Special Issue "Smart Manufacturing Processes in the Context of Industry 4.0"

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: closed (30 July 2018).

Special Issue Editor

Guest Editor
Prof. Dr. George-Christopher Vosniakos

Manufacturing Technology Section, School of Mechanical Engineering, National Technical University of Athens, Heroon Polytehneiou 9, 15780 Athens, Greece
Website | E-Mail
Interests: intelligence in Industry 4.0, 3D printer design and control, CNC machine tools, robotics in manufacturing, optimal process planning, VR in manufacturing

Special Issue Information

Dear Colleagues,

Industry 4.0 is one of the most talked-about topics in building the factory of the future, largely involving digitalization of industry towards supporting a cross-functional global value chain, shared by many companies from many countries. Manufacturing processes are at the heart of all industrial activity. Their digitalization employs a synergetic combination of major innovations, currently being at different stages of maturity, e.g. sensors, Internet of Things (IoT), Virtual Reality, Artificial Intelligence, Big data analytics in real time, Cloud computing, etc. Most known paradigms in Industry 4.0 refer to the support of manufacturing operations and so-called intra-logistics in production and distribution sites, rather than manufacturing processes as such. In this light, there is a strong need for defining, demonstrating and establishing Industry 4.0 paradigms in the focused context of manufacturing processes: material removal, forming, casting, additive manufacturing, laser-based, etc. This Special Issue is seeking paradigms and applications of pertinent systems including cyber-physical systems. These are expected to fuse together, amongst others, some of the following: traditional process mechanics models, control models, process monitoring schemes, real-time quality monitoring, real-time data and intelligent information processing methods, information conveying interfaces, cloud computing, etc. Industrial case studies, pilot projects and demonstrators are particularly welcome and so are position papers on concepts concerning so-called ‘smart’ and ‘digital’ factories focusing on the manufacturing process as such. Topics include, but are not limited to, the following:

  • Data and information-driven schemes and their real-time implementation for control of manufacturing processes, both stand-alone and chains thereof.
  • Sensor-based data collection for monitoring manufacturing processes as well as the pertinent equipment, such as machine tools, tools, jigs and fixtures, etc.
  • Intelligent algorithms, tools and IT infrastructure, for assessing manufacturing process and product quality in real time, including computer vision.
  • Self-correction of manufacturing process setup in real-time based on models, sensors and/or large data.
  • Virtual and Augmented Reality for designing, planning, monitoring or supporting the execution of manufacturing processes.
  • Sensor and software-based real-time reconfiguration of machine tools.
  • Smart machine tools, with internet-enhanced functionality, including 3D printers, hybrid material addition-removal machines and photonics-based machines.

Prof. Dr. George-Christopher Vosniakos
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 papers will be 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. Journal of Manufacturing and Materials Processing is an international peer-reviewed open access quarterly 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 1000 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

  • Industry 4.0
  • Manufacturing processes
  • Intelligent Manufacturing
  • Cyber-physical systems
  • Smart controllers
  • Smart machine tools
  • Reconfigurability
  • Condition Monitoring
  • Process Monitoring
  • Preventive maintenance
  • Manufacturing Execution System
  • Sensors
  • Big data
  • Real time analytics
  • Internet of things
  • Virtual Reality
  • Decision systems

Published Papers (9 papers)

