What is Plating Index Solutions

Understanding the Plating Index

The Plating Index is a critical parameter used in the metal finishing industry to assess and optimize electroplating processes. It provides valuable insights into the performance and quality of the plating bath, enabling manufacturers to make informed decisions and achieve desired results. In this comprehensive article, we will delve into the intricacies of Plating Index Solutions, exploring their significance, applications, and best practices.

Definition of Plating Index

The Plating Index is a numerical value that represents the relative efficiency and effectiveness of an electroplating solution. It takes into account various factors such as the composition of the plating bath, operating conditions, and the desired properties of the plated deposit. By calculating and monitoring the Plating Index, metal finishers can evaluate the health and performance of their plating solutions.

Importance of Plating Index in Metal Finishing

In the metal finishing industry, achieving consistent and high-quality plating results is of utmost importance. The Plating Index serves as a valuable tool in this regard, offering several benefits:

  1. Quality Control: By regularly measuring and tracking the Plating Index, manufacturers can ensure that their plating processes are operating within optimal ranges. Any deviations from the desired index values can be promptly identified and corrected, preventing defects and maintaining the quality of the plated products.

  2. Process Optimization: The Plating Index provides insights into the efficiency of the plating bath. By analyzing the index values, metal finishers can make informed decisions to optimize their processes. This may involve adjusting bath composition, operating parameters, or replenishment strategies to enhance plating performance and reduce waste.

  3. Cost Savings: Maintaining a stable and optimal Plating Index can lead to significant cost savings. By ensuring that the plating solution is operating at peak efficiency, manufacturers can minimize the consumption of chemicals, reduce reject rates, and extend the life of the plating bath. This translates to lower operating costs and improved profitability.

Factors Affecting the Plating Index

Several key factors influence the Plating Index of an electroplating solution. Understanding these factors is crucial for maintaining a healthy and efficient plating bath. Let’s explore each of these factors in detail:

Bath Composition

The composition of the plating bath plays a vital role in determining the Plating Index. The concentration and ratio of the main components, such as metal salts, additives, and pH regulators, directly impact the plating efficiency and deposit quality. Here are some key considerations regarding bath composition:

  • Metal Salt Concentration: The concentration of the metal salt, such as copper sulfate or nickel chloride, affects the plating rate and deposit properties. Maintaining the optimal concentration range is essential for achieving the desired Plating Index.

  • Additives: Plating baths often contain various additives, such as brighteners, levelers, and stress reducers, to enhance the deposit characteristics. The type and concentration of these additives significantly influence the Plating Index. Proper selection and control of additives are crucial for maintaining a stable and efficient plating process.

  • pH Level: The pH of the plating bath impacts the plating reaction and deposit properties. Each plating system has an optimal pH range that must be maintained to achieve the desired Plating Index. Regular monitoring and adjustment of the pH are necessary to ensure consistent results.

Operating Conditions

The operating conditions of the electroplating process also play a significant role in determining the Plating Index. Key factors to consider include:

  • Current Density: The current density, measured in amperes per square foot (ASF) or amperes per square decimeter (ASD), affects the plating rate and deposit properties. Higher current densities generally result in faster plating rates but may also lead to increased stress and reduced deposit quality. Finding the optimal current density range is essential for achieving the desired Plating Index.

  • Temperature: The temperature of the plating bath influences the plating reaction kinetics and deposit characteristics. Each plating system has a recommended temperature range that must be maintained to ensure optimal performance. Deviations from this range can affect the Plating Index and lead to various plating defects.

  • Agitation: Proper agitation of the plating bath is crucial for maintaining a uniform distribution of metal ions and additives. Insufficient or excessive agitation can impact the Plating Index by affecting the mass transfer and deposit uniformity. Selecting the appropriate agitation method and intensity is important for achieving consistent plating results.

Contaminants and Impurities

Contaminants and impurities in the plating bath can have a detrimental effect on the Plating Index. These unwanted substances can enter the bath through various sources, such as drag-in from pretreatment steps, airborne particles, or chemical degradation. Common contaminants include:

  • Organic Impurities: Organic contaminants, such as oils, greases, and surfactants, can interfere with the plating reaction and lead to deposit defects. Regular carbon treatment or other purification methods are necessary to remove these impurities and maintain a stable Plating Index.

