Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0

SIMONIČ, Marko ;PALČIČ, Iztok ;KLANČNIK, Simon .
Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0. 
Articles in Press, [S.l.], v. 0, n.0, p. , july 2025. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/advancing-intelligent-toolpath-generation-a-systematic-review-of-cad-cam-integration-in-industry-4-0-and-5-0/>. Date accessed: 04 oct. 2025. 
doi:http://dx.doi.org/.

Simonič, M., Palčič, I., & Klančnik, S.
(0).
Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0.
Articles in Press, 0(0), .
doi:http://dx.doi.org/

@article{.,
	author = {Marko  Simonič and Iztok  Palčič and Simon  Klančnik},
	title = {Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0},
	journal = {Articles in Press},
	volume = {0},
	number = {0},
	year = {0},
	keywords = {},
	abstract = {This systematic literature review (SLR) investigates advancements in intelligent CAD-CAM integration and toolpath generation, analyzing their evolution across Industry 4.0 and emerging Industry 5.0 paradigms. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the study synthesizes 51 peer-reviewed studies (2000–2025, including early-access publications) to map theoretical foundations, industrial applications, technical innovations, and methodological trends. Findings reveal that artificial intelligence (AI) and machine learning dominate research, driving breakthroughs in feature recognition, adaptive toolpath optimization, and predictive maintenance. However, human-centric frameworks central to Industry 5.0, such as socio-technical collaboration and cognitive ergonomics, remain underexplored. High-precision sectors (aerospace, biomedical) lead adoption, while SMEs lag due to resource constraints. Technologically, AI-driven automation and STEP-NC standards show promise, yet interoperability gaps persist due to fragmented data models and legacy systems. Methodologically, AI-based modeling prevails (49% of studies), but experimental validation and socio-technical frameworks are sparse. Key gaps include limited real-time adaptability, insufficient AI training datasets, and slow adoption of sustainable practices. The review highlights the urgent need for standardized data exchange protocols, scalable SME solutions, and human-AI collaboration models to align CAD-CAM integration with Industry 5.0’s sustainability and resilience goals. By bridging these gaps, this work provides a roadmap for advancing intelligent, human-centered manufacturing ecosystems.},
	issn = {0039-2480},	pages = {},	doi = {},
	url = {https://www.sv-jme.eu/article/advancing-intelligent-toolpath-generation-a-systematic-review-of-cad-cam-integration-in-industry-4-0-and-5-0/}
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Simonič, M.,Palčič, I.,Klančnik, S.
0 July 0. Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0. Articles in Press. [Online] 0:0

%A Simonič, Marko 
%A Palčič, Iztok 
%A Klančnik, Simon 
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%T Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0
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%! Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0
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%X This systematic literature review (SLR) investigates advancements in intelligent CAD-CAM integration and toolpath generation, analyzing their evolution across Industry 4.0 and emerging Industry 5.0 paradigms. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the study synthesizes 51 peer-reviewed studies (2000–2025, including early-access publications) to map theoretical foundations, industrial applications, technical innovations, and methodological trends. Findings reveal that artificial intelligence (AI) and machine learning dominate research, driving breakthroughs in feature recognition, adaptive toolpath optimization, and predictive maintenance. However, human-centric frameworks central to Industry 5.0, such as socio-technical collaboration and cognitive ergonomics, remain underexplored. High-precision sectors (aerospace, biomedical) lead adoption, while SMEs lag due to resource constraints. Technologically, AI-driven automation and STEP-NC standards show promise, yet interoperability gaps persist due to fragmented data models and legacy systems. Methodologically, AI-based modeling prevails (49% of studies), but experimental validation and socio-technical frameworks are sparse. Key gaps include limited real-time adaptability, insufficient AI training datasets, and slow adoption of sustainable practices. The review highlights the urgent need for standardized data exchange protocols, scalable SME solutions, and human-AI collaboration models to align CAD-CAM integration with Industry 5.0’s sustainability and resilience goals. By bridging these gaps, this work provides a roadmap for advancing intelligent, human-centered manufacturing ecosystems.
%U https://www.sv-jme.eu/article/advancing-intelligent-toolpath-generation-a-systematic-review-of-cad-cam-integration-in-industry-4-0-and-5-0/
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%8 2025-07-21
%7 2025-07-21