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Research

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Open AccessArticle
A Systems Dynamics Enabled Real-Time Efficiency for Fuel Cell Data-Driven Remanufacturing
J. Manuf. Mater. Process. 2018, 2(4), 77; https://doi.org/10.3390/jmmp2040077
Received: 31 August 2018 / Revised: 2 November 2018 / Accepted: 2 November 2018 / Published: 6 November 2018
Cited by 1 | PDF Full-text (1560 KB) | HTML Full-text | XML Full-text
Abstract
Remanufacturing is a viable option to extend the useful life of an end-of-use product or its parts, ensuring sustainable competitive advantages under the current global economic climate. Challenges typical to remanufacturing still persist, despite its many benefits. According to the European Remanufacturing Network, [...] Read more.
Remanufacturing is a viable option to extend the useful life of an end-of-use product or its parts, ensuring sustainable competitive advantages under the current global economic climate. Challenges typical to remanufacturing still persist, despite its many benefits. According to the European Remanufacturing Network, a key challenge is the lack of accurate, timely and consistent product knowledge as highlighted in a 2015 survey of 188 European remanufacturers. With more data being produced by electric and hybrid vehicles, this adds to the information complexity challenge already experienced in remanufacturing. Therefore, it is difficult to implement real-time and accurate remanufacturing for the shop floor; there are no papers that focus on this within an electric and hybrid vehicle environment. To address this problem, this paper attempts to: (1) identify the required parameters/variables needed for fuel cell remanufacturing by means of interviews; (2) rank the variables by Pareto analysis; (3) develop a casual loop diagram for the identified parameters/variables to visualise their impact on remanufacturing; and (4) model a simple stock and flow diagram to simulate and understand data and information-driven schemes in remanufacturing. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Open AccessArticle
Online Tool Wear Classification during Dry Machining Using Real Time Cutting Force Measurements and a CNN Approach
J. Manuf. Mater. Process. 2018, 2(4), 72; https://doi.org/10.3390/jmmp2040072
Received: 30 August 2018 / Revised: 5 October 2018 / Accepted: 10 October 2018 / Published: 18 October 2018
Cited by 2 | PDF Full-text (2827 KB) | HTML Full-text | XML Full-text
Abstract
The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as [...] Read more.
The new generation of ICT solutions applied to the monitoring, adaptation, simulation and optimisation of factories are key enabling technologies for a new level of manufacturing capability and adaptability in the context of Industry 4.0. Given the advances in sensor technologies, factories, as well as machine tools can now be sensorised, and the vast amount of data generated can be exploited by intelligent information processing techniques such as machine learning. This paper presents an online tool wear classification system built in terms of a monitoring infrastructure, dedicated to perform dry milling on steel while capturing force signals, and a computing architecture, assembled for the assessment of the flank wear based on deep learning. In particular, this approach demonstrates that a big data analytics method for classification applied to large volumes of continuously-acquired force signals generated at high speed during milling responds sufficiently well when used as an indicator of the different stages of tool wear. This research presents the design, development and deployment of the system components and an overall evaluation that involves machining experiments, data collection, training and validation, which, as a whole, has shown an accuracy of 78%. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Open AccessArticle
Selection of Machining Parameters Using a Correlative Study of Cutting Tool Wear in High-Speed Turning of AISI 1045 Steel
J. Manuf. Mater. Process. 2018, 2(4), 66; https://doi.org/10.3390/jmmp2040066
Received: 31 July 2018 / Revised: 4 October 2018 / Accepted: 7 October 2018 / Published: 10 October 2018
Cited by 1 | PDF Full-text (3155 KB) | HTML Full-text | XML Full-text
Abstract
The manufacturing industry aims to produce many high quality products efficiently at low cost, thereby motivating companies to use advanced manufacturing technologies. The use of high-speed machining is increasingly widespread; however, it lacks a deep-rooted knowledge base needed to facilitate implementation. In this [...] Read more.
The manufacturing industry aims to produce many high quality products efficiently at low cost, thereby motivating companies to use advanced manufacturing technologies. The use of high-speed machining is increasingly widespread; however, it lacks a deep-rooted knowledge base needed to facilitate implementation. In this paper, response surface methodology (RSM) has been applied to determine the optimum cutting conditions leading to minimum flank wear in high-speed dry turning on AISI 1045 steel. The mathematical models in terms of machining parameters were developed for flank wear prediction using RSM on the basis of experimental results. The high speed turning experiments were carried out with two coated carbide and a cermet inserts using AISI 1045 steel as work material at different cutting speeds and machining times. The models selected for optimization were validated through the Pareto principle. Results showed the GC4215 insert to be the most optimal option, because it did not reach the cutting tool life limit and could be used for the whole range of cutting parameters selected. To quantitatively evaluate the usefulness of the cutting tools, it was proposed the coefficient of use of the tools from the results of the contour graphs. The GC4215 insert showed 100% effectiveness, followed by the GC4225 with 98.4%, and finally, the CT5015 insert with 83%. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Open AccessArticle
Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means
J. Manuf. Mater. Process. 2018, 2(3), 60; https://doi.org/10.3390/jmmp2030060
Received: 30 July 2018 / Revised: 29 August 2018 / Accepted: 31 August 2018 / Published: 4 September 2018
Cited by 1 | PDF Full-text (3356 KB) | HTML Full-text | XML Full-text
Abstract
Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how [...] Read more.
Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Open AccessArticle
Octree-Based Generation and Variation Analysis of Skin Model Shapes
J. Manuf. Mater. Process. 2018, 2(3), 52; https://doi.org/10.3390/jmmp2030052
Received: 8 July 2018 / Revised: 1 August 2018 / Accepted: 8 August 2018 / Published: 12 August 2018
Cited by 1 | PDF Full-text (8275 KB) | HTML Full-text | XML Full-text
Abstract
The concept of Skin Model Shape has been introduced as a method for a close representation of manufactured parts using a discrete geometry representation scheme. However, discretized surfaces make irregular polyhedra, which are computationally demanding to model and process using the traditional implicit [...] Read more.
