AI for Sustainability in Metal Production
The metal manufacturing industry plays a prominent role in the global economy and has always been the frontier of technological innovation. It is also a primal industry
The metal manufacturing industry plays a prominent role in the global economy and has always been the frontier of technological innovation. It is also a primal industry that requires enormous amounts of heat and energy. Hence, it isn’t the industry that comes to mind when discussing energy efficiency and sustainability.
Nevertheless, the metal production industry, especially the steel industry, is evolving to lower its carbon footprint and improve its eco-credentials by employing innovative technologies to decrease the environmental impact of its manufacturing output.
Besides energy consumption, another issue that surrounds the industry today is the scrap pile of metal waste. One of five casting components ends up discarded as scrap in the metal casting process. While defective metal is recyclable, it takes time and energy to rework. With a lofty goal of zero waste—material, energy, and more—the metal industry is hyper-focused on reducing defects during manufacturing and improving sustainability.
Leveraging AI to achieve sustainability in metal production
Artificial intelligence can solve several issues that are critical for sustainable manufacturing. This includes excessive use of certain materials, redundant production of scrap waste, inefficient supply chain management, logistics, and unequal distribution of energy resources. Most importantly, manufacturing entrepreneurs will not have to invest in numerous solutions because AI alone can eradicate all difficulties, as mentioned earlier. AI can analyze specific data and accurately predict the expected output, thus eliminating excessive material use or waste. Additionally, AI algorithms may be set to make precise recommendations to strike a balance in energy use. Artificial intelligence can benefit supply chain management and logistics with demand forecasting, improved communications, and real-time decision-making solutions. Artificial intelligence is an advanced technology that has the potential to transform the manufacturing industry fundamentally. Leveraging AI can create an efficient and transparent supply chain with significantly decreased operational friction. Moreover, AI is the backbone of efficient quality control systems that instantly identify even the slightest deviations and inform of possible failures in advance
By implementing AI-based technologies like Tvarit’s industrial AI solution, TIA, manufacturers can predict, detect and resolve errors in the die casting process and hence, reduce metal scrap. Most importantly, artificial intelligence will soon create unparalleled working opportunities and forge the path towards intelligent, efficient and sustainable manufacturing.
Technological advances are boosting the industry’s efficiency in other ways, too. The fourth industrial revolution allows technology to work in ever-closer harmony with different aspects of metals production, transforming the way steel is made.
As well as converting traditional production environments into highly automated “smart” plants, digitalization enables the different parts of the steel manufacturing process to interact and perform at their full potential.
A digitalized plant’s production management systems use sensor technology, digital production planning tools, and sophisticated AI-driven diagnostics to monitor each intelligent component. Output is optimized for maximum overall performance, and, as part of this process, each function within the plant is continually analyzed and refined for incremental improvements inefficiency.
Future systems will use machine learning to discover the optimum way to produce steel with minimum resources.
Along with efficiency improvements, this will also help reduce the environmental impact of steelmaking across the whole manufacturing journey.
Advantages of artificial intelligence in the metal manufacturing industry
- Traceability of energy consumption per component.
- Identification of potentials for reduction of waste energy and emissions per component
- Quality assurance
- Improved OEM supplier ratings
- Measurable decrease in energy consumption
- Reduction of CO2 emissions and environmental footprint
- An important step towards Industry 4.0