Blog | LeanTechnique Glass

Category: AI

An Overview of the End-to-End Yield of the Glass Loop

An Overview of the End-to-End Yield of the Glass Loop

In glass production, ‘furnace pull’ refers to the total tonnage of molten glass that exits the furnace. However, not all of this molten glass is converted into usable products; a significant portion becomes waste due to various factors. The losses in the float glass loop can be attributed to the following: Approximately 10-15 cm of […]

What’s Next for Leantechnique? Future Developments to Watch

What’s Next for Leantechnique? Future Developments to Watch

Now, let’s delve into our future roadmap, which is geared towards tackling troubleshooting and data analysis challenges. Here’s a sneak peek into the exciting features and updates we plan to roll out: Enhanced User Experience: We’re committed to refining the user interface to make LeanTechnique not only more visually appealing but also more intuitive and […]

Leantechnique Relational Smart Model (RSM) for Data Collection and Analysis – Part 2

Leantechnique Relational Smart Model (RSM) for Data Collection and Analysis – Part 2

Data Collection for RSM In general, the data feeding into the model are initially organized in the RSM based on their associated plant, workshops, or production sections. They are classified according to various defect sources: Raw Material-Related Defects, Utilities-Related Defects, Batch-Plant Related Defects, Melting-Related Defects, Forming-Related Defects (Tin Bath), Annealing-Related Defects, Cold End-Related Defects, and […]

How Leantechnique Uses AI to Solve Glass Industry Challenges Through Cause-and-Effect Modelling

How Leantechnique Uses AI to Solve Glass Industry Challenges Through Cause-and-Effect Modelling

AI at the Core of Leantechnique Leantechnique  integrates advanced AI technologies to streamline operations within the glass industry. At its core, the app utilizes machine learning algorithms and data analysis to predict and solve potential problems in the production pipeline. Data Analysis for Quality Control: In addition to machine learning, Leantechnique employs sophisticated data analysis […]

Time-Honoured Techniques: Navigating Troubleshooting

Time-Honoured Techniques: Navigating Troubleshooting

Preventive Techniques Traditionally, preventive measures in glass manufacturing involve maintaining critical process parameters within established operational limits. Thus, it is essential to inspect, regulate, and standardize all parameters from the grain size and composition of raw materials to the packaging of the final product. Additionally, ensuring the proper design and maintenance of all equipment, from […]

Understanding the Challenges in the Glass Industry – Part 1

Understanding the Challenges in the Glass Industry – Part 1

For instance, if the primary issue is an increase in silica content in the glass batch, an operator might temporarily increase the furnace temperature to compensate. However, such rapid temperature adjustments are not sustainable and can accelerate the corrosion of refractory materials, leading to visible defects like seeds in the final product and other issues […]