The concept of Skin Model Shape has been introduced as a method for a close representation of manufactured parts using a discrete geometry representation scheme. However, discretized surfaces make irregular polyhedra, which are computationally demanding to model and process using the traditional implicit surface and boundary representation techniques. Moreover, there are still some research challenges related to the geometrical variation modelling of manufactured products; specifically, methods for geometrical data processing, the mapping of manufacturing variation sources to a geometric model, and the improvement of variation visualization techniques. To provide steps towards addressing these challenges this work uses Octree, a 3D space partitioning technique, as an aid for geometrical data processing, variation visualization, variation modelling and propagation, and tolerance analysis. Further, Skin Model Shapes are generated either by manufacturing a simulation using a non-ideal toolpath on solid models of Skin Model Shapes that are assembled to non-ideal fixtures or from measurement data. Octrees are then used in a variation envelope extraction from the simulated or measurement data, which becomes a basis for further simulation and tolerance analysis. To illustrate the method, an industrial two-stage truck component manufacturing line was studied. Simulation results show that the predicted Skin Model Shapes closely match to the measurement data from the manufacturing line, which could also be used to map to manufacturing error sources. This approach contributes towards the application of Octrees in many Skin Model Shape related operations and processes. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Open AccessArticle
Towards Sustainable Machining of Inconel 718 Using Nano-Fluid Minimum Quantity Lubrication
J. Manuf. Mater. Process. 2018, 2(3), 50; https://doi.org/10.3390/jmmp2030050
Received: 9 July 2018 / Revised: 24 July 2018 / Accepted: 30 July 2018 / Published: 2 August 2018
Cited by 9 | PDF Full-text (1129 KB) | HTML Full-text | XML Full-text
Abstract
Difficult-to-cut materials have been widely employed in many engineering applications, including automotive and aeronautical designs because of their effective properties. However, other characteristics; for example, high hardness and low thermal conductivity has negatively affected the induced surface quality and tool life, and consequently [...] Read more.
Difficult-to-cut materials have been widely employed in many engineering applications, including automotive and aeronautical designs because of their effective properties. However, other characteristics; for example, high hardness and low thermal conductivity has negatively affected the induced surface quality and tool life, and consequently the overall machinability of such materials. Inconel 718, is widely used in many industries including aerospace; however, the high temperature generated during machining is negatively affecting its machinability. Flood cooling is a commonly used remedy to improve machinability problems; however, government regulation has called for further alternatives to reduce the environmental and health impacts of flood cooling. This work aimed to investigate the influence of dispersed multi-wall carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3) gamma nanoparticles, on enhancing the minimum quantity lubrication (MQL) technique cooling and lubrication capabilities during turning of Inconel 718. Machining tests were conducted, the generated surfaces were examined, and the energy consumption data were recorded. The study was conducted under different design variables including cutting speed, percentage of added nano-additives (wt.%), and feed velocity. The study revealed that the nano-fluids usage, generally improved the machining performance when cutting Inconel 718. In addition, it was shown that the nanotubes additives provided better improvements than Al2O3 nanoparticles. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Open AccessArticle
Towards a Generic Framework for the Performance Evaluation of Manufacturing Strategy: An Innovative Approach
J. Manuf. Mater. Process. 2018, 2(2), 23; https://doi.org/10.3390/jmmp2020023
Received: 25 February 2018 / Revised: 16 March 2018 / Accepted: 23 March 2018 / Published: 29 March 2018
Cited by 1 | PDF Full-text (6994 KB) | HTML Full-text | XML Full-text
Abstract
To be competitive in a manufacturing environment by providing optimal performance in terms of cost-effectiveness and swiftness of system changes, there is a need for flexible production systems based on a well-defined strategy. Companies are steadily looking for methodology to evaluate, improve and [...] Read more.
To be competitive in a manufacturing environment by providing optimal performance in terms of cost-effectiveness and swiftness of system changes, there is a need for flexible production systems based on a well-defined strategy. Companies are steadily looking for methodology to evaluate, improve and update the performance of manufacturing systems for processing operations. Implementation of an adequate strategy for these systems’ flexibility requires a deep understanding of the intricate interactions between the machining process parameters and the manufacturing system’s operational parameters. This paper proposes a framework/generic model for one of the most common metal cutting operations—the boring process of an engine block machining system. A system dynamics modelling approach is presented for modelling the structure of machining system parameters of the boring process, key performance parameters and their intrinsic relationships. The model is based on a case study performed in a company manufacturing engine blocks for heavy vehicles. The approach could allow for performance evaluation of an engine block manufacturing system condition. The presented model enables a basis for other similar processes and industries producing discrete parts. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Open AccessArticle
Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills
J. Manuf. Mater. Process. 2018, 2(1), 16; https://doi.org/10.3390/jmmp2010016
Received: 6 February 2018 / Revised: 26 February 2018 / Accepted: 27 February 2018 / Published: 2 March 2018
Cited by 1 | PDF Full-text (9416 KB) | HTML Full-text | XML Full-text
Abstract
Due to their high ductility, high durability, and excellent corrosion resistance, stainless steels are attractive materials for a variety of applications. However, high work hardening, low thermal conductivity, and high built-up edge (BUE) formation make these materials difficult to machine. Rapid tool wear [...] Read more.
Due to their high ductility, high durability, and excellent corrosion resistance, stainless steels are attractive materials for a variety of applications. However, high work hardening, low thermal conductivity, and high built-up edge (BUE) formation make these materials difficult to machine. Rapid tool wear and high cutting forces are the common problems encountered while machining these materials. In the present work, the application of Taguchi optimization methodology has been used to optimize the cutting parameters of the drilling process for machining two stainless steels: austenitic AISI 304 and duplex AISI 2205 under dry conditions. The machining parameters which were chosen to be evaluated in this study are the tool material, cutting speed, and feed rate, while, the response factors to be measured are the tool life (T), cutting force (Fc), and specific cutting energy (ks). Additionally, empirical models were created for predicting the T, Fc and ks using linear regression analysis. The results of this study show that AISI 2205 stainless steel has a shorter tool life, a higher cutting force, and a higher specific cutting energy than AISI 304 stainless steel. In addition, the Taguchi method determined that A3B1C1 and A3B3C1 (A3 = TiN-coated twist drill, B1 = 13 m/min, B3 = 34 m/min, C1 = 0.12 mm/rev) are the optimized combination of levels for the best tool life and the lowest cutting force, respectively. Meanwhile, the optimized combination of levels for all three control factors from the analysis, which provides the lowest specific cutting energy, was found to be A3B1C3 (A3 = TiN-coated twist drill, B1 = 13 m/min, C3 = 0.32 mm/rev) for both stainless steels. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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Review