  • Metallic Impurities: Metallic impurities, such as iron, chromium, or zinc, can co-deposit with the desired metal and alter the deposit properties. These impurities can enter the bath through substrate drag-in or chemical contamination. Monitoring and controlling the levels of metallic impurities are essential for maintaining the Plating Index within acceptable limits.

  • Anions: Excessive levels of anions, such as chlorides or sulfates, can affect the conductivity and stability of the plating bath. Regular analysis and adjustment of anion concentrations are necessary to prevent any negative impact on the Plating Index.

Measuring and Monitoring the Plating Index

To effectively control and optimize the electroplating process, it is crucial to measure and monitor the Plating Index regularly. This involves several key steps:

Sampling and Analysis

Accurate measurement of the Plating Index requires representative sampling and analysis of the plating bath. The following guidelines should be followed:

  • Sampling Frequency: The frequency of sampling depends on the specific plating system and production requirements. Generally, samples should be taken at least once per shift or more frequently if the process is critical or prone to variations.

  • Sampling Method: Samples should be collected from a well-mixed and representative portion of the plating bath. Care should be taken to avoid contamination during the sampling process.

  • Analysis Techniques: Various analytical techniques can be used to measure the Plating Index, including:

  • Hull Cell Testing: This method involves plating a specially designed test panel under controlled conditions and evaluating the deposit characteristics to determine the Plating Index.
  • Cyclic Voltammetry: This electrochemical technique measures the current response to a varying potential, providing insights into the plating reaction kinetics and efficiency.
  • Spectroscopic Methods: Techniques such as atomic absorption spectroscopy (AAS) or inductively coupled plasma (ICP) analysis can be used to measure the concentrations of metal ions and other components in the plating bath.

Data Interpretation and Trending

Once the Plating Index is measured, it is important to interpret the data and identify any trends or deviations from the desired range. This involves:

  • Establishing Baseline Values: Determine the optimal Plating Index range for the specific plating system based on historical data, industry standards, or experimental results.

  • Tracking Trends: Monitor the Plating Index values over time to identify any gradual changes or sudden fluctuations. Use statistical tools, such as control charts, to visualize the data and detect any patterns or anomalies.

  • Identifying Deviations: Compare the measured Plating Index values with the established baseline range. Any significant deviations should be investigated and corrective actions taken to bring the process back into control.

Corrective Actions and Optimization

Based on the data analysis and interpretation, appropriate corrective actions should be implemented to maintain or improve the Plating Index. Some common strategies include:

  • Bath Additions: If the Plating Index is below the desired range, additions of metal salts, additives, or other components may be necessary to restore the bath composition and improve the plating efficiency.

  • Bath Purification: If contaminants or impurities are detected, purification methods such as carbon treatment, electrolytic dummying, or selective precipitation can be employed to remove the unwanted substances and improve the Plating Index.

  • Parameter Adjustments: Adjusting operating parameters, such as current density, temperature, or agitation, can help optimize the plating process and maintain the Plating Index within the desired range.

  • Process Optimization: Continuously monitor and analyze the Plating Index data to identify opportunities for process improvement. This may involve fine-tuning the bath composition, implementing advanced control strategies, or exploring alternative plating chemistries to enhance the overall efficiency and quality of the plating process.

Best Practices for Maintaining Plating Index Solutions

To ensure the long-term stability and performance of Plating Index Solutions, several best practices should be followed:

Regular Maintenance and Housekeeping

  • Bath Filtration: Implement a regular filtration schedule to remove suspended particles and maintain bath clarity. Use appropriate filter media and replace filters as needed.

  • Equipment Cleaning: Regularly clean and maintain plating equipment, including anodes, cathodes, and tanks, to prevent the buildup of contaminants and ensure optimal performance.

  • Proper Storage and Handling: Store plating chemicals in a cool, dry place and follow proper handling procedures to prevent contamination or degradation.

Employee Training and Education

  • Operator Training: Provide comprehensive training to plating operators on the importance of Plating Index, measurement techniques, and corrective actions. Ensure they understand the impact of their actions on the plating process.

