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The present study is aimed to determine the infoecology of scientific articles in the field of smart manufacturing (SM). The researchers designed a general framework for the investigation of infoecology.

The qualitative and quantitative data collection methods are applied to collect data from the Scopus and experts. The bibliometric technique, clustering and graph mining are applied to analysis data by Scopus data analysis tools, VOSviewer and Excel software.

It is concluded that researchers paid attention to “Flow Control”, “Embedded Systems”, “IoT”, “Big Data” and “Cyber-Physical System” more than other infocenose. Finally, a thematic model presented based on the infoecology of SM in Scopus for future studies. Also, as future work, designing a “research-related” metamodel for SM would be beneficial for the researchers, to highlight the main future research directions.

The results of the present study can be applied to the following issues: (1) To make decisions based on research and scientific evidence and conduct scientific research on real needs and issues in the field of SM, (2) Holding the workshops on infoecology to determine research priorities with the presence of experts in related industries, (3) Determining the most important areas of research in order to improve the index of applied research, (4) Assist in prioritizing research in the field of SM to select a set of research and technological activities and allocate resources effectively to these activities, (5) Helping to increase the relationship between research and technological activities with the economic and long-term goals of industry and society, (6) Helping to prioritize the issues of SM in research and technology in order to target the allocation of financial and human capital and solving the main challenges and take advantage of opportunities, (7) Helping to avoid fragmentation of work and providing educational infrastructure based on prioritized research needs and (8) Helping to hold start-ups and the activities of knowledge-based companies based on research priorities in the field of SM.

The analysis results demonstrated that the information ecosystem of SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of this field.

This infecological study mainly aimed to know the thematic and conceptual relationship in published papers in deep learning (DL) and smart manufacturing (SM).

The research methodology has specific research objectives based on the type and method of research, data analysis tools. In general, description methods are applied by Web of Science (WoS) analysis tools and Voyant tools as a web-based reading and analysis environment for digital texts. The Yewno tool is applied to draw a knowledge map to show the concept's interaction between DL and SM.

The knowledge map of DL and SM concepts shows that there are currently few concepts interacting with each other, while the rapid growth of technology and the needs of today's society have revealed the need to use more and more DL in SM. The results of this study can provide a coherent and well-mapped road map to the main policymakers of the field of research in DL and SM, through the study of coexistence and interactions of the thematic categories with other thematic areas. In this way, they can design more effective guidelines and strategies to solve the problems of researchers in conducting their studies and direct. The analysis results demonstrated that the information ecosystem of DL and SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of subjects and concepts in this area.

The paper investigated the thematic interaction of the scientific productions in DL and SM and discussed possible implications. We used of the variety tools and techniques to draw our own perspective. Also, we drew arguments from other research work to back up our findings.