01 · Digital twin
Process-aware quality prediction
Research on injection moulding examines how live process parameters can support defect prediction and improve separation decisions.
Visionvista · Research & Innovation
We connect mechanical design with digital-twin methods, applied AI and automation to study process quality, defect prediction and reliable equipment operation.
Research focus
Our work brings mechanical systems, process data and practical automation into the same conversation.
01 · Digital twin
Research on injection moulding examines how live process parameters can support defect prediction and improve separation decisions.
02 · Responsible AI
Decision-support approaches pair transparent models with human review when the system cannot make a confident quality call.
03 · Applied machinery
Design work spans part-separation mechanisms, automated processing equipment and road-maintenance machinery concepts.
Selected publications
Peer-reviewed and conference work on digital twins, injection-moulding quality and industrial decision support.
International journal · 2026
Published in the International Journal on Interactive Design and Manufacturing (IJIDeM). The study explores process-data monitoring and defect prediction for an injection-moulding part separator.
View the IJIDeM publication ↗INDIACom conference · 2026
Conference research explores explainable decision logic and human review for uncertain manufacturing quality decisions.
Open Google Scholar profile ↗Intellectual property & design
Visionvista’s applied design work includes a part-separator mechanism for injection moulding and road-maintenance system concepts.
For publication and profile details, visit the Visionvista research profile.
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