Special Issue "Smart Manufacturing and Industry 4.0"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 10769

Special Issue Editors

Dr. Ahmad Barari
E-Mail Website
Guest Editor
Department of Automotive, Mechanical and Manufacturing Engineering, Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, ON L1H 7K4, Canada
Interests: precision manufacturing; advanced manufacturing technologies; digital manufacturing; precision manufacturing; measurement uncertainty; 3D coordinate metrology
Special Issues, Collections and Topics in MDPI journals
Dr. Marcos de Sales Guerra Tsuzuki
E-Mail Website
Guest Editor
Department of Mechatronics and Mechanical Systems Engineering, Universidade de São Paulo, São Paulo 2231, Brazil
Interests: CAD/CAM; computer graphics; Industry 4.0; cutting and packing and optimization problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart manufacturing processes and systems have been receiving great attention through the latest innovations, ongoing efforts, and best practices in the Industry 4.0 ear. The idea of the smart factory and its cyberphysical systems, intelligent support systems for manufacturing decision making, intelligent inspection to monitor the production health, in situ data collection and fusion of sensor information for manufacturing processes, collaborative robots, self-configuration and self-diagnosis Internet of Things for manufacturing shop floors, intelligent prescriptive and preventive maintenance, simulation-assisted process control and digital twins, big data analytics for manufacturing systems and processes, on-demand and customized processes utilizing the hybrid of additive and subtractive manufacturing, autonomy and autonomous vehicles, smart quality assurance and intelligent inspection, data-driven and model-based prognostics, and zero defect production are among the most important topics that need further research attention. This call aims at developing a Special Issue of the Journal of Applied Sciences dedicated to publishing new initiatives, applications, and research advances on smart manufacturing processes and systems addressing the needs of the fourth industrial revolution.

Prof. Ahmad Barari
Prof. Marcos de Sales Guerra Tsuzuki
Guest Editors

Manuscript Submission Information

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Keywords

  • Smart manufacturing
  • Intelligent manufacturing
  • Industry 4.0
  • Digital manufacturing
  • Digital metrology
  • Intelligent support systems
  • Manufacturing process control
  • Smart quality assurance
  • Intelligent inspection
  • Predictive and prescriptive maintenance
  • Model-based prognostics
  • Vision systems
  • Collaborative robots
  • Manufacturing health management
  • Artificial intelligence for manufacturing processes
  • Big data analytics
  • Sensor information
  • Digital twins
  • Manufacturing virtualization and simulation
  • Self-configuration and self-diagnosis
  • Internet of Things
  • Self-optimization models
  • Scheduling and sequencing
  • Blockchain technology
  • Resource efficiency
  • Circular economy tracking
  • Autonomy
  • Autonomous vehicles
  • Drones

Published Papers (8 papers)

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Editorial

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Editorial
Smart Manufacturing and Industry 4.0
Appl. Sci. 2023, 13(3), 1545; https://doi.org/10.3390/app13031545 - 25 Jan 2023
Viewed by 283
Abstract
Smart manufacturing processes and systems have received great attention through the latest innovations, ongoing efforts, and best practices in the Industry 4 [...] Full article
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)