Simonič, Marko, Iztok  Palčič, & Simon  Klančnik.
"Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0." Articles in Press [Online], 0.0 (0): . Web.  04 Oct. 2025

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AU  - Palčič, Iztok 
AU  - Klančnik, Simon 
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TI  - Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0
JF  - Articles in Press
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N2  - This systematic literature review (SLR) investigates advancements in intelligent CAD-CAM integration and toolpath generation, analyzing their evolution across Industry 4.0 and emerging Industry 5.0 paradigms. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the study synthesizes 51 peer-reviewed studies (2000–2025, including early-access publications) to map theoretical foundations, industrial applications, technical innovations, and methodological trends. Findings reveal that artificial intelligence (AI) and machine learning dominate research, driving breakthroughs in feature recognition, adaptive toolpath optimization, and predictive maintenance. However, human-centric frameworks central to Industry 5.0, such as socio-technical collaboration and cognitive ergonomics, remain underexplored. High-precision sectors (aerospace, biomedical) lead adoption, while SMEs lag due to resource constraints. Technologically, AI-driven automation and STEP-NC standards show promise, yet interoperability gaps persist due to fragmented data models and legacy systems. Methodologically, AI-based modeling prevails (49% of studies), but experimental validation and socio-technical frameworks are sparse. Key gaps include limited real-time adaptability, insufficient AI training datasets, and slow adoption of sustainable practices. The review highlights the urgent need for standardized data exchange protocols, scalable SME solutions, and human-AI collaboration models to align CAD-CAM integration with Industry 5.0’s sustainability and resilience goals. By bridging these gaps, this work provides a roadmap for advancing intelligent, human-centered manufacturing ecosystems.
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	author = {Simonič, M., Palčič, I., Klančnik, S.},
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TY  - JOUR
AU  - Simonič, Marko 
AU  - Palčič, Iztok 
AU  - Klančnik, Simon 
PY  - 2025/07/21
TI  - Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0
JF  - Articles in Press; Vol 0, No 0 (0): Articles in Press
DO  - 
KW  - 
N2  - This systematic literature review (SLR) investigates advancements in intelligent CAD-CAM integration and toolpath generation, analyzing their evolution across Industry 4.0 and emerging Industry 5.0 paradigms. Using the Theory-Context-Characteristics-Methodology (TCCM) framework, the study synthesizes 51 peer-reviewed studies (2000–2025, including early-access publications) to map theoretical foundations, industrial applications, technical innovations, and methodological trends. Findings reveal that artificial intelligence (AI) and machine learning dominate research, driving breakthroughs in feature recognition, adaptive toolpath optimization, and predictive maintenance. However, human-centric frameworks central to Industry 5.0, such as socio-technical collaboration and cognitive ergonomics, remain underexplored. High-precision sectors (aerospace, biomedical) lead adoption, while SMEs lag due to resource constraints. Technologically, AI-driven automation and STEP-NC standards show promise, yet interoperability gaps persist due to fragmented data models and legacy systems. Methodologically, AI-based modeling prevails (49% of studies), but experimental validation and socio-technical frameworks are sparse. Key gaps include limited real-time adaptability, insufficient AI training datasets, and slow adoption of sustainable practices. The review highlights the urgent need for standardized data exchange protocols, scalable SME solutions, and human-AI collaboration models to align CAD-CAM integration with Industry 5.0’s sustainability and resilience goals. By bridging these gaps, this work provides a roadmap for advancing intelligent, human-centered manufacturing ecosystems.
UR  - https://www.sv-jme.eu/article/advancing-intelligent-toolpath-generation-a-systematic-review-of-cad-cam-integration-in-industry-4-0-and-5-0/

Simonič, Marko, Palčič, Iztok, AND Klančnik, Simon.
"Advancing Intelligent Toolpath Generation: A Systematic Review of CAD-CAM Integration in Industry 4.0 and 5.0" Articles in Press [Online], Volume 0 Number 0 (21 July 2025)