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        <title>Frontiers in Manufacturing Technology | Software Technologies section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/manufacturing-technology/sections/software-technologies</link>
        <description>RSS Feed for Software Technologies section in the Frontiers in Manufacturing Technology journal | New and Recent Articles</description>
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        <pubDate>2026-04-13T23:28:17.217+00:00</pubDate>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmtec.2023.1282843</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmtec.2023.1282843</link>
        <title><![CDATA[Leveraging I4.0 smart methodologies for developing solutions for harvesting produce]]></title>
        <pubdate>2023-12-15T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ava Recchia</author><author>Jill Urbanic</author>
        <description><![CDATA[Leveraging Computer-Aided Design (CAD) and Manufacturing (CAM) tools with advanced Industry 4.0 (I4.0) technologies presents numerous opportunities for industries to optimize processes, improve efficiency, and reduce costs. While certain sectors have achieved success in this effort, others, including agriculture, are still in the early stages of implementation. The focus of this research paper is to explore the potential of I4.0 technologies and CAD/CAM tools in the development of pick and place solutions for harvesting produce. Key technologies driving this include Internet of Things (IoT), machine learning (ML), deep learning (DL), robotics, additive manufacturing (AM), and simulation. Robots are often utilized as the main mechanism for harvesting operations. AM rapid prototyping strategies assist with designing specialty end-effectors and grippers. ML and DL algorithms allow for real-time object and obstacle detection. A comprehensive review of the literature is presented with a summary of the recent state-of-the-art I4.0 solutions in agricultural harvesting and current challenges/barriers to I4.0 adoption and integration with CAD/CAM tools and processes. A framework has also been developed to facilitate future CAD/CAM research and development for agricultural harvesting in the era of I4.0.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmtec.2022.971410</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmtec.2022.971410</link>
        <title><![CDATA[Large data for design research: An educational technology framework for studying design activity using a big data approach]]></title>
        <pubdate>2022-10-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Corey Schimpf</author><author>Molly H. Goldstein</author>
        <description><![CDATA[The complexity of design problems compels the collection of rich process data to understand designers. While some methods exist for capturing detailed process data (e.g., protocol studies), design research focused on design activities still faces challenges, including the scalability of these methods and technology transformations in industry that require new training. This work proposes the Large Data for Design Research (LaDDR) framework, which seeks to integrate big data properties into platforms dedicated to studying design practice and design learning to offer a new approach for capturing process data. This technological framework has three design principles for transforming design platforms: broad simulation scope, unobtrusive logging and support for creation and analysis actions. The case is made that LaDDR platforms will lead to three affordances for research and education: capturing design activities, context setting and operationalization, and research design scalability. Big data and design expertise are reviewed to show how this approach builds on past work. Next, the framework and affordances are presented. Three previously published studies are presented as cases to illustrate the ways in which a LaDDR platform’s affordances manifest. The discussion covers how LaDDR platforms can address the aforementioned challenges, including advancing human-technology collaboration and how this approach can be extended to other design platforms.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmtec.2022.946452</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmtec.2022.946452</link>
        <title><![CDATA[Defect detection on optoelectronical devices to assist decision making: A real industry 4.0 case study]]></title>
        <pubdate>2022-08-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>George P. Moustris</author><author>George Kouzas</author><author>Spyros Fourakis</author><author>Georgios Fiotakis</author><author>Apostolos Chondronasios</author><author>Abd Al Rahman M. Abu Ebayyeh</author><author>Alireza Mousavi</author><author>Kostas Apostolou</author><author>Jovana Milenkovic</author><author>Zoi Chatzichristodoulou</author><author>Erik Beckert</author><author>Jeremy Butet</author><author>Stéphane Blaser</author><author>Olivier Landry</author><author>Antoine Müller</author>
        <description><![CDATA[This paper presents an innovative approach, based on industry 4.0 concepts, for monitoring the life cycle of optoelectronical devices, by adopting image processing and deep learning techniques regarding defect detection. The proposed system comprises defect detection and categorization during the front-end part of the optoelectronic device production process, providing a two-stage approach; the first is the actual defect identification on individual components at the wafer level, while the second is the pre-classification of these components based on the recognized defects. The system provides two image-based defect detection pipelines. One using low resolution grating images of the wafer, and the other using high resolution surface scan images acquired with a microscope. To automate the entire process, a communication middleware called Higher Level Communication Middleware (HLCM) is used for orchestrating the information between the processing steps. At the last step of the process, a Decision Support System (DSS) collects all information, processes it and labels it with additional defect type categories, in order to provide recommendations to the optoelectronical engineer. The proposed solution has been implemented on a real industrial use-case in laser manufacturing. Analysis shows that chips validated through the proposed process have a probability to lase at a specific frequency six times higher than the fully rejected ones.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmtec.2022.937889</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmtec.2022.937889</link>
