Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Analogical and Logical Thinking – In the Context of Inter- or Trans-Disciplinary Communication and Real-Life Problems
Nagib Callaos, Jeremy Horne
(pages: 1-17)

Artificial Intelligence for Drone Swarms
Mohammad Ilyas
(pages: 18-22)

Brains, Minds, and Science: Digging Deeper
Maurício Vieira Kritz
(pages: 23-28)

Can AI Truly Understand Us? (The Challenge of Imitating Human Identity)
Jeremy Horne
(pages: 29-38)

Comparison of Three Methods to Generate Synthetic Datasets for Social Science
Li-jing Arthur Chang
(pages: 39-44)

Digital and Transformational Maturity: Key Factors for Effective Leadership in the Industry 4.0 Era
Pawel Poszytek
(pages: 45-48)

Does AI Represent Authentic Intelligence, or an Artificial Identity?
Jeremy Horne
(pages: 49-68)

Embracing Transdisciplinary Communication: Redefining Digital Education Through Multimodality, Postdigital Humanism and Generative AI
Rusudan Makhachashvili, Ivan Semenist
(pages: 69-76)

Engaged Immersive Learning: An Environment-Driven Framework for Higher Education Integrating Multi-Stakeholder Collaboration, Generative AI, and Practice-Based Assessment
Atsushi Yoshikawa
(pages: 77-94)

Focus On STEM at the Expense of Humanities: A Wrong Turn in Educational Systems
Kleanthis Kyriakidis
(pages: 95-101)

From Disciplinary Silos to Cyber-Transdisciplinary Networks: A Plural Epistemic Model for AGI-Era Knowledge Production
Cristo Leon, James Lipuma
(pages: 102-115)

Generative AI (Artificial Intelligence): What Is It? & What Are Its Inter- And Transdisciplinary Applications?
Richard S. Segall
(pages: 116-125)

How Does the CREL Framework Facilitate Effective Interdisciplinary Collaboration and Experiential Learning Through Role-Playing?
James Lipuma, Cristo Leon
(pages: 126-145)

Narwhals, Unicorns, and Big Tech's Messiah Complex: A Transdisciplinary Allegory for the Age of AI
Jasmin Cowin
(pages: 146-151)

Playing by Feel: Gender, Emotion, and Social Norms in Overwatch Role Choice
Cristo Leon, Angela Arroyo, James Lipuma
(pages: 152-163)

Responsible Integration of AI in Public Legal Education: Regulatory Challenges and Opportunities in Albania
Adrian Leka, Brunilda Haxhiu
(pages: 164-170)

The Civic Mission of Universities: Transdisciplinary Communication in Practice
Genejane Adarlo
(pages: 171-175)

The Promise and Peril of Artificial Intelligence in Higher Education
James Lipuma, Cristo Leon
(pages: 176-182)

They Learned the Course! Why Then Do They Come to Tutorials?
Russell Jay Hendel
(pages: 183-187)

To Use or Not to Use Artificial Intelligence (AI) to Solve Terminology Issues?
Ekaterini Nikolarea
(pages: 188-195)

Transdisciplinary Supersymmetry: Generative AI in the Vector Space of Postdigital Humanism
Rusudan Makhachashvili, Ivan Semenist
(pages: 196-204)

Why Is Trans-Disciplinarity So Difficult?
Ekaterini Nikolarea
(pages: 205-207)


 

Abstracts

 


ABSTRACT


Production Quality of Shaped Surfaces During Milling

Marek Sadilek, Robert Cep, Lenka Cepova, Lukas Kusnir, Patrik Sniehotta, Hana Stverkova, Katarzyna Czerna


In the context of the development of continuous improvement, increasing the level of quality, safety and protection of the ecological environment, it is necessary to deal with the sensitive phases of the production process and to evaluate the efficiency in terms of time and cost. In the paper, the quality of production in milling (method that is using rotary cutters to remove material) of shaped surfaces is pursued. The quality of the production process leads to the satisfaction of customer needs, and it is essential to focus on the quality/price ratio due to non-conformities. In the paper, the authors use the quality method to provide effective solutions and improve production activities, processes, and systems. This approach stands for a quality management system applied as a perpetual improvement tool, where individual activities consist of four steps: Plan, Do, Check, and Action, with returned stages developing a cycle. This cycle starts with minor to examine potential effects on systems and progresses to more extensive and precise improvements. The results of the implementation of effective solution method can be practiced for constant improvement and as a working model in developing a process or system in an organization. The different stages of the method are applied to set the path tolerance in relation to precision in 3-axis milling. The paper describes area computer numeric control milling center programming during 3-axis finishing milling. The article is focusing on setting the tolerance of tool paths during finishing milling in Computer Aided Manufacturing systems to recommend specific tolerance settings in computer aided Manufacturing systems concerning achieved accuracy, machining time, surface roughness, and quantity of blocks of machine tool control program. Finding suitable tool paths during finishing is very time-consuming and can be expensive. The aim is also to compare the practical results of machining with predicted simulation. The methodology for evaluating this problem is based on the following steps: experimental sample design for production, accuracy prediction of machined samples, production of samples using Computer Numeric Control milling center, analysis of accuracy, and surface roughness for the shape of the workpiece. The result is the variance of the shape accuracy deviations from the specified computer-aided design model of the workpiece, focusing on individual areas of its shape. The workpiece (aluminium alloy), focusing on individual areas of its shape. The research results show milled surface errors depending on the tool path tolerances. Using the effective solution method, it is possible to efficiently set up individual processes to improve the quality of production processes for time and cost.

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