TaBlitz AI is getting another addition, an AI model that maximizes compression force. With this release we have published an article to help understand why this model was created, principles that were followed to create it and the result comparison to the existing model within TaBlitz.
Optimizing Tablet Design for Maximum Compression Force - Enhancing Tool Longevity and Manufacturing Efficiency
Problem Statement:
Tablet design can be a challenging parametric design process where many different variables must be determined based on your requirements. This process is made even more difficult when attempting to optimize your design for tool life while still meeting your other requirements.
A customer from the nutraceutical industry requested that TaBlitz develop a model prediction that maximizes compression force without sacrificing other characteristics, thus minimizing the potential for manufacturing defects. This article explores how TaBlitz AI was extended to address this request and discusses other potential improvements resulting from this model's application, such as enhanced tool life and reduced maintenance due to tool longevity. Utilizing a design with a higher compression force than what is required significantly reduces the potential for tool failure, contributing to more robust and efficient manufacturing processes.
Overview
This article explores how various geometric ratios and characteristics influence the maximum compression force rating of a given weight and how TaBlitz AI can be leveraged to optimize these factors. TaBlitz AI utilizes deep learning models to analyze shape, size, and ratio relationships, to provide precise recommendations for enhancing the tablet's structural integrity and performance. By incorporating TaBlitz AI into the design process, manufacturers can efficiently evaluate different geometric configurations and identify the optimal design for maximizing compression force potential. Additionally, we will discuss practical applications of TaBlitz AI in tablet manufacturing, including strategies for achieving desired mechanical properties and ensuring product quality.
Creating a tablet design with a maximum compression force higher than what is required by your formulation offers significant advantages, such as longer-lasting tools and reduced tool maintenance. By ensuring that the tool design can withstand greater forces than necessary, the strain on the shape and cup configuration is minimized, leading to extended tool life and fewer instances of wear and tear. This not only reduces downtime and maintenance costs but also enhances overall production efficiency. Utilizing TaBlitz AI to achieve this optimal balance between tablet strength and tool durability can result in a more robust and cost-effective manufacturing process, ultimately contributing to higher quality products and greater operational sustainability.
This article demonstrates how TaBlitz AI can serve as a powerful tool for pharmaceutical scientists and engineers in the tablet formulation and design process.
Producing the Dataset
To gather sufficient data for analyzing the geometric factors that most impact tablet design, we created over 5 million tablet designs. We focused on key geometric characteristics, including Land, Length to Width Ratio, Cup to Thickness Ratio, Thickness to Width Ratio, and End Radius to Width Ratio (for non-round tablets). These characteristics were chosen, aside from Land, based on their significance in the Tablet Specification Manual's (TSM) calculations for maximum compression force. Land was added as a key factor component as the industry recognizes this feature as having a great impact on the overall strength of a design. By concentrating on these parameters, a diverse array of tablet sizes and varying ratios was generated, ensuring a comprehensive dataset for analysis. As each design was calculated, the maximum compression force based on the TSM was also calculated and stored with each data point.
Analyzing the Data
The extensive dataset was analyzed to determine the impact of each geometric characteristic on achieving the highest compression force. By meticulously examining the data, the specific contributions of Land, Length to Width Ratio, Cup to Thickness Ratio, Thickness to Width Ratio, and End Radius to Width Ratio in enhancing the tablet's structural integrity and performance were identified.
The analysis revealed nuanced insights into how each parameter interacts with the others and how these interactions affect the overall compression force potential. For instance, certain ratios significantly improve the tablet's ability to withstand higher compression forces, while others may lead to structural weaknesses if not properly balanced. Understanding these relationships allowed for the identification of optimal geometric configurations that maximize compression force while maintaining the tablet's quality and functionality.
Figure 1 illustrates how different ratios impact the geometry of various tablet types. Figure 2 illustrates the impact associated with different Cup Types. Interpreting these charts with a fixed volume is essential, as it helps to reveal and understand the key relationships and their effects on the overall design. Notably, round tablets lack an end radius and length, which means that the effect distribution is heavily weighted towards the other two ratios.
This evaluation provided a deeper understanding of the basic principles governing tablet design regarding ratio effects and offered practical guidelines for optimizing compression profiles. With these insights, data-driven decisions can be made to refine tablet designs, ensuring they meet stringent performance criteria.
Considering the ratios with the most impact on maximizing compression force we can start to determine where their ratios fall to achieve these results. Looking at Figure 3, the averages for Cup to Thickness and Thickness to Width give us an idea on where the target should be for different shape and cup combinations.
