Effective Defect Detection in Additive Manufacturing can lead to upto 50% Of savings in Production Cost

The cost savings from defect detection in additive manufacturing can vary significantly depending on?various factors such as the type of defects, the scale of production, the specific industry, and the efficiency of the detection process.

?In this article we will cover some of the potential areas where cost?can be saved through?correct and efficient?defect detection:

1. Material Savings: Identifying and?eliminating defective parts or structures early in the manufacturing process could lead to less waste of expensive materials, which can lead to substantial cost reductions.

2. Rework and Scrap Reduction: Defect detection can help catch issues before the manufacturing process is completed, reducing the need for?rework or scrapping of faulty components, saving time and additional cost.

3. Improved Product Quality: Detecting defects before the products are delivered to customers can prevent costly product recalls, returns, and customer complaints. Ensuring high-quality products with better customer satisfaction and brand reputation.

4. Avoiding Equipment Damage: Some defects in additive manufacturing may cause damage to the machinery or tools used in the process. Early detection can prevent costly repairs or replacements.

5. Time and Labor Savings:?Identifying and addressing defects early can reduce the time spent on troubleshooting and rework, freeing up resources for other productive tasks.

6. Enhanced Efficiency: Efficient defect detection systems can streamline the production process, reducing downtime and increasing overall productivity.

Research papers and industry use case have shown that defect detection in additive manufacturing can lead to savings ranging from a 15% to over 50% of production costs, depending on the different factors.?

One of the efficient techniques for defect detection is to see visuals of manufactured parts in detail. Interspectral-AM Explorer specialize in viewing these defects minutely helping in improved AM workflow while saving cost and time.

Explore all features of the AM Explorer here???

https://interspectral.com/industry/amexplorer/

Want to dig deeper? Here are some links:

Visual Detection of Defects in Robot 3D Printing - 3DPrint.com | The Voice of 3D Printing / Additive Manufacturing

Brion, D. a. J., & Pattinson, S. W. (2022). Generalisable 3D printing error detection and correction via multi-head neural networks. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-31985-y

Eight Manufacturing Wastes Explained - Definition, Causes, and Solutions (benjaminwann.com)

Colosimo, B. M., Cavalli, S., & Grasso, M. (2020). A cost model for the economic evaluation of in-situ monitoring tools in metal additive manufacturing. International Journal of Production Economics, 223, 107532. https://doi.org/10.1016/j.ijpe.2019.107532

Chen, Y., Peng, X., Kong, L., Dong, G., Remani, A., & Leach, R. (2021). Defect inspection technologies for additive manufacturing. International Journal of Extreme Manufacturing, 3(2), 022002. https://doi.org/10.1088/2631-7990/abe0d0