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Smart Manufacturing Technology II
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Smart Manufacturing Technology II

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

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 32702

Special Issue Editor

Prof. Dr. Michele Calì

E-Mail Website
Guest Editor
Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, 95124 Catania, Italy
Interests: industrial technical drawing; computer-assisted drawing; exercises of automotive constructions; geometric modeling of machines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is believed that digital transformation corresponds to the fourth industrial revolution, the one linked to the concept of Smart Manufacturing; that is, the total integration of information technology, data and physical systems or, using a neologism, the birth and adoption of cyber-physical systems.

Smart Manufacturing is a process where new technologies can be tested to improve productivity and business performances, such as the Internet of Things, Big Data and Cloud Computing, Advanced Automation, wearable devices and Additive Manufacturing (3D printing). These new technologies help to achieve optimization by guaranteeing new levels of performance and efficiency to each staff through a gradual renewal process.

Smart Manufacturing is characterized by the integration of 3 factors: productivity automation, operational information, and advanced analyses. The main ideas behind Smart Manufacturing are that each element of the production chain is connected thanks to the contribution of sensors, measurement and monitoring instruments, and RFID chips, and that intelligent control systems allow to optimize the specific phase of the process, known as “decentralization of control”.

This Special Issue is mainly focused on the latest advances made in the interconnection and cooperation between people, machinery, and information, and in manufacturing processes, manufacturing equipment, manufacturing systems and techniques, machine tools, and enabling technologies. This Special Issue continues on from the success of the first edition, which received considerable interest from various researchers in the field.

Dr. Michele Calì
Guest Editor

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Keywords

  • manufacturing processes
  • machine tools and manufacturing equipment
  • enabling technologies
  • machine learning
  • ergonomics, health, and safety
  • education and training
  • collaborative robots (robot–robot, human–robot etc.)
  • process planning, production planning/scheduling/control
  • computational geometry and CAD/CAM
  • virtual/augmented reality
  • manufacturing networks and security

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Published Papers (9 papers)

