Innovative Approaches to Processing Steel: Advances in Machining, Welding, and Sustainability

By Ibtisam AbbasiReviewed by Lexie CornerJan 29 2025

Carbon steel plays a vital role across various industries due to its high strength, excellent machinability, and ability to perform under extreme conditions, including high temperatures. Steel processing is a complex task that has evolved significantly with recent advancements in machining, welding, and sustainable manufacturing techniques.

Image Credit: casa.da.photo/Shutterstock.com

Advanced Machining Techniques

Wire Electric Discharge Machining

Wire electric discharge machining (WEDM) shapes high-strength components by removing material through controlled thermal erosion. A recent study optimized WEDM parameters for AISI 1045 medium carbon steel using the Taguchi L9 orthogonal array, focusing on the production of a timing chain sprocket.

The study found that current significantly influences the material removal rate (MRR), with higher current values leading to increased MRR. Other key factors include pulse-on time and voltage, where an increase in voltage was observed to reduce MRR.¹ While these insights help optimize machining for AISI 1045 steel, different grades may exhibit variations in critical parameters affecting efficiency.

High-Speed Cutting Tool Optimization

Stainless steel cutting tools are prone to wear and abrasion, requiring frequent replacements and driving up operational costs. To address this, researchers have explored advanced cooling and lubrication methods, such as minimum quantity cooling lubrication (MQCL) and minimum quantity lubrication (MQL), to enhance tool longevity and efficiency.

One study examined the effects of MQL on sintered carbide cutting tools with a 3-micrometer resistant coating, used on a conventional lathe machine. The findings revealed an 18 % reduction in wear band compared to traditional cutting methods. The use of an oil mist in the MQL process significantly reduced frictional forces, improving tool life and cutting performance.²

Welding Innovations

Rotary Friction Welding

Researchers have focused on optimizing welding processes for carbon steel. Rotary friction welding (RFW) is commonly used for heterogeneous welds, but challenges arise when joining materials with different forging temperatures and metallurgical properties. For example, welding commercial copper to stainless steel often results in intermetallic compounds that weaken the joint.

In recent research, AISI 1045 medium carbon steel was joined to aluminum (containing 4 % copper) using FW. To improve the weld's performance, researchers introduced a metal powder insert between the materials. This insert, made of AISI 1045 steel and copper, was placed between the steel parts to refine the weld quality.

A mixture of 1.5 g with an initial thickness of 1.6 mm led to a maximum tensile strength of 287.4 MPa, with the thickness reducing to 1.1 mm after welding. The ideal composition of the powder insert was determined to be 47 % AISI 1045 and 53 % copper, with a total mass of 1.41 g. At this ratio, tensile strength improved to 291.8 MPa—significantly higher than the 189.6 MPa achieved without the insert.³ This approach helped reduce weld brittleness, lowered stress concentration, and enhanced the overall reliability of the FW process.

Laser Welding and Hybrid Techniques

Steel-to-aluminum and other dissimilar metal joints are widely used in automotive and aerospace applications to reduce weight. However, a major challenge with laser welding these materials is the formation of intermetallic compounds (IMCs), which can weaken the joint.

To address this, researchers have explored using transition layers and filler wires to improve tensile strength and overall weld quality.⁴ Thin foils of elemental substances, such as manganese, have been found to enhance corrosion resistance in steel-aluminum welds while also reducing the risk of cracking.

Recent studies by Das et al. have shown that incorporating graphene nanoplatelets (GNPs) as a transition layer can significantly improve weld strength—by as much as 50 to 120 %.⁵ By refining tensile properties, optimizing melting points, and enhancing wettability, transition materials and filler wires continue to play a key role in advancing the performance of laser-welded steel joints.

Sustainability in Steel Processing

Energy-Efficient Machining

Milling and turning steel are energy-intensive processes, contributing to nearly 99 % of the environmental impact in machining operations. Research by Camposeco-Negrete et al. focused on optimizing cutting parameters for turning AISI 1045 medium carbon steel to improve energy efficiency.⁶

Key findings identified feed rate and cut depth as crucial factors in minimizing energy consumption. Experiments showed that an optimal feed rate of 0.18 mm/rev, a cut depth of 2.22 mm, and a cutting speed of 366 m/min resulted in a 16 % reduction in specific energy consumption. Additionally, surface roughness improved by 26.3 %, and the material removal rate increased by 3.6 %, demonstrating a balance between efficiency and machining quality.⁶

Recycling Steel for Sustainability

Steel is widely used across automotive, construction, and aerospace industries, making its recyclability essential for sustainability efforts. According to the European Circular Economy Stakeholders Platform, about 90 % of steel is recycled into new products. In 2018, the EU repurposed over 90 million tons of stainless steel scrap, generating cost savings of approximately 20 billion euros.