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Open AccessReview
Fractal Analysis Application Outlook for Improving Process Monitoring and Machine Maintenance in Manufacturing 4.0
J. Manuf. Mater. Process. 2018, 2(3), 62; https://doi.org/10.3390/jmmp2030062
Received: 28 July 2018 / Revised: 7 September 2018 / Accepted: 8 September 2018 / Published: 10 September 2018
Cited by 1 | PDF Full-text (524 KB) | HTML Full-text | XML Full-text
Abstract
Industry 4.0 has been advertised for a decade as the next disruptive evolution for production. It relies on automation growth and particularly on data exchange using numerous sensors in order to develop faster production with tight monitoring. The huge amount of data generated [...] Read more.
Industry 4.0 has been advertised for a decade as the next disruptive evolution for production. It relies on automation growth and particularly on data exchange using numerous sensors in order to develop faster production with tight monitoring. The huge amount of data generated by clouds of sensors during production is often used to feed machine learning systems in order to detect faults, monitor and find possible ways for improvement. However, the artificial intelligence within machine learning requires finding and selecting key features, such as average and root mean square. While current machine learning has already proven its use in diverse applications, its efficiency could be further improved by generating better characteristics such as fractal parameters. In this paper, fractal analysis concept is presented and its current and future applications in machining are discussed. This sensitive and robust technique is already extracting high performance key features that could fill in monitoring and prediction systems. On top of improving features selection and, thus, improving the overall performance of monitoring and predictive systems in machining, this could lead to a more rapid artificial intelligence implementation into manufacturing. Full article
(This article belongs to the Special Issue Smart Manufacturing Processes in the Context of Industry 4.0)
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