Unveiling Surface Roughness Trends and Mechanical Properties in Friction Stir Welded Similar Alloys Joints Using Adaptive Thresholding and Grayscale Histogram Analysis

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study provides an evaluation of the mechanical and surface characteristics of FSW-welded AA6061-T6 aluminum alloy, incorporating tensile strength, hardness, and surface roughness analyses. The integration of macrostructural imaging and microhardness profiling provides good information on joint performance. However, some aspect of this study require further descriptions. Below are my comments:

 

1. Please describe in the abstract what criteria or parameters were considered to reach a reliable surface roughness in the FSW joint
2. The hardness and tensile results should be quantified or specified.
3. In line 35, FSW joint is not defect free joint. It has residual stress which can also effect on the surface roughness. Refer to the following paper to see how the FSW parameters can effect on the residual stress

Farhang, M., Farahani, M., et al.. (2022). Experimental Correlation Between Microstructure, Residual Stresses and Mechanical Properties of Friction Stir Welded 2024-T6 Aluminum Alloys. International Journal of Advanced Design & Manufacturing Technology, 15(3).

Bhushan, R. K., & Sharma, D. (2020). Investigation of mechanical properties and surface roughness of friction stir welded AA6061-T651. International Journal of Mechanical and Materials Engineering, 15, 1-14.

1. Line 146, how did you prepare your samples for microhardness test
2. Line 155, the profile presented in Fig.3 is for surface temperature. Can a thermal camera monitor temperature variations through the thickness of the specimen?
3. Please rewrite this sentence “As previously mentioned, FSW welded AA6061-T6 aluminum alloys at a rotational speed of 1250 rpm, a welding speed of 71 mm/min, and a tilt angle of 1.5°”
4. Line 189, what do you mean of “roughen the microstructure”? Please describe.
5. Line 202, please specify the standard for Vickers test.
6. In line 206, please write each picture specification in Fig.6
7. Please specify how surface roughness correlates with tensile/hardness results in your study.

Author Response

Reviewer #1

This study provides an evaluation of the mechanical and surface characteristics of FSW-welded AA6061-T6 aluminum alloy, incorporating tensile strength, hardness, and surface roughness analyses. The integration of macrostructural imaging and microhardness profiling provides good information on joint performance. However, some aspect of this study require further descriptions. Below are my comments:

Ans: We sincerely thank the reviewer for the constructive and insightful comments. We greatly appreciate the time and effort invested in reviewing our manuscript. We have carefully addressed each point raised, and detailed responses are provided below. All suggested clarifications and improvements have been implemented to enhance the quality and clarity of the study.

1. Please describe in the abstract what criteria or parameters were considered to reach a reliable surface roughness in the FSW joint. The hardness and tensile results should be quantified or specified.

Ans: Thank you for your valuable comment. In response, we have revised the abstract to explicitly include the criteria and parameters used to achieve reliable surface roughness in the FSW joint. Additionally, we have specified the hardness and tensile strength results as requested.

The following text has been added to the abstract:

“The surface roughness of the weld joint was measured along the weld line at three symmetrical levels using welding parameters that included a rotational speed of 1250 rpm, a welding speed of 71 mm/min, and a tilt angle of 1.5°. The average hardness in the stir zone was measured at 64 HV, compared to 50 HV in the base material, indicating a strengthening effect induced by the welding process. In terms of tensile strength, the welded joint exhibited a maximum force of 2.759 kN.”

1. In line 35, FSW joint is not defect free joint. It has residual stress which can also effect on the surface roughness. Refer to the following paper to see how the FSW parameters can effect on the residual stress

Farhang, M., Farahani, M., et al.. (2022). Experimental Correlation Between Microstructure, Residual Stresses and Mechanical Properties of Friction Stir Welded 2024-T6 Aluminum Alloys. International Journal of Advanced Design & Manufacturing Technology, 15(3).

Bhushan, R. K., & Sharma, D. (2020). Investigation of mechanical properties and surface roughness of friction stir welded AA6061-T651. International Journal of Mechanical and Materials Engineering, 15, 1-14.