The insights gained from this study provide a clear path for developing and utilizing the AI model. Simply applying these averages to the designs shown in Figure 3 would overlook the potential gains within the nuances of the geometry. By creating a deep learning model and training it on the 5 million data points, it will be possible to understand and maximize force potential at a nuanced level.
Furthermore, these findings have significant implications for the pharmaceutical industry. Manufacturers can leverage this knowledge to enhance the durability and efficiency of their tooling systems, resulting in longer-lasting tools, reduced maintenance requirements, and overall cost savings. By focusing on these key geometric characteristics, a foundation has been established for developing more robust, high-performance tablets that meet both current and future demands.
Existing Model Results
The baseline model currently used in TaBlitz AI prediction targets a tablet design that optimizes the four indexes and maintains the design within the center of the acceptable range for each ratio (Thickness to Width, Cup to Thickness, End Radius to Width, Length to Width). These targets, along with other key considerations in the model, result in a balanced approach to the four indexes (Tablet Manufacturability, Tool Manufacturability, Swallowability and Tool Life). By using this baseline model as a comparison, it is possible to determine whether the results achieve the goal of targeting geometry that maximizes compression force while minimizing detriment to the four indexes.
For this comparison, we will use a target weight of 500mg and a compressed density of 1.1656 g/ml. Round, Oval and Capsule will be the target shapes.
Results from the original model (Figure 4, Figure 5):
Index Chart Summary
Max Compression Force
New Model Results
Reviewing the data from the Max Force chart (Figure 6) initially to determine if we achieved our first goal of maximizing compression force for our 500mg tablet target.
Max Compression Force (New Model)
The model has produced a significant increase in max compression force for the same 500mg tablet. For each tablet and cup configuration, the max force value has nearly doubled. This substantial improvement indicates the model's effectiveness in achieving higher compression forces and suggests a positive impact on tool life. Targeting a tablet design with a max compression force that far exceeds what is required reduces tool wear and maintenance requirements. Consequently, this leads to longer-lasting tools, more efficient manufacturing processes, and improved overall product quality.
Looking at Figure 7, the impact of this model on the other indexes can be determined. As shown, most configurations display the same score or an increase in the overall score across the four indexes. This outcome is achieved due to the model's ability to understand the nuances in each design configuration and maximize the compression force up to the point just before it would cause detriment to the indexes.
Index Chart Summary (New Model)
This delicate balance demonstrates the model's precision and effectiveness in optimizing tablet design. By pushing the compression force to its limits without compromising the structural integrity related to the Thickness to Width, Cup to Thickness, End Radius to Width, and Length to Width ratios, the model ensures that the tablets remain robust and durable. This careful optimization not only improves tablet quality but also enhances the efficiency and longevity of the manufacturing tools.
The ability to maintain or improve the scores across all indexes while significantly increasing the max compression force suggests that the model is highly adept at making fine-tuned adjustments. These adjustments consider the intricate relationships between various geometric parameters, leading to a more holistic and optimized design process. This advancement translates into tangible benefits in manufacturing, including reduced tool wear, lower maintenance costs, increased production efficiency, and ultimately, higher quality pharmaceutical products.
Overall, the data in Figure 7 underscores the model's capability to enhance compression force and maintain balanced performance across all critical indexes, paving the way for more efficient and cost-effective tablet production.
Summary
The analysis of the TaBlitz AI model reveals a substantial enhancement in maximum compression force across various tablet designs. The force values have significantly increased, highlighting the model's effectiveness in optimizing compression force while positively impacting tool life. By targeting designs with compression forces well above the minimum requirements, the model reduces tool wear and maintenance needs, resulting in longer-lasting tools and more efficient manufacturing processes.
Additionally, the model achieves this improvement without compromising the balance of critical design indexes. This careful optimization ensures that tablets remain structurally sound while maximizing performance. The model's ability to understand and adjust the nuances of each design configuration allows it to push compression force limits while maintaining the integrity of these key parameters.
This advanced optimization approach translates into practical benefits for the manufacturing process, including reduced tool maintenance costs, increased production efficiency, and improved overall product quality. The model's precision in balancing compression force with design stability demonstrates its capability to deliver robust and high-quality pharmaceutical tablets, leading to more effective and cost-efficient tablet production.
If you are interested in utilizing the new force optimizing model, login to TaBlitz and open Guided Mode. If you are not yet a user of TaBlitz please reach out to us and we can help get you onboarded to the platform.
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