Hardware and software components
The peak hardware and software components have been meticulously selected for experimental configuration in order to ensure optimal performance and reliability. Tables 1 and 2 detail the hardware and software configurations.
IoT-Blockchain system performance
Detection of defects
The improvements in the performance of the proposed IoT-Blockchain method were evaluated by comparing the results in terms of F1 score, data integrity and traceability with those of a basic method, which uses traditional detection of defects based on Computer vision without integration of the blockchain. The results of these comparisons have been subject to statistical tests to assess whether the improvements observed were statistically significant.
A test of summer carried out to compare F1 scores, data integrity scores and traceability measures between the proposed method and the basic method. The zero hypothesis assumed no difference between the two systems, while the alternative hypothesis posed that the improvements were statistically significant. P values for the F1 score, data integrity and traceability improvements have proved to be 0.003, 0.001 and 0.004, which are all lower than 0.05 respectively. This indicates that the improvements observed in the proposed system are statistically significant.
For example, the F1 score of the proposed system increased to 0.94, against 0.84 in the basic system, with a value of P of 0.003, confirming a statistically significant improvement. Likewise, the data integrity score increased to 0.99 and traceability to 0.98, both showing statistically significant improvements with P values of 0.001 and 0.004, respectively, as illustrated in Table 3.
Real -time performance
Tests have been carried out to assess the system’s capacity to process and analyze molds in real time. The following table 4 sums up the results.
Performance measurements indicate the efficiency and speed of our integrated IoT-Blockchain system in real world manufacturing scenarios. On average, the system only takes 2.3 s to treat and fully analyze a flow. This rapid processing includes image capture, defect detection and recording results on blockchain. The system has a flow of 26 casts per minute. The system can manage a significant production volume. Assuming continuous operation, this is equivalent to 1560 casts per hour. In 24 hours, the system can manage 37,440 plaster. For the practical implementation of industry, a combination of time and debit is important. This allows transparent integration in existing lines. The impact on manufacturing speed is minimal. Performance guarantees quality. The control maintains the pace. This prevents bottlenecks and maintains effective operations.
Dimensional measurement precision
The dimensional measurement system has been rigorously examined. The tested data set contained 500 molds, all with pre -established dimensions. The evaluation has shown that an average average error (MAE) of only 0.05 mm was obtained. In addition, an average root square error (RMSE) of 0.07 mm has been demonstrated. These results had a remarkable level of precision and confirmed that the system is suitable for quality control in the production of investment flow.
Blockchain efficiency
The need for improved data and security management is suitable for the effective use of IoT devices for advanced evaluation of flow defects via the integration of blockchain. In this system, each conclusion of inspection, such as defect categories and even size measurements, would be captured in blockchain technology, resulting in detailed and transparent recordings. To control the quality of the inspection data provided via the network, transactions are confirmed using consensus techniques such as proof of work or proof of participation. Although the blockchain adds a considerable calculation constraint to the system (15% more use of the processor and 22% in addition to the use of the RAM), the advantages prevail over the drawbacks. Bimodal access makes it possible to keep the aspect data in a location that prevents change, gives a way to follow the data processed and validates the data in question throughout the inspection process using the blockchain. The recording of a transaction takes approximately 1.2 s because the transaction blocks are produced every 12 s, and each block can manage 10 transactions. The use of blockchain technology in IoT does not compromise the efficiency or secrecy of inspection processes.
Calculation cost
The integration of blockchain technology leads to an increase of 15% of the use of the processor and a 22% increase in the use of memory. However, the advantages of immutability and traceability prevail over calculation costs. These features are essential for safety and transparency, which non -blockchain solutions cannot achieve. Despite the increase in resource consumption, the cognitive benefits of blockchain technology, such as innovation, efficiency, traceability and a single source of truth, justify calculation costs.
Scalability
To effectively meet the scalability challenges associated with high production volumes and large data sets, the proposed system incorporates EDGE calculation to process data locally on IoT devices. This approach considerably reduces the quantity of data which must be transmitted to central servers or blockchain, thus attenuating the congestion of the network and minimizing latency. By moving data processing by the network, closer to where the data is generated, the system can provide real -time information without crushing the central processing unit. This not only improves treatment speed, but also improves the system’s ability to evolve in environments with high data flow. In addition, Edge Computing optimizes the use of resources by reducing the need for continuous data transfers, making the system more economical in energy and capable of managing the growing demands of large -scale manufacturing operations. In combination with the characteristics of the immutability and integrity of blockchain data, Edge Computing guarantees that the system remains effective, secure and reactive as the production costs increase, which allows it to develop transparent and meet the growing requirements of modern manufacturing environments. The scalability of the system has been tested by simulating increased production rates as indicated in Table 5:
Under higher loads, the system has shown excellent scalability, with only a moderate increase in times for creating transactions and blocks.
Comparison with other methods
The comparison of the IoT-Blockchain method proposed with three existing cutting-edge systems highlights significant advantages in quantitative and qualitative measurements. In terms of F1 score, the proposed method reaches 0.94, exceeding all other methods, with traditional approaches giving scores between 0.80 and 0.85. This improvement is also supported by a higher 96%precision, demonstrating the higher performance of the method in defect detection. In addition, the proposed system excels in the integrity and traceability of data, with scores of 0.99 and 0.98, respectively, thanks to its integration of blockchain, offering a level of security and transparency that lack ‘Other systems. The processing time and flow are also higher, the proposed system dealing with 26 pieces molded per minute in 2.3 s per molding, considerably surpassing alternative methods which take 3 to 5 ss by molding and treat fewer pieces molded per minute.
From a qualitative perspective, the proposed method offers a large -scale breed, which allows it to effectively manage large production volumes of data and increase effectively. It also has superior security, with the integrity of the blockchain -based data and protection against falsification, unlike traditional methods that can be vulnerable to data handling. However, the ease of integration with existing manufacturing processes is moderately complex compared to the simpler and not based on chain solutions. The system of useability of the system (SUS) of 0.82 indicates a high satisfaction of users, highlighting the conviviality of the method in practical applications. Overall, the proposed IoT-Blockchain system is distinguished by its robust combination of precision, safety, efficiency and scalability, positioning it as a transformative solution for intelligent manufacturing environments, as shown in the table 6.
User experience assessment
In addition to the score score of system (SUS) of 0.82, which indicates high user satisfaction, user qualitative comments highlighted forces and areas to improve the system. Many users have found the data validation process and the integration of intuitive IoT devices, as it required a minimum manual input and provided real -time comments. The ability to transparently validate and store fault data on blockchain has been much appreciated to guarantee data integrity and traceability, which users have found particularly useful in quality control tasks.
However, some users were faced with challenges when browsing the user interface (user interface), in particular when interpreting error messages during data entry. These messages were sometimes clear, which makes users difficult to quickly solve problems. In addition, the integration of blockchain and IoT devices forced users to learn new concepts, which some have found complex. Although the system is generally easy to use once familiar, some users have suggested that integration guides or offenses could help improve the initial learning curve.
Comments have also highlighted the need for personalization options more in terms of preferences and user notifications. Some users have suggested that adding visual feedback, such as progression bars during data processing or alerts when data is successfully recorded on blockchain – could make the system more user -friendly.
Overall, qualitative feedback is aligned with the SUI scoring, reflecting that the system is generally welcomed but could benefit from certain improvements in the user interface and a more intuitive handling of errors. This information will shed light on future updates, helping to refine the approach to system usability and global user experience.