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Research

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19 pages, 2003 KiB  
Article
Complex Job Shop Simulation “CoJoSim”—A Reference Model for Simulating Semiconductor Manufacturing
by Dennis Bauer, Daniel Umgelter, Andreas Schlereth, Thomas Bauernhansl and Alexander Sauer
Appl. Sci. 2023, 13(6), 3615; https://doi.org/10.3390/app13063615 - 12 Mar 2023
Cited by 1 | Viewed by 2047
Abstract
The manufacturing industry is facing increasing volatility, uncertainty, complexity, and ambiguity, while still requiring high delivery reliability to meet customer demands. This is especially challenging for complex job shops in the semiconductor industry, where the manufacturing process is highly intricate, making it difficult [...] Read more.
The manufacturing industry is facing increasing volatility, uncertainty, complexity, and ambiguity, while still requiring high delivery reliability to meet customer demands. This is especially challenging for complex job shops in the semiconductor industry, where the manufacturing process is highly intricate, making it difficult to predict the consequences of changes. Although simulation has proven to be an effective tool for optimizing manufacturing processes, reference data sets and models often produce disparate and incomparable results. CoJoSim is introduced in this article as a reference model for semiconductor manufacturing, along with an associated reference implementation that accelerates the implementation and application of the reference model. CoJoSim can serve as a testbed and gold standard for other implementations. Using CoJoSim, different dispatching rules are evaluated to demonstrate an improvement of almost 15 percentage points in adherence to delivery dates compared to the reference. Findings emphasize the importance of optimizing setup time, particularly in products with high variance, as it significantly impacts adherence to delivery dates and throughput. Moving forward, future applications of CoJoSim will evaluate additional dispatching rules and use cases. Combining CoJoSim with dispatching methods that integrate manufacturing and supply networks to optimize production planning and control through reinforcement-learning-based agents is also planned. In conclusion, CoJoSim provides a reliable and effective tool for optimizing semiconductor manufacturing and can serve as a benchmark for future implementations. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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13 pages, 3105 KiB  
Article
Complexity Modeling of Steel-Laser-Hardened Surface Microstructures
by Matej Babič, Dragan Marinkovic, Marco Bonfanti and Michele Calì
Appl. Sci. 2022, 12(5), 2458; https://doi.org/10.3390/app12052458 - 26 Feb 2022
Cited by 8 | Viewed by 2069
Abstract
Nowadays, laser hardening is a consolidated process in many industrial sectors. One of the most interesting aspects to be considered when treating the surface-hardening process in steel materials by means of laser devices is undoubtedly the evaluation of the heat treatment quality and [...] Read more.
Nowadays, laser hardening is a consolidated process in many industrial sectors. One of the most interesting aspects to be considered when treating the surface-hardening process in steel materials by means of laser devices is undoubtedly the evaluation of the heat treatment quality and surface finish. In the present study, an innovative method based on fractal geometry was proposed to evaluate the quality of surface-steel-laser-hardened treatment. A suitable genetic programming study of SEM images (1280 × 950 pixels) was developed in order to predict the effect of the main laser process parameters on the microstructural geometry, assuming the microstructure of laser-hardened steel to be of a structurally complex geometrical nature. Specimens hardened by anthropomorphic laser robots were studied to determine an accurate measure of the process parameters investigated (surface temperature, laser beam velocity, laser beam impact angle). In the range of variation studied for these parameters, the genetic programming model obtained was in line with the complexity index calculated following the fractal theory. In particular, a percentage error less than 1% was calculated. Finally, a preliminary study of the surface roughness was carried out, resulting in its strong correlation with complex surface microstructures. Three-dimensional voxel maps that reproduce the surface roughness were developed by automating a routine in Python virtual environment. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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15 pages, 6136 KiB  
Article
Research on Roundness Error Evaluation of Connecting Rod Journal in Crankshaft Journal Synchronous Measurement
by Tingting Gu, Xiaoming Qian and Peihuang Lou
Appl. Sci. 2022, 12(4), 2214; https://doi.org/10.3390/app12042214 - 20 Feb 2022
Cited by 3 | Viewed by 2634
Abstract
The crankshaft is the core part of an automobile engine, and the accuracy requirements of various shape and position errors are very high. On the basis of a synchronous measurement system, the connecting rod journal is deeply studied, including data processing and roundness [...] Read more.
The crankshaft is the core part of an automobile engine, and the accuracy requirements of various shape and position errors are very high. On the basis of a synchronous measurement system, the connecting rod journal is deeply studied, including data processing and roundness evaluation. Firstly, according to the measuring processes of connecting rod journals, the real sampling angle distribution function was established, and the corresponding Gaussian weight function of each sampling angle was calculated. The weight function and the collected data corresponding to the angle were subjected to discrete cyclic convolution operation in the spatial domain to obtain the filtered effective circular contour data. Secondly, the particle swarm optimization algorithm was improved, and its inertia weight was set to decrease nonlinearly to speed up the convergence. A calculation process suitable for the evaluation of journal errors was designed. Then, the improved particle swarm optimization algorithm was used to evaluate the roundness of the corrected rod journal contour data. At last, through multiple measurement experiments, the feasibility and effectiveness of the synchronous measurement scheme and data processing method proposed in this paper are verified. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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13 pages, 3948 KiB  
Article
Analytical Prediction of Balling, Lack-of-Fusion and Keyholing Thresholds in Powder Bed Fusion
by Wenjia Wang, Jinqiang Ning and Steven Y. Liang
Appl. Sci. 2021, 11(24), 12053; https://doi.org/10.3390/app112412053 - 17 Dec 2021
Cited by 16 | Viewed by 4717
Abstract
This paper proposes analytical modeling methods for the prediction of balling, lack-of-fusion and keyholing thresholds in the laser powder bed fusion (LPBF) additive manufacturing. The molten pool dimensions were first predicted by a closed-form analytical thermal model. The effects of laser power input, [...] Read more.