This large-scale recycling effort contributed to a 60 % reduction in CO₂ emissions and decreased energy consumption in the steel processing sector by 72 %. Recycled steel is also used to produce green concrete and steel bars, significantly reducing pollution. Experts estimate a 40 % decrease in air pollution and over a 70 % reduction in water contaminants, reinforcing the environmental benefits of steel recycling.⁷

Artificial Intelligence-Driven Insights in Steel Processing

Artificial Intelligence for Fatigue Life Prediction

Artificial intelligence (AI), particularly neural networks, is increasingly used to analyze material properties for industrial applications. Recent research by Maleki et al. applied deep learning to study the fatigue behavior of coated AISI 1045 medium carbon steel.⁷

The study examined coated samples with thicknesses of 0, 13, and 19 µm to determine the impact of coating thickness on fatigue resistance. Findings revealed that nickel coatings and warm galvanization at 13 µm improved fatigue life, whereas hardened chromium coatings had the opposite effect. Neural network analysis suggested that the optimal fatigue life for nickel and warm galvanization coatings occurs at thicknesses between 10–15 µm, while hardened chromium coatings resulted in reduced durability.⁸

By applying these insights, manufacturers can enhance the fatigue life of steel components, making them ideal for high-strength applications such as gears, sockets, clamps, and ratchets. In aerospace, these findings support the development of durable landing gears and structural load-bearing parts.

Artificial Intelligence for Process Optimization

Machinability is influenced by factors such as cutting tools, cutting fluids, and cutting speeds. AI-driven platforms, including machine learning (ML) and deep learning (DL), are increasingly being used to optimize steel machining parameters.

A study by Altug et al. employed deep learning and extreme learning machine (ELM) models using Python to predict key machining metrics, including surface roughness (R_a), MRR, and wire wear rate (WRR). The model produced highly accurate predictions:

• MRR: 0.000101 (r² = 0.9444)
• WRR: 0.000037 (r² = 0.9184)
• R_a: 0.012 (r² = 0.9274)

When compared with experimental results, the model achieved an r² value of 0.953 for R_a with a 2 % error margin, while WRR predictions had an r² value of 0.956, with a difference of only 3.5 % from actual values.⁹ These results confirm that predictive modeling can optimize machining parameters in advance, reducing trial-and-error adjustments and improving overall efficiency.

Discover the Latest Innovations in Steel Processing

Advancements in machining, welding, and sustainability are shaping the future of steel processing. Innovations like energy-efficient machining and increased recycling efforts are making steel production more sustainable. The integration of AI-driven technologies is further enhancing process optimization, leading to more efficient and environmentally friendly steel manufacturing.

If you're interested in exploring these developments further, consider reading:

• Forging a Sustainable Future: The Rise of Green Steel Plants
• Recycled Steel and its Role in Sustainable Construction
• Sustainable Materials for Industry and Manufacturing
• Green Hydrogen for Steel Production
• The Role of X-Ray Fluorescence for In-Process Steel Slag Monitoring

References and Further Reading

1. Zaman, U., et al. (2022). Optimization of Wire Electric Discharge Machining (WEDM) Process Parameters for AISI 1045 Medium Carbon Steel Using Taguchi Design of Experiments. Materials. https://doi.org/10.3390/ma15217846
2. Szczotkarz, N., et al. (2021). Cutting tool wear in turning 316L stainless steel in the conditions of minimized lubrication. Tribology International. https://doi.org/10.1016/j.triboint.2020.106813
3. Bouarroudj, E., et al. (2023). Improved performance of a heterogeneous weld joint of copper-steel AISI 1045 obtained by rotary friction using a metal powder insert. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-022-10326-9
4. Zhao, Y., et al. (2022). Research progress of transition layer and filler wire for laser welding of steel and aluminum dissimilar metals. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-021-08442-z
5. Das, T., et al. (2020) Resistance spot welding of dissimilar AISI-1008 steel/Al-1100 alloy lap joints with a graphene interlayer. J Manuf Process. https://doi.org/10.1016/j.jmapro.2020.02.032
6. Camposeco-Negrete, C., et al. (2019). Sustainable machining as a mean of reducing the environmental impacts related to the energy consumption of the machine tool: a case study of AISI 1045 steel machining. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-018-3178-0
7. EuRIC. (2020). Metal Recycling Factsheet. [Online] EuRIC. Available at: https://circulareconomy.europa.eu/platform/sites/default/files/euric_metal_recycling_factsheet.pdf [Accessed on: December 20, 2024].
8. Maleki, E., et al. (2022). Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings. Journal of Marine Science and Engineering. https://doi.org/10.3390/jmse10020128
9. Altuğ, M., et al. (2023). Optimization with artificial intelligence of the machinability of Hardox steel, which is exposed to different processes. Sci Rep
10. . Available at: https://doi.org/10.1038/s41598-023-40710-8

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.