        <title><![CDATA[Advances in Adaptive Scheduling in Industry 4.0]]></title>
        <pubdate>2022-07-19T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Dimitris Mourtzis</author>
        <description><![CDATA[The shift of traditional mass-producing industries towards mass customisation practices is nowadays evident. However, if not implemented properly, mass customisation can lead to disturbances in material flow and severe reduction in productivity. Moreover, manufacturing enterprises often face the challenge of manufacturing highly customized products in small lot sizes. One solution to adapt to the ever-changing demands, which increases resource flexibility, lies in the digitization of the manufacturing systems. Furthermore, the distributed manufacturing environment and the ever-increasing product variety and complexity result in reduced time-to market, ubiquitous data access and sharing and adaptability and responsiveness to changes. These requirements can be achieved through smart manufacturing tools and especially Wireless Sensor Networks (WSN). Thus, the aim of this position paper is to summarize the design and development of solutions based on cutting-edge technologies such as Cloud Computing, Artificial Intelligence (AI), Internet of Things (IoT), Simulation, 5G, and so on. Concretely, the first part discusses the development of a Cloud-based production planning and control system for discrete manufacturing environments. The proposed approach takes into consideration capacity constraints, lot sizing and priority control in a “bucket-less” manufacturing environment. Then, an open and interoperable Internet of Things platform is discussed, which is enhanced by innovative tools and methods that transform them into Cyber-Physical Systems (CPS), supporting smart customized shopping, through gathering customers’ requirements, adaptive production, and logistics of vending machines replenishment and Internet of Things and Wireless Sensor Networks for Smart Manufacturing. To that end, all the proposed methodologies are validated using data derived from Computer Numerical Control (CNC) machine building industry, from European Metal-cutting and mold-making SMEs, from white goods industry and SMEs that produces solar panels.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmtec.2022.921445</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmtec.2022.921445</link>
        <title><![CDATA[Identifying and Assessing the Required I4.0 Skills for Manufacturing Companies’ Workforce]]></title>
        <pubdate>2022-07-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Federica Acerbi</author><author>Monica Rossi</author><author>Sergio Terzi</author>
        <description><![CDATA[Nowadays, the diffusion of digital and industry 4.0 (I4.0) technologies is affecting the manufacturing sector with a twofold effect. While on one side it represents the boost fastening the competitive advantage of companies, on the other hand it is often accompanied by several challenges that companies need to face. Among all, companies are required to invest in technologies to empower their production activities on the shopfloor without lagging behind their workforce in order to undertake a linear, aware, and structured path toward digitization. The extant literature presents some research conducted to support companies toward digitization, and they usually rely on maturity models in this intention. Nevertheless, few studies included the assessment of workforce skills and competencies in the overall assessment, and in this case, they provide a high level perspective of the investigation, mainly based on check lists which may limit the objectivity of the assessment, and usually they do not customize the assessment based on companies’ requirements. Therefore, considering the importance to balance investments in technologies with those in the workforce to move toward the same direction, this contribution aims to develop a structured, customizable, and objective skill assessment model. With this intention, it has been first clarified the set of job profiles required in I4.0, together with the needed related skills based on the extant literature findings; second, it has been identified the set of key criteria to be considered while performing the assessment of the workforce; third, it has been defined the method to be integrated in the maturity model to enable the initial setting of the weights of the criteria identified according to the company needs; and fourth, based on these findings, it has been developed the assessment model. The developed model facilitates the elaboration of the proper workforce improvement plans to be put in practice to support the improvement of the skills of the whole workforce based on company’s needs.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fmtec.2022.880332</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fmtec.2022.880332</link>
        <title><![CDATA[The Game Analysis of Information Sharing for Supply Chain Enterprises in the Blockchain]]></title>
        <pubdate>2022-06-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qian Tang</author><author>Zeng Zhang</author><author>Zhonglei Yuan</author><author>Zhen Li</author>
        <description><![CDATA[The problems of additional inventory costs and inaccurate demand estimation caused by information asymmetry has severely damaged the profits of all participants and restricted the overall development of the supply chain. New technologies and ideas are urgently needed to solve these problems. The blockchain technology is widely accepted as a disruptive technology and a powerful tool to resolve information asymmetry with its advantage in decentralization, transparency, traceability, confidentiality, immutability, etc. The introduction of blockchain into the information system will effectively promote supply chain collaboration by facilitating information sharing among enterprises at various nodes. The paper builds a consortium chain model suitable for supply chain information sharing, uses evolutionary game theory to analyze the strategy changes and influencing factors in the information sharing choices of supply chain participants, and finally verifies the correctness of the results through MATLAB simulation.]]></description>
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