Ans: Thank you for your insightful comment regarding the presence of residual stresses in FSW joints and their influence on surface roughness. In response, we have added the following text to the manuscript to acknowledge this important aspect and cite the suggested references:

It is well-established that FSW joints are not entirely free of defects, particularly re-sidual stresses, which can influence surface quality, mechanical performance, and even surface roughness [13], [14].

1. Line 146, how did you prepare your samples for microhardness test

Ans: Thank you for your comment regarding the sample preparation for the microhardness test. We have clarified this in the manuscript by adding the following sentence:

"Samples were sectioned perpendicular to the weld, then mounted, polished, and etched to reveal the microstructure before microhardness testing, following standard metallographic procedures."

1. Line 155, the profile presented in Fig.3 is for surface temperature. Can a thermal camera monitor temperature variations through the thickness of the specimen?

Ans: Thank you for your observation. We acknowledge that thermal cameras are limited to measuring surface temperatures only. To capture temperature variations through the thickness of a specimen, alternative methods such as embedding thermocouples at various depths or employing numerical thermal simulations are required. This has been clarified in the revised manuscript.

"It should be noted that the thermal camera used in this study records only surface temperature. Embedded thermocouples or numerical thermal modeling techniques would be necessary to assess temperature distribution through the material thickness, which is out of this study's purpose."

1. Please rewrite this sentence “As previously mentioned, FSW welded AA6061-T6 aluminum alloys at a rotational speed of 1250 rpm, a welding speed of 71 mm/min, and a tilt angle of 1.5°”

Ans: Thank you for your comment. We have revised the sentence for clarity and to avoid repetition, as suggested. The modified version has been updated in the manuscript.

"In this study, AA6061-T6 aluminum alloy plates were joined using friction stir welding, performed at a tool rotational speed of 1250 rpm, a traverse speed of 71 mm/min, and a tool tilt angle set at 1.5° which were considered according to previous studies."

1. Line 189, what do you mean of “roughen the microstructure”? Please describe.

Ans: Thank you for your observation. We acknowledge that the phrase “roughen the microstructure” was not precise. We have revised the text for clarity. In this context, it refers to the development of microstructural irregularities or heterogeneities—such as non-uniform grain size or distribution—resulting from insufficient stirring or thermal instability, which can negatively impact surface finish and joint integrity.

"This is because high temperatures can significantly alter the microstructure of the welded joints, leading to the formation of irregular grain structures or heterogeneous zones that may adversely affect surface quality and mechanical performance."

 

1. Line 202, please specify the standard for Vickers test.

Ans: Thank you for your observation. The text below has already been added to the manuscript:

"The Vickers microhardness test was conducted according to ISO 6507-1."

1. In line 206, please write each picture specification in Fig.6

Ans: Thank you for your observation. The text below has already been added to the manuscript:

All the images in Figure 6 were captured under identical imaging conditions and magnification. The key distinction between them lies in their respective positions along the weld line, representing different distances from the start of the welding zone.

1. Please specify how surface roughness correlates with tensile/hardness results in your study.

Thank you for your valuable comment. While our study did not perform a detailed statistical correlation analysis, a general trend was observed: lower surface roughness (minimum Ra) tended to align with higher tensile strength and increased hardness values. This suggests that improved surface finish may be associated with better weld quality and more uniform microstructure in the stir zone. We have clarified this in the revised manuscript. We also added this text for more clarification:

"Although a direct quantitative correlation was not established, the results indicate that regions exhibiting lower surface roughness also demonstrated higher tensile strength and microhardness. This relationship suggests that smoother weld surfaces may indicate improved material consolidation and microstructural uniformity within the stir zone."

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript investigates the surface roughness and mechanical properties of friction stir welded AA6061-T6 aluminum alloy joints. Here are some suggestions for the authors to improve the paper:

• Stay consistent with the terminology (i.e., joint types-is it correct to use FSW joint as well as the welded joint anywhere) throughout the text.
• Provide the details of image analysis: specify the specific type of adaptive thresholding (e.g., Otsu, mean, Gaussian) as well the software and image preprocessing
• How was the roughness tester calibrated, how many times per location were roughness values measured, and how the repeatability controlled?
• What is the sample size of each test (tensile, hardness, roughness) and specify if these are on average and in the case include variability measures (e.g., SD)
• Add error bars and statistical analysis in all graphs and tables that are relevant. For mechanical properties and surface roughness values also report standard deviations or confidence intervals Report.
• The authors should discuss image processing and how they correspond to different types of defects or quality in terms of adaptive thresholding, histrogram analysis for welded area.