  • Continuous Learning: Encourage employees to stay updated with the latest advancements and best practices in Plating Index Solutions through workshops, seminars, and industry publications.

Documentation and Record-Keeping

  • Standard Operating Procedures (SOPs): Develop and maintain detailed SOPs for Plating Index measurement, data analysis, and corrective actions. Ensure that all employees follow these procedures consistently.

  • Data Management: Implement a robust data management system to store and track Plating Index measurements, trends, and corrective actions. This data can be used for future reference, process optimization, and quality assurance purposes.

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Frequently Asked Questions (FAQs)

  1. What is the Plating Index, and why is it important?
    The Plating Index is a numerical value that represents the efficiency and effectiveness of an electroplating solution. It is important because it helps metal finishers assess the health and performance of their plating baths, ensuring consistent and high-quality plating results.

  2. What factors influence the Plating Index?
    The Plating Index is influenced by several key factors, including bath composition (metal salt concentration, additives, pH level), operating conditions (current density, temperature, agitation), and the presence of contaminants or impurities in the plating bath.

  3. How often should I measure the Plating Index?
    The frequency of Plating Index measurement depends on the specific plating system and production requirements. As a general guideline, samples should be taken at least once per shift or more frequently if the process is critical or prone to variations.

  4. What corrective actions can be taken if the Plating Index is out of the desired range?
    If the Plating Index is out of the desired range, corrective actions may include bath additions (metal salts, additives), bath purification (carbon treatment, electrolytic dummying), parameter adjustments (current density, temperature, agitation), or process optimization (fine-tuning bath composition, implementing advanced control strategies).

  5. How can I ensure the long-term stability and performance of Plating Index Solutions?
    To ensure the long-term stability and performance of Plating Index Solutions, follow best practices such as regular maintenance and housekeeping (bath filtration, equipment cleaning), employee training and education, and proper documentation and record-keeping (standard operating procedures, data management).

Conclusion

Plating Index Solutions play a vital role in the metal finishing industry, enabling manufacturers to assess and optimize their electroplating processes. By understanding the factors that influence the Plating Index, implementing regular measurement and monitoring practices, and taking appropriate corrective actions, metal finishers can achieve consistent and high-quality plating results.

Investing in Plating Index Solutions not only ensures the production of superior plated products but also leads to cost savings, increased efficiency, and enhanced customer satisfaction. By staying updated with the latest advancements and best practices in Plating Index Solutions, manufacturers can remain competitive and adapt to the ever-evolving demands of the industry.

As the metal finishing landscape continues to evolve, the importance of Plating Index Solutions will only continue to grow. By embracing this powerful tool and integrating it into their quality control and process optimization strategies, manufacturers can unlock new levels of excellence and drive their businesses towards a successful future.

This comprehensive article has provided an in-depth exploration of Plating Index Solutions, covering its definition, importance, influencing factors, measurement techniques, data interpretation, corrective actions, best practices, and frequently asked questions. Armed with this knowledge, metal finishers can confidently navigate the complexities of electroplating and achieve outstanding results consistently.

So, whether you are a seasoned professional or new to the world of metal finishing, understanding and leveraging Plating Index Solutions is crucial for success. By implementing the insights and strategies outlined in this article, you can optimize your plating processes, enhance product quality, and maximize profitability. Embrace the power of Plating Index Solutions and take your metal finishing operations to new heights!

Parameter Optimal Range
Metal Salt Concentration 5 – 10 oz/gal
Additive Concentration 1 – 5 mL/L
pH Level 3.5 – 4.5
Current Density 20 – 50 ASF
Temperature 120 – 140°F
Agitation 100 – 200 RPM

Table 1: Optimal ranges for key plating parameters.

Contaminant Maximum Allowable Limit
Iron (Fe) 50 ppm
Chromium (Cr) 20 ppm
Zinc (Zn) 10 ppm
Organic Impurities 100 ppm
Chlorides (Cl-) 200 ppm
Sulfates (SO4-2) 500 ppm

Table 2: Maximum allowable limits for common contaminants in plating baths.

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