Research

Jump to: Editorial

Article
Knowledge-Based Automated Mechanical Design of a Robot Manipulator
Appl. Sci. 2022, 12(12), 5897; https://doi.org/10.3390/app12125897 - 09 Jun 2022
Cited by 1 | Viewed by 762
Abstract
Design methods have been improving with an increasing level of algorithmic support for some time. The most recent advances include generative design and various optimization methods. However, the automated design tools are often focused on a single stage of the design process, for [...] Read more.
Design methods have been improving with an increasing level of algorithmic support for some time. The most recent advances include generative design and various optimization methods. However, the automated design tools are often focused on a single stage of the design process, for example, kinematics design, mechanical topology, or drive selection. In this paper, we show the whole design process of a robotic manipulator in an automated workflow. The method consisted of two main parts: a genetic optimization of the kinematic structure and an iterative automated CAD design. The method was then applied to a case study in which a manipulator with five degrees of freedom for a handling task was designed. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)
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Article
Production Scheduling Methodology, Taking into Account the Influence of the Selection of Production Resources
Appl. Sci. 2022, 12(11), 5367; https://doi.org/10.3390/app12115367 - 26 May 2022
Cited by 1 | Viewed by 620
Abstract
The overwhelming majority of methodologies for the flexible flow shop scheduling problem proposed so far have a common feature, which is the assumption of constant time and cost for the execution of individual technological operations (ignoring an optimal selecting combination of individual employees [...] Read more.
The overwhelming majority of methodologies for the flexible flow shop scheduling problem proposed so far have a common feature, which is the assumption of constant time and cost for the execution of individual technological operations (ignoring an optimal selecting combination of individual employees and tools). Even if the existence of the influence of the selection of production resources on the course of operations is signaled in the available works, the research so far has not focused on the measurable effect of such a solution that takes into account this phenomenon in scheduling. The proposed production scheduling methodology, including the influence of employees and tools, turned out to be more effective in terms of minimizing the maximum completion time and the cost of the production process compared to existing solutions. The efficiency of the new proposed scheduling methodology was assessed using examples of four technological processes. The research was carried out on the basis of a dedicated adaptation of the Monte Carlo optimization algorithm in order to determine the actual effect of the new solution. The algorithm itself is not an integral part of the proposed solution, and the universal methodology developed will ensure significant profit for any optimization algorithm correctly implemented. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)
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Article
Framework of 2D KDE and LSTM-Based Forecasting for Cost-Effective Inventory Management in Smart Manufacturing
Appl. Sci. 2022, 12(5), 2380; https://doi.org/10.3390/app12052380 - 24 Feb 2022
Cited by 2 | Viewed by 1047
Abstract
Over the last decade, the development of machine-learning models has enabled the design of sophisticated regression models. For this reason, studies have been conducted to design predictive models using machine learning in various industries. In particular, in terms of inventory management, forecasting models [...] Read more.
Over the last decade, the development of machine-learning models has enabled the design of sophisticated regression models. For this reason, studies have been conducted to design predictive models using machine learning in various industries. In particular, in terms of inventory management, forecasting models predict historical market demand, predict future demand, and enable systematic inventory management. However, in most small and medium enterprise (SMEs), there is no systematic management of data, and because of the lack of data and the volatility of random data, it is difficult for prediction models to work well. Since the predictive model is a core function derived from the management of the enterprise’s inventory data, the poor performance of the model causes the company’s inventory data-management system to be degraded. Companies that have poor inventory data because of this vicious cycle will continue to have difficulty introducing data-management systems. In this paper, we propose a framework that can reliably predict the inventory data of a firm by modeling the volatility of a firm stochastically. The framework makes the prediction using the point prediction model by means of LSTM(Long Short Term Memory), the 2D kernel density function, and the prediction result reflecting inventory-management cost. Through various experiments, the necessity of interval prediction in demand prediction and the validity of the cost-effective prediction model through the readjustment function were shown. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)
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Article
Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
Appl. Sci. 2021, 11(24), 11814; https://doi.org/10.3390/app112411814 - 13 Dec 2021
Cited by 1 | Viewed by 1246
Abstract
This paper represents how typical advanced engineering design can be structured using a set of parameters and objective functions corresponding to the nature of the problem. The set of parameters can be in different types, including integer, real, cyclic, combinatorial, interval, etc. Similarly, [...] Read more.
This paper represents how typical advanced engineering design can be structured using a set of parameters and objective functions corresponding to the nature of the problem. The set of parameters can be in different types, including integer, real, cyclic, combinatorial, interval, etc. Similarly, the objective function can be presented in various types including integer (discrete), float, and interval. The simulated annealing with crystallization heuristic can deal with all these combinations of parameters and objective functions when the crystallization heuristic presents a sensibility for real parameters. Herein, simulated annealing with the crystallization heuristic is enhanced by combining Bates and Gaussian distributions and by incorporating feedback strategies to emphasize exploration or refinement, or a combination of the two. The problems that are studied include solving an electrical impedance tomography problem with float parameters and a partially evaluated objective function represented by an interval requiring the solution of 32 sparse linear systems defined by the finite element method, as well as an airplane design problem with several parameters and constraints used to reduce the explored domain. The combination of the proposed feedback strategies and simulated annealing with the crystallization heuristic is compared with existing simulated annealing algorithms and their benchmark results are shown. The enhanced simulated annealing approach proposed herein showed better results for the majority of the studied cases. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)
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Article
CNN-Based Fault Detection for Smart Manufacturing
Appl. Sci. 2021, 11(24), 11732; https://doi.org/10.3390/app112411732 - 10 Dec 2021
Cited by 6 | Viewed by 2139
Abstract
A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating [...] Read more.
A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)
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Article
An Optimization Tool for Production Planning: A Case Study in a Textile Industry
Appl. Sci. 2021, 11(18), 8312; https://doi.org/10.3390/app11188312 - 08 Sep 2021
Cited by 1 | Viewed by 1780
Abstract
The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete [...] Read more.
The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete Event Simulation (DES) and optimization through genetic algorithms. The focus is on production planning for weaving processes and optimization to help make decisions about batch sizing and production scheduling activities. In addition, the correlations between some current technological trends and their implications for the textile industry are also highlighted. Another important contribution of this study is to detail the use of the commercial software Tecnomatix Plant Simulation 13®, to simulate and optimize a production problem by applying genetic algorithms with real production data. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)
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Article
Smart Topology Optimization Using Adaptive Neighborhood Simulated Annealing
Appl. Sci. 2021, 11(11), 5257; https://doi.org/10.3390/app11115257 - 05 Jun 2021
Cited by 5 | Viewed by 1703
Abstract
Topology optimization (TO) of engineering products is an important design task to maximize performance and efficiency, which can be divided into two main categories of gradient-based and non-gradient-based methods. In recent years, significant attention has been brought to the non-gradient-based methods, mainly because [...] Read more.
Topology optimization (TO) of engineering products is an important design task to maximize performance and efficiency, which can be divided into two main categories of gradient-based and non-gradient-based methods. In recent years, significant attention has been brought to the non-gradient-based methods, mainly because they do not demand access to the derivatives of the objective functions. This property makes them well compatible to the structure of knowledge in the digital design and simulation domains, particularly in Computer Aided Design and Engineering (CAD/CAE) environments. These methods allow for the generation and evaluation of new evolutionary solutions without using the sensitivity information. In this work, a new non-gradient TO methodology using a variation of Simulated Annealing (SA) is presented. This methodology adaptively adjusts newly-generated candidates based on the history of the current solutions and uses the crystallization heuristic to smartly control the convergence of the TO problem. If the changes in the previous solutions of an element and its neighborhood improve the results, the crystallization factor increases the changes in the newly random generated solutions. Otherwise, it decreases the value of changes in the recently generated solutions. This methodology wisely improves the random exploration and convergence of the solutions in TO. In order to study the role of the various parameters in the algorithm, a variety of experiments are conducted and results are analyzed. In multiple case studies, it is shown that the final results are well comparable to the results obtained from the classic gradient-based methods. As an additional feature, a density filter is added to the algorithm to remove discontinuities and gray areas in the final solution resulting in robust outcomes in adjustable resolutions. Full article
(This article belongs to the Special Issue Smart Manufacturing and Industry 4.0)
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