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Applied Sciences
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Innovative Solutions for Intelligent and Sustainable Machinery
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Innovative Solutions for Intelligent and Sustainable Machinery

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 10314

Special Issue Editor

Dr. Łukasz Gierz

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, Poznan University of Technology, 60-965 Poznan, Poland
Interests: innovative machines and devices for the agri-food and forestry sector; DEM and FEM simulation studies; artificial intelligence; neural networks; machine learning; computer image analysis; SLA/DLP; mechanical and thermal properties; application of coatings in devices and machines; photopolymerization; photocurable coatings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Innovative machines and devices for the agri-food and forestry sector have been dynamically developing for several years, providing space for the development of all fields of science. For many scientists, this means a great opportunity for dynamic development, which translates into lower production costs of products as well as a significant increase in efficiency and, at the same time, better quality of products. The use of modern and even innovative design solutions and algorithms in control systems, machine learning, and artificial intelligence methods, as well as simulation methods, allows you to optimize individual processes. The constantly growing expectations of the market and potential customers force the development of new machines and devices and their automation through the use of new algorithms in automation and control systems. Intelligent machines, devices, and systems are becoming an inseparable element of industry 4.0, agriculture 4.0, and sustainable food system 4.0. The synergy of expertise and science can increase the potential and competitiveness of innovation for broader and more equitable global socioeconomic development.

Dr. Łukasz Gierz
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • innovative solutions
  • sustainable food system 4.0
  • industry 4.0
  • agriculture 4.0
  • digital technology
  • simulation methods
  • artificial vision
  • artificial intelligence (AI)
  • machine learning methods

Published Papers (6 papers)