Author Response

Reviewer 2

This manuscript investigates the surface roughness and mechanical properties of friction stir welded AA6061-T6 aluminum alloy joints. Here are some suggestions for the authors to improve the paper:

Ans: We sincerely thank the respected reviewer for their valuable time, thoughtful comments, and constructive suggestions. We greatly appreciate the careful evaluation of our manuscript titled "Investigation of Surface Roughness and Mechanical Properties of Friction Stir Welded AA6061-T6 Aluminum Alloy Joints." The feedback provided has been instrumental in improving the quality, clarity, and scientific rigor of our work. In response, we have thoroughly revised the manuscript as per the recommendations, and we believe the updated version more effectively presents the findings and their significance..

1. Stay consistent with the terminology (i.e., joint types-is it correct to use FSW joint as well as the welded joint anywhere) throughout the text.

Ans: Thank you for the valuable comment. All terms have been revised to consistently use 'FSW joints'

1. Provide the details of image analysis: specify the specific type of adaptive thresholding (e.g., Otsu, mean, Gaussian) as well the software and image preprocessing

Ans: Thank you for your insightful comment. The image analysis was conducted using adaptive thresholding based on histogram distribution techniques in Python, implemented through Google Colab. Specifically, Gaussian adaptive thresholding was applied to enhance contrast and segment key features of the image. Prior to thresholding, standard preprocessing steps—including grayscale conversion and noise reduction via Gaussian blurring—were performed to improve the accuracy of segmentation and minimize false detections. The text below already adequately addresses:

The image analysis was performed using Python within the Google Colab environment. Adaptive thresholding techniques were applied to extract critical features from the images. Specifically, Gaussian adaptive thresholding was used, which computes the threshold for smaller regions of the image, thereby allowing for more effective segmentation under varying lighting conditions. Before thresholding, all images were converted to grayscale and subjected to Gaussian blurring to reduce noise and enhance edge detection. The histogram-based analysis supported accurate segmentation and identification of key image regions related to defect characterization and feature boundaries.

1. How was the roughness tester calibrated, how many times per location were roughness values measured, and how the repeatability controlled?

Ans: Thank you for your insightful comment. The roughness tester was calibrated according to the manufacturer's instructions before each measurement session to ensure accuracy and reliability. For each location on the welded joint, surface roughness values (Ra) were measured three times, and the average value was used for analysis. This repeated measurement approach-controlled repeatability and minimized random errors, ensuring that the reported roughness values accurately reflected the true surface condition of the FSW joint.

1. What is the sample size of each test (tensile, hardness, roughness) and specify if these are on average and in the case include variability measures (e.g., SD)

Ans: Thank you for your insightful comment. For each test, the sample size was as follows:

• Tensile tests: Three tensile specimens were prepared and tested for each welding condition
• Hardness tests: Three measurements were taken at each designated location across the weld zone
• Surface roughness tests: Surface roughness was measured three times at each location, and the average value was used for analysis

Add error bars and statistical analysis in all graphs and tables that are relevant. For mechanical properties and surface roughness values also report standard deviations or confidence intervals Report.

1. The authors should discuss image processing and how they correspond to different types of defects or quality in terms of adaptive thresholding, histrogram analysis for welded area.

Ans: Thank you for your insightful comment. The text below already adequately addresses:

To evaluate the quality of welded regions and identify defect patterns, digital image processing techniques were applied. For isolating the welded region, especially under non-uniform lighting, Gaussian adaptive thresholding proved effective. This approach calculates thresholds by considering the weighted sum of nearby pixel values, a technique well-suited for highlighting local contrasts and boundaries. These thresholded images then underwent further analysis using grayscale histograms. This step allowed for the classification of how pixel intensities were distributed within the welded zone. Typically, areas with low intensity and inconsistent patterns were linked to porosity, lack of bonding, or voids. In contrast, uniform intensity generally suggested a sound weld. This combination of adaptive thresholding and histogram analysis offered a robust method for differentiating between defect-free and defective regions, contributing to a more quantitative assessment of weld quality.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Accept in present form