Studying Methods of Estimating Heat Generation at Three Different Zones in Metal Cutting: A Review of Analytical models

Goyal, Ajay; Kumar, Shailendra; Dhiman, Suresh; Sharma, Rajesh

doi:10.14445/22315381/ijett-v8p296

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…With the increasing use of image processing applications, it is widely used in analyzing targets in the defense industry [7], [9], [10], [40], developing facial recognition systems in security cameras [52], investigating forensic cases [17], [53], analyzing products in industrial organizations [2], [11], [16], [22], [36], [55], [61], [94], developing medical devices in the field of health, making medical diagnoses [45], and analyzing products in the field of agricuture-grain [49], [67]. In these analyses, the attributes of the objects are determined by performing recognition, classification, and clustering operations [74], [77].…”

Section: Image Processingmentioning

confidence: 99%

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

2024

jjmie

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The iron and steel industry is one of the leading sectors contributing to the economic power of countries. It is incorporated into numerous fields, including automotive, construction, manufacturing, agriculture, defense, and healthcare. Today, the increasing supply and demand balance in iron and steel products makes the stock management and control of the products crucial. The large quantity and variety of products in the iron and steel industry make stock management and control difficult. Creating stock for the business is a very costly process and is one of the important elements of the business. Successful stock management can make the business financially advantageous. This study aims to estimate the stock costs of different products and quantities according to different dates found in the business by using data mining. For this purpose, data mining classifier models are used, and estimated costs are found. By establishing a stock tracking system throughout the supply chain, the business should register all inventory movements of the products. It should work in an integrated way with stock management costing. Thus, unforeseen decreases and financial losses in products can be detected.The iron and steel sector, a key player in enhancing competition among nations and shaping the economic landscape, caters to the needs of various industries through its commitment to sustainable steel production. The industry has encountered challenges due to the swift advancements in recent years, impacting the costs associated with iron and steel products and posing issues for the sector. Tackling these challenges is essential for fostering national development and enhancing the competitiveness of businesses. This research delves into the elements influencing the expenses within the iron and steel industry, emphasizing the importance of minimizing factors leading to elevated costs. An automation system was developed using image processing techniques, and iron and steel products were analyzed. In the automation system, k-means and twostep, one of the clustering analysis methods, was applied.A decision support system defined as SDSS (Steel Decision Support System) and BIPS (Burak's Image Processing System)has been developed to determine the inventory costs of iron and steel products, adopting a data mining approach and including clustering methods to reveal similar inventory costs. Measurements with the SDSS-BIPS automation system were 94.4% successful. Withcost levels were determined by matching the products in the iron and steel business database with their costs.

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Section: Image Processingmentioning

confidence: 99%

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

2024

jjmie

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“…Please refer to Figure 5 for a visual representation of this architecture. Goyal A et al [24] have used AI for measuring temperature caused by metal cutting.…”

Section: Review Of Literaturementioning

confidence: 99%

Parametric Study of Inspecting Surface Defects in Investment Casting

2023

jjmie

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Metal defects detection has always been an essential task for the majority of various industries, moreover, it is the core element in the metal inspection too. This research paper explores the effectiveness of different deep learning algorithms for surface-defect detection in investment casting using the Inspection 4.0 approach. The study compared the performance of four popular deep learning algorithms, Fast R-CNN, Faster R-CNN, ResNet, and YOLO, using the accuracy metric as a performance evaluation measure. The results show that ResNet achieved the highest accuracy rate of 95.89%, followed by Faster R-CNN with 90.23%, Fast R-CNN with 89.21%, and YOLO with 86.43%. The findings of this research demonstrate that ResNet and Faster R-CNN are effective deep-learning algorithms for automated surface-defect detection in investment casting. On the other hand, Fast R-CNN and YOLO exhibited lower accuracy rates.The outcomes of this study provide valuable insights into the effectiveness of deep learning algorithms for surface-defect detection in investment casting. The high accuracy rate achieved by ResNet and Faster R-CNN can guide the development of automated inspection systems for investment casting in various industries such as aerospace, automotive, and medical.

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“…Goyal et al [71] comparatively studied the different methods of temperature measurement and explained the pros and cons of different techniques as summarised in table 2. During machining of steel and other superalloys, many researchers have mainly applied thermocouple methods and infrared radiation methods compared to other methods.…”

Section: Analysis Of Grinding Temperaturementioning

confidence: 99%

“…Pros and cons of various methods for temperature measurement[71]. Reproduced with permission from[71].…”

mentioning

confidence: 99%

Grinding temperature and its consequences on induced residual stresses during grinding of nickel-based superalloys: a review

Gupta

Chak

2022

Eng. Res. Express

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In the modern era, the grinding process is economically preferred over conventional milling, turning, and drilling processes considering the factors of high tool wear and time constraints. The grinding process offers a more refined surface finish, high tolerances and is suitable for machining difficult-to-machine materials like nickel-based superalloy. High-Temperature application components used for aerospace industries always impose thermal problems due to their high corrosion resistance and low thermal conductivity which largely influence the surface integrity during machining nickel-based superalloys. Various techniques for grinding temperature measurement in the contact zone of wheel and workpiece and the effects of process parameters on grinding temperature and induced residual stresses are described in this paper. A comprehensive review has been carried out to control thermal damage due to high heat generation and its correlations with the induced residual stress at the ground surface during grinding of Ni-based superalloys by optimizing the grinding parameters, cooling environments and selection of grinding wheel and suggests new findings.

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Studying Methods of Estimating Heat Generation at Three Different Zones in Metal Cutting: A Review of Analytical models

Cited by 7 publications

References 11 publications

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System

Parametric Study of Inspecting Surface Defects in Investment Casting

Grinding temperature and its consequences on induced residual stresses during grinding of nickel-based superalloys: a review

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