This paper proposes analytical modeling methods for the prediction of balling, lack-of-fusion and keyholing thresholds in the laser powder bed fusion (LPBF) additive manufacturing. The molten pool dimensions were first predicted by a closed-form analytical thermal model. The effects of laser power input, boundary heat loss, powder size distribution and powder packing pattern were considered in the calculation process. The predicted molten pool dimensions were then employed in the calculation of analytical thresholds for these defects. Reported experimental data with different materials were compared to predictions to validate the presented analytical models. The predicted thresholds of these defects under various process conditions have good agreement with the experimental results. The computation time for the presented models is less than 5 min on a personal computer. The optimized process window for Ti6Al4V was obtained based on the analytical predictions of these defects. The sensitivity analyses of the value of threshold to the laser power and scanning speed were also conducted. The proposed analytical methods show higher computational efficiency than finite element methods, without including any iteration-based computations. The acceptable predictive accuracy and low computational time will make the proposed analytical strategy be a good tool for the optimization of process conditions for the fabrication of defects-free complex products in laser powder bed fusion. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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19 pages, 448 KiB  
Article
Evidence-Based and Explainable Smart Decision Support for Quality Improvement in Stainless Steel Manufacturing
by Henna Tiensuu, Satu Tamminen, Esa Puukko and Juha Röning
Appl. Sci. 2021, 11(22), 10897; https://doi.org/10.3390/app112210897 - 18 Nov 2021
Cited by 5 | Viewed by 1992
Abstract
This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, [...] Read more.
This article demonstrates the use of data mining methods for evidence-based smart decision support in quality control. The data were collected in a measurement campaign which provided a new and potential quality measurement approach for manufacturing process planning and control. In this study, the machine learning prediction models and Explainable AI methods (XAI) serve as a base for the decision support system for smart manufacturing. The discovered information about the root causes behind the predicted failure can be used to improve the quality, and it also enables the definition of suitable security boundaries for better settings of the production parameters. The user’s need defines the given type of information. The developed method is applied to the monitoring of the surface roughness of the stainless steel strip, but the framework is not application dependent. The modeling analysis reveals that the parameters of the annealing and pickling line (RAP) have the best potential for real-time roughness improvement. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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19 pages, 2635 KiB  
Article
Analysis and Optimization of the Robotic Mobile Fulfillment Systems Considering Congestion
by Cheng Chi, Yanyan Wang, Shasha Wu and Jian Zhang
Appl. Sci. 2021, 11(21), 10446; https://doi.org/10.3390/app112110446 - 7 Nov 2021
Cited by 6 | Viewed by 2614
Abstract
With the development of the social economy and the improvement of the consumption concept, a new business model combining offline and online has been promoted. The warehousing system is one of the important links of commodity production and circulation, which involves storage, sorting, [...] Read more.
With the development of the social economy and the improvement of the consumption concept, a new business model combining offline and online has been promoted. The warehousing system is one of the important links of commodity production and circulation, which involves storage, sorting, and distribution. It has a significant impact on the operation cost and the efficiency of the whole logistics system. The progress of robot technology, the Internet of things, and artificial intelligence technology promotes the automation and intelligence of storage systems. The Robotic Mobile Fulfillment Systems (RMFS), which takes the automatic guided vehicles (AGVs) as the way of handling and picking, greatly improves the space utilization, operation efficiency, and flexibility of the system. This paper studies the RMFS with fixed shelves and establishes the performance evaluation model of the picking system considering the AGVs congestion by establishing the queuing network. The effectiveness of the model is verified by simulation, and the optimization of system parameter configuration is further discussed according to the experimental data. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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14 pages, 4004 KiB  
Article
AI-Based Quality Control of Wood Surfaces with Autonomous Material Handling
by Mikael Ericsson, Dahniel Johansson and David Stjern
Appl. Sci. 2021, 11(21), 9965; https://doi.org/10.3390/app11219965 - 25 Oct 2021
Cited by 3 | Viewed by 3206
Abstract
The theory and applications of Smart Factories and Industry 4.0 are increasing the entry into the industry. It is common in industry to start converting exclusive parts, of their production, into this new paradigm rather than converting whole production lines all at once. [...] Read more.
The theory and applications of Smart Factories and Industry 4.0 are increasing the entry into the industry. It is common in industry to start converting exclusive parts, of their production, into this new paradigm rather than converting whole production lines all at once. In Europe and Sweden, recent political decisions are taken to reach the target of greenhouse gas emission reduction. One possible solution is to replace concrete in buildings with Cross Laminated Timber. In the last years, equipment and software that have been custom made for a certain task, are now cheaper and can be adapted to fit more processes than earlier possible. This in combination, with lessons learned from the automotive industry, makes it possible to take the necessary steps and start redesigning and building tomorrows automated and flexible production systems in the wood industry. This paper presents a proof of concept of an automated inspection system, for wood surfaces, where concepts found in Industry 4.0, such as industrial Internet of things (IIoT), smart factory, flexible automation, artificial intelligence (AI), and cyber physical systems, are utilized. The inspection system encompasses, among other things, of the shelf software and hardware, open source software, and standardized, modular, and mobile process modules. The design of the system is conducted with future expansion in mind, where new parts and functions can be added as well as removed. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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Review

