Addressing the top 3 challenges Manufacturers still have apprehensions when it comes to adopting machine vision solutions. However, consistent advancement in this domain has been addressing their concerns.
Halting production
CHALLENGE
To ensure seamless transformation of production lines, where defects or concerns are addressed without halting production or impacting any loss in product volumes.
SOLUTION
State-of-the-art depth cameras enable quick scanning of machine parts, reverse engineering, editing changes, printing fresh designs, and putting processes back to production in almost no time. This ‘non-intrusiveness’ of machine vision technology enables its adoption into existing processes without halting or causing significant changes in the production line.
Scaling production
CHALLENGE
There is an urgency to replicate or scale up successful machine vision solutions. However, the transition may not be smooth as requirements and models may vary.
SOLUTION
Undoubtedly, the value machine vision technology can deliver to manufacturing industries in almost all its value chains. Vision cameras need to get cheaper for their widespread adoption. Data has to be made available for continuous machine vision model training to address data drifts, adhering to all data access standards and privacy concerns. Simultaneously, as the hunger for expedited manufacturing grows, there is also a dire need to be open in standardizing manufacturing processes and aligning on targeted delivery. This may require investment in the form of upgrading toward uniform production lines.
Lack of data
CHALLENGE
Lots of data flows into manufacturing units but may not be retained or labeled. Where success highly depends on the quality of data available, the absence of labeled data or no data is a major hiccup while delivering solutions to the manufacturing or supply chain industry.
SOLUTION
Machine learning, a part of machine vision, now develops models that work on less data footprint. Where thousands of images were earlier required to train models, now with semi-supervised learning methods, models can be fine-tuned with very less or partially labeled data to generate effective solutions.
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