditional methods, data-driven Predictive Maintenance (PdM), a superior approach to equipment and system maintenance, has been paid considerable attention by scholars in this field due to its high applicability and accuracy with a highly reliable quantization basis provided by big data. However, current data-driven methods typically provide only point estimates of the state rather than quantification of uncertainty, impeding effective maintenance decision-making. In addition, few studies have conducted further research on maintenance decision-making based on state predictions to achieve the full functionality of PdM. A PdM policy is proposed in this work to obtain the continuous probability distribution of system states dynamically and make maintenance decisions. The policy utilizes the Long Short-Term Memory (LSTM) network and Kernel Density Estimation with a Single Globally-optimized Bandwidth (KDE-SGB) method to dynamic predicting of the continuous probability distribution of the Remaining Useful Life (RUL). A comprehensive optimization target is introduced to establish the maintenance decision-making approach acquiring recommended maintenance time. Finally, the proposed policy is validated through a bearing case study, indicating that it allows for obtaining the continuous probability distribution of RUL centralized over a range of ±10 sampling cycles. In comparison to the other two policies, it could reduce the maintenance costs by 24.49~70.02%, raise the availability by 0.46~1.90%, heighten the reliability by 0.00~27.50%, and promote more stable performance with various maintenance cost and duration. The policy has offered a new approach without priori hypotheses for RUL prediction and its uncertainty quantification and provided a reference for constructing a complete PdM policy integrating RUL prediction with maintenance decision-making. Full article

(This article belongs to the Special Issue Intelligent Machine Tools and Manufacturing Technology)

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Review

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27 pages, 3767 KiB

Open AccessReview

Overview of Technology and Functionality Standards for Industry 4.0 and Digitalization in Mechanical Engineering

by Matthias Staiger and Tobias Voigt

Machines 2024, 12(4), 242; https://doi.org/10.3390/machines12040242 - 7 Apr 2024

Viewed by 1864

Abstract

This paper concerns the identification of current communication technologies and software functionalities from the field of digitalization and Industry 4.0, from the point of view of benefits and applicability for the machine manufacturer. To identify the relevant technologies and functionalities, research was carried [...] Read more.

This paper concerns the identification of current communication technologies and software functionalities from the field of digitalization and Industry 4.0, from the point of view of benefits and applicability for the machine manufacturer. To identify the relevant technologies and functionalities, research was carried out in both the descriptive and statistical literature. As a result, both technologies that are currently the most widely distributed, as well as eleven IT functionalities relevant to mechanical engineers, were identified and described in terms of their application and implementation. Furthermore, a knowledge gap was identified in the area of industry transfer in the field of Industry 4.0/digitalization. Full article

(This article belongs to the Special Issue Intelligent Machine Tools and Manufacturing Technology)

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