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Intelligent Machine Tools and Manufacturing Technology

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A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 8445

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Special Issue Editor

Prof. Dr. Jürgen Fleischer

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Guest Editor

Wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
Interests: machine technology; process automation; agile production systems; intelligent machines and components; remanufacturing; retrofit

Special Issue Information

Dear Colleagues,

In a world where digitalization and automation continue to advance, we are witnessing a revolutionary transformation of production technology. The interconnectivity of production in combination with the use of suitable applications takes production to new levels of efficiency, flexibility and intelligence.

Focus is currently on intelligent machines, which gain new capabilities through the integration of sensors, actuators and applications. Brownfield facilities can also be integrated into modern production environments via retrofitting by upgrading them with intelligent components. However, intelligence is no longer limited to individual machines. Comprehensive networking enables entirely new production concepts that are characterized by high flexibility and adaptability.

Despite significant progress in the field of intelligent machines and manufacturing technologies, numerous further innovations are needed to prepare production technology for an increasingly dynamic environment.

In this Special Issue, we therefore call on researchers, engineers and experts to share the latest advancements in intelligent machine tools and manufacturing technology. We welcome contributions related to innovative applications, intelligent machines and components, adaptive production concepts and approaches for optimizing manufacturing technologies.

Prof. Dr. Jürgen Fleischer
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

intelligent machines
intelligent components
interconnectivity
digitalization
machine learning
artificial intelligence
smart services
retrofit
agile production concepts
industry 4.0

Published Papers (7 papers)

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Research

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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

Viewed by 867

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 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

Viewed by 896

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 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 1174

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 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 3 | Viewed by 1116

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 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 1 | Viewed by 1068

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 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 1 | Viewed by 1216

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 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 855

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 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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