S.I.Ver, a company in the sheet metal processing sector, adopted an AI system from Technesthai to optimize their production process, improving efficiency, quality, and profitability. The system addresses complex challenges in selecting the optimal setting and has led to significant reductions in operational waste, order release times, and downtime.

The Company

S.I.Ver, a company active in the sheet metal processing sector, relied on Technesthai to develop a cutting-edge AI system that optimizes the production process, increasing efficiency, quality, and profitability.

The Challenge

The manufacturing industry is undergoing a significant transformation driven by artificial intelligence (AI).

A crucial aspect of sheet metal processing is choosing the correct combination of machinery and tools necessary for a specific task - for example, a particular bend on a 40mm thick galvanized steel sheet. This choice, known as "setting," is fundamental to ensure the quality of the final product and the efficiency of the production process. The choice of setting depends on both the technical specifications of the processing and concomitant factors, such as the availability of usable machines and the skills of the operator; it also significantly impacts production costs.

Therefore, selecting the optimal setting presents several challenges:

• High complexity: Choosing the optimal setting depends on multiple factors, including the characteristics of the sheet to be processed, available machines and tools, production priorities, and possible time constraints. Considering all these factors simultaneously is extremely difficult for a human.
• Need for extensive experience: Selecting the optimal setting requires specific skills and knowledge often not widespread within the company, as they develop over years of work. This limitation can lead to bottlenecks and suboptimal decisions with significant impacts on production.
• Dynamism: Unexpected events, such as tool breakage or operator absence, can disrupt the production schedule and require immediate and complete rescheduling. Managing such situations efficiently and promptly with manual methods is practically impossible.

The Solution

The AI system developed by Technesthai for S.I.Ver successfully addresses these challenges, offering an intelligent and automated solution for production process optimization. The main advantages of this technology include:

• Continuous machine learning: The AI system automatically extracts the criteria for setting selection from historical production data, determining methods of association capable of generalizing even to cases not previously encountered. However, a human operator can modify the processing-setting associations proposed by the AI, allowing the AI to learn and improve its performance. This ensures that the system remains cutting-edge and able to adapt to changing production needs.
• Setting optimization: The AI analyzes production data in real-time to identify the optimal setting for each task, considering the entire production queue and minimizing the tool change times on the machines (re-setting). This ensures reduced processing times, maximum efficiency, and the best quality of the final product.
• Waste reduction: Precise setting selection prevents errors and defects in processing, significantly reducing the amount of scrap and rework. This translates into significant savings in materials and labor, as well as a lower likelihood of damaging machines or tool wear.
• Increased productivity: Optimizing cycle times and efficient machine use significantly increases overall productivity, allowing more to be produced in less time.
• Better management of unforeseen events: The AI can react in real-time to unexpected events, automatically rescheduling production to minimize disruptions and downtime. This ensures greater flexibility and resilience of the production system.

The Results

Several months after implementation, we observed the following results:

17% reduction in operational waste by leveraging the AI application's suggestions in choosing the most appropriate equipment.

25% reduction in order release times thanks to the ability to start work and define the most appropriate setting without waiting for the availability of specialized personnel.

70% reduction in downtime using AI processes that assist personnel in selecting an alternative setting in the event of an unforeseen incident.