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24 pages, 3773 KiB  
Review
Virtual Reality and Digital Human Modeling for Ergonomic Assessment in Industrial Product Development: A Patent and Literature Review
by Adailton Gonçalves da Silva, Marcus Vinicius Mendes Gomes and Ingrid Winkler
Appl. Sci. 2022, 12(3), 1084; https://doi.org/10.3390/app12031084 - 20 Jan 2022
Cited by 23 | Viewed by 8259
Abstract
The late detection of ergonomic component assembly issues during manufacturing processes has an influence on operator well-being and productivity, as well as having a high cost of correction. Although virtual reality may enhance digital human modeling, there is a knowledge gap on the [...] Read more.
The late detection of ergonomic component assembly issues during manufacturing processes has an influence on operator well-being and productivity, as well as having a high cost of correction. Although virtual reality may enhance digital human modeling, there is a knowledge gap on the combination of these technologies to assess ergonomics. This study aims to analyze the application of virtual reality and digital human modeling for physical ergonomics assessment during product development in the industry, through a review of patents and the literature. We searched the Derwent Innovation Index, Scopus, and Web of Science databases and found 250 patents and 18 articles. We observed an exponential increase in patents, concentrated among major technological players, and a wide range of technologies being invented. A significant number of studies focuses on the automotive and aviation industries. Despite a relative consensus in the literature on the benefits of integrating virtual reality and digital human modeling to assess physical ergonomics in the early stages of product development, the technologies are seldom combined in the same analysis; moreover, most cases continue to focus on analyzing pre-designed production processes, when resources are completely deployed. These outcomes may provide a reference for practitioners and researchers to develop novel solutions for the early detection of physical ergonomics issues in the industry. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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18 pages, 1619 KiB  
Review
The Future of Factories: Different Trends
by Giulio Salierno, Letizia Leonardi and Giacomo Cabri
Appl. Sci. 2021, 11(21), 9980; https://doi.org/10.3390/app11219980 - 25 Oct 2021
Cited by 11 | Viewed by 3706
Abstract
The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and [...] Read more.
The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and methodologies that have to be data-driven, AI-assisted, smart, and service-oriented. Therefore, there is a great interest in experimenting with emerging technologies and evaluating how they impact the actual business processes. This paper reports a comparison among the major trends in the digitalization of a Factory of the Future, in conjunction with the two major strategic programs of Industry 4.0 and China 2025. We have focused on these two programs because we have had experience with them in the context of the FIRST H2020 project. European industrialists identify the radical change in the traditional manufacturing production process as the rise of Industry 4.0. Conversely, China mainland launched its strategic plan in China 2025 to promote smart manufacturing to digitalize traditional manufacturing processes. The main contribution of this review paper is to report about a study, conducted and part of the aforementioned FIRST project, which aimed to investigate major trends in applying for both programs in terms of technologies and their applications for the factory’s digitalization. In particular, our analysis consists of the comparison between Digital Factory, Virtual Factory, Smart Manufacturing, and Cloud Manufacturing. We analyzed their essential characteristics, the operational boundaries, the employed technologies, and the interoperability offered at each factory level for each paradigm. Based on this analysis, we report the building blocks in terms of essential technologies required to develop the next generation of a factory of the future, as well as some of the interoperability challenges at a different scale, for enabling inter-factories communications between heterogeneous entities. Full article
(This article belongs to the Special Issue Smart Manufacturing Technology II)
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