Common challenges when applying AI
Artificial intelligence is a relatively new technology, its application in real production still faces many obstacles:
Large investment costs: Investment in servers, software, and human resources to develop AI systems. This is difficult for small and medium enterprises.
Requires AI training with big data. Collecting and processing data takes too much time and human resources through many processes.
The team of engineers and technicians must be highly trained in AI. Human resources with this ability are still scarce
AI systems are difficult to control and predict completely. There is always a potential risk of unexpected errors.
Unique limitations of automatic filling technology
Besides, AI-based automatic filling also faces some specific difficulties:
High technical requirements for machines and automatic system lines require machines to operate accurately, synchronously, and avoid errors.
Diversity in scale and production process: each filling product has its own formula, from large to small scale. Applying the same AI model is hardly feasible.
Difficulty in maintenance and repair: complex mechanical system, lack of technicians with in-depth AI knowledge to handle problems.
The initial investment cost is huge: the production line must be completely renovated to apply AI.
Product quality control still has many limitations: AI cannot completely replace human inspection.3
Thus, it is clear that limitations in technology, human resources, and initial investment costs are still big challenges for manufacturers. To overcome this, long-term investment and research is needed.
Although there are still many challenges, some solutions can be applied to gradually perfect AI automatic filling technology:
Research suitable models for each production scale. Build each separate AI module for each product type, then gradually expand.
Combine AI with sensors and intelligent quality monitoring systems to complement AI.
Promote human resource training in both AI techniques and specialized techniques in filling machinery.
Building a large common database for businesses, reducing the burden of data collection for each company.
Flexible application of the above solutions will help AI automatic filling technology gradually improve in the future.
This is certainly an inevitable trend that cannot be reversed. Thus, the article has analyzed more deeply the challenges and potential of AI automatic filling technology. Hopefully the information will be useful to those who are interested.
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