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Research

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11 pages, 894 KiB  
Article
Application of a Selected Pseudorandom Number Generator for the Reliability of Farm Tractors
by Karol Durczak, Piotr Rybacki and Agnieszka Sujak
Appl. Sci. 2022, 12(23), 12452; https://doi.org/10.3390/app122312452 - 5 Dec 2022
Cited by 3 | Viewed by 984
Abstract
Knowledge of the use-to-failure periods of process equipment, including agricultural vehicles, is essential for the determination of their durability and reliability. Obtaining any empirical data on this issue is difficult and sometimes impossible. Experimental studies are costly and time-consuming. Manufacturers are usually reluctant [...] Read more.
Knowledge of the use-to-failure periods of process equipment, including agricultural vehicles, is essential for the determination of their durability and reliability. Obtaining any empirical data on this issue is difficult and sometimes impossible. Experimental studies are costly and time-consuming. Manufacturers are usually reluctant to share such data, claiming that the information is classified for the sake of their companies. The purpose of this study was to compare empirical data with data generated using adequate statistical tools. The newly generated and very similar in value pseudorandom numbers were obtained by simulations using the Monte Carlo, Latin hypercube sampling and Iman-Conover methods. Reliability function graphs obtained from the generated time-series (use-to-failure periods) with matching Weibull distribution had very similar shape and scale parameters. They were are also comparable to parameters from experimental data extracted from a Polish Zetor agricultural tractor service station. The validation of the applied methods was limited as it was carried out only on the basis of the available data. Analysis of line graphs of cumulative deviations of the values of use-to-failure periods (times-to-fail) generated against empirical times-to-fail indicated that the best method in the studied case was the Monte Carlo method. Full article
(This article belongs to the Special Issue Innovative Solutions for Intelligent and Sustainable Machinery)
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15 pages, 3229 KiB  
Article
A Comparative Analysis of the Dynamic Strength Properties of the Long Guides of Intelligent Machines for a New Method of the Thermal Spraying of Polymer Concrete
by Gulnara Zhetessova, Tatyana Nikonova, Łukasz Gierz, Alexandra Berg, Vassiliy Yurchenko, Olga Zharkevich and Kalinin Alexey
Appl. Sci. 2022, 12(20), 10376; https://doi.org/10.3390/app122010376 - 14 Oct 2022
Cited by 1 | Viewed by 1120
Abstract
The possibility of using polymer concrete for metal cutting machine beds is analyzed. A comparison of the structures of the machine beds made of polymer concrete and cast iron is made. The frequency of the body of the machine beds made of polymer [...] Read more.
The possibility of using polymer concrete for metal cutting machine beds is analyzed. A comparison of the structures of the machine beds made of polymer concrete and cast iron is made. The frequency of the body of the machine beds made of polymer concrete and cast iron is determined. An analysis of the stress–strain state under static loads is carried out. To increase resistance to wear, it is proposed to spray polymer concrete frames are proposed by the gas–thermal method. The installation of thermal spraying for guide machine beds is given. The optimal parameters for spraying the guides of the machine beds made of polymer concrete are established calculated using the finite element method for the guide beds of metal cutting machine tools using polymer concrete with gaseous coating. The manufacture of the foundation bed from polymer concrete increases the vibration resistance of the machine by 1.4 times. At the same time, the metal consumption of metal cutting machines will decrease by 60%. To increase the wear resistance of the frame guides, it is proposed to use thermal spraying with certain modes typical for polymer concrete. The installation of thermal spraying for bed guides is given. Calculating using the finite element method for the guide beds of metal cutting machines showed that the use of polymer concrete with a wear-resistant coating is justified. Full article
(This article belongs to the Special Issue Innovative Solutions for Intelligent and Sustainable Machinery)
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19 pages, 958 KiB  
Article
Determinants of Collaborative Robots Innovation Adoption in Small and Medium-Sized Enterprises: An Empirical Study in China
by Dong Liu and Junwei Cao
Appl. Sci. 2022, 12(19), 10085; https://doi.org/10.3390/app121910085 - 7 Oct 2022
Cited by 3 | Viewed by 2159
Abstract
With the rapid development of industry 4.0 and the boom of large-scale product customization, the adoption of collaborative robots’ innovation becomes a hot topic in research. Previous studies have mainly focused on individuals, but few on enterprises, and in particular, there has been [...] Read more.
With the rapid development of industry 4.0 and the boom of large-scale product customization, the adoption of collaborative robots’ innovation becomes a hot topic in research. Previous studies have mainly focused on individuals, but few on enterprises, and in particular, there has been a lack of empirical research on the enterprise level. Based on the combined model of Technology-Organization-Environment Framework (TOE) and Diffusion of Innovations Theory (DOI), this study investigated 373 small and medium-sized enterprises (SMEs) in Guangdong Province, China, to explore the determinants of SMEs’ adoption of collaborative robot innovation in technology, organization, and environment. The result shows that the technical factors of relative advantage, compatibility, observability, and trialability have a significant positive correlation with the adoption of collaborative robots, while complexity has a significant negative correlation with the adoption. Among the organizational factors, top management support and organizational readiness have a significant positive correlation with the adoption of collaborative robots. Among the environmental factors, agent support is positively and significantly correlated with adoption. The findings will help practitioners develop appropriate strategies for the adoption of collaborative robot innovation. Full article
(This article belongs to the Special Issue Innovative Solutions for Intelligent and Sustainable Machinery)
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15 pages, 2037 KiB  
Article
Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
by Adnan Riaz, Hameedur Rahman, Muhammad Ali Arshad, Muhammad Nabeel, Affan Yasin, Mosleh Hmoud Al-Adhaileh, Elsayed Tag Eldin and Nivin A. Ghamry
Appl. Sci. 2022, 12(19), 9723; https://doi.org/10.3390/app12199723 - 27 Sep 2022
Cited by 2 | Viewed by 1374
Abstract
Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based [...] Read more.
Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based architecture for network-wide traffic speed prediction. We propose a deep-learning-based model consisting of a fully convolutional neural network, bidirectional long short-term memory, and attention mechanism. Our design aims to consider both backward and forward dependencies of traffic data to predict multistep network-wide traffic speed. Thus, we propose a model named AttBDLTSM-FCN for multistep traffic speed prediction. We augmented the attention-based bidirectional long short-term memory recurrent neural network with the fully convolutional network to predict the network-wide traffic speed. In traffic speed prediction, this is the first time that augmentation of AttBDLSTM and FCN have been exploited to measure the backward dependency of traffic data, as a building block for a deep architecture model. We conducted comprehensive experiments, and the experimental evaluations illustrated that the proposed architecture achieved better performance compared to state-of-the-art models when considering the short and long traffic speed prediction, e.g., 15, 30, and 60 min, in multistep traffic speed prediction. Full article
(This article belongs to the Special Issue Innovative Solutions for Intelligent and Sustainable Machinery)
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12 pages, 931 KiB  
Article
Determination of Seed Volume Based on Selected Seed Dimensions
by Zdzisław Kaliniewicz, Dariusz Choszcz and Adam Lipiński
Appl. Sci. 2022, 12(18), 9198; https://doi.org/10.3390/app12189198 - 14 Sep 2022
Cited by 1 | Viewed by 2729
Abstract
The volume coefficient, which denotes a simple relationship between selected seed dimensions and seed volume, can be used to facilitate volume calculations in individual seeds, in particular in species with a complex seed shape. For this reason, seed thickness, width, and length were [...] Read more.
The volume coefficient, which denotes a simple relationship between selected seed dimensions and seed volume, can be used to facilitate volume calculations in individual seeds, in particular in species with a complex seed shape. For this reason, seed thickness, width, and length were measured in nine species of forest trees and shrubs. The volume of seeds belonging to each plant species was determined by pycnometry, and the results were used to calculate 10 volume coefficients based on different combinations of basic seed dimensions. The calculated coefficients had different values, and they were lowest when volume was determined based on the cube of seed length and highest when volume was determined based on the cube of seed thickness. In a formula based on all three basic dimensions, the calculated volume coefficient ranged from 0.376 to 0.537, and Cornus macrophylla, Picea abies, and Cornus sanguinea seeds most closely resembled an ellipsoid. When seed volume was determined with the use of two basic dimensions, formulas based on the square of the smaller dimension produced somewhat smaller errors in individual seeds. In turn, seed thickness should be used in formulas that rely on a single dimension. Seed volume coefficients were most strongly correlated with the sphericity index, which indicates that this parameter can be used to estimate their values. The sphericity index was most strongly correlated with volume coefficients; the strongest correlations were observed for volume coefficients calculated based on the square of the seed length and seed width, and the cube of the seed length. Full article
(This article belongs to the Special Issue Innovative Solutions for Intelligent and Sustainable Machinery)
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14 pages, 2340 KiB  
Technical Note
A Proposal for a Processing Line for Cauliflower and Broccoli Floretting
by Krzysztof Jadwisieńczak, Zdzisław Kaliniewicz, Stanisław Konopka, Dariusz Choszcz and Joanna Majkowska-Gadomska
Appl. Sci. 2023, 13(4), 2509; https://doi.org/10.3390/app13042509 - 15 Feb 2023
Viewed by 1119
Abstract
The edible portions of cauliflowers and broccoli are immature flower heads composed of florets attached to the stalk. In most cases, larger florets are separated into smaller pieces during processing. Complex processing lines for cauliflower and broccoli floretting are available on the market, [...] Read more.
The edible portions of cauliflowers and broccoli are immature flower heads composed of florets attached to the stalk. In most cases, larger florets are separated into smaller pieces during processing. Complex processing lines for cauliflower and broccoli floretting are available on the market, but they are very expensive and require a large working area. Therefore, the aim of this study was to present a proposal for a new floretting unit dedicated to this group of vegetables. The unit will be operated in small farms; it will help producers shorten processing times and sell their goods for higher prices. It was assumed that the unit will feature two main devices: a vegetable crusher and a calibrator. The crusher will remove the florets from the stalk and break larger florets into smaller pieces of appropriate size. Florets with a diameter of 2 to 6 cm will be separated by the calibrator. During the process, leaves, stalks, and very small florets will fall into separate containers, and these fractions will be further processed into food products. The entire process will be carried out directly on the farm, which can increase potential profits by around 25%. Due to a shorter processing time, the product is likely to be fresher and more appealing for consumers. Full article
(This article belongs to the Special Issue Innovative Solutions for Intelligent and Sustainable Machinery)
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