Artificial Intelligence is one of the leading movements that is powering the technological revolution we are in today. The takeover is already present in almost\u2014if not all of the aspects of our lives, societies, and industries.<\/p>\n\n\n\n
With the ongoing research and development surrounding artificial intelligence, it is branching out in several fields as an essential asset. Surface technology is one of the beneficiaries of this advancement, which has improved drastically over the years thanks to AI.<\/p>\n\n\n\n
There are numerous ways artificial intelligence has helped surface technology reach optimal efficiency, which we will elaborate on here.<\/p>\n\n\n\n
Artificial intelligence in Surface Technology<\/strong><\/p>\n\n\n\n Dr. Ralph Wilken, head of Plasma Technology and Surfaces at Fraunhofer IFAM<\/a>, thoroughly captures AI’s modifications in the material industry, specifically in pretreatment processes.<\/p>\n\n\n\n Surface Pretreatment Monitoring<\/strong><\/p>\n\n\n\n Surface pretreatment requires immense precision and control, which is challenging to regulate manually. These processes, as well as laser decorating as preparation for painting or bonding, are vital prerequisites for further finishing procedures. Therefore, a highly-automated system is needed to employ these methods excellently.<\/p>\n\n\n\n In an effort to prevent any fluctuations and mishaps, manufacturers such as IFAM<\/a> have been looking for AI solutions to make surface pretreatment self-adjusting and safer. Moreover, the utilization of AI in acoustic and optical monitoring systems is one of the most promising ways to accomplish this.<\/p>\n\n\n\n Surface Pretreatment Control<\/strong><\/p>\n\n\n\n In addition to monitoring surface pretreatment, controlling it is just as essential in perfecting the final product. Artificial intelligence contributes to this aspect of creating material by facilitating its automation.<\/p>\n\n\n\n To understand the role of AI further, IFAM described the step-by-step process. First, there must be an input of necessary information, such as process parameter values and product properties. Next, all the relevant data must be programmed within the AI algorithms to determine the correlations and allow the technology to learn. Then, the AI would be equipped to detect any deviations and anomalies in real-time and offset any malfunctions. As a result of these actions, ideally, a reproducible surface condition would be obtained.<\/p>\n\n\n\n Laser Decorating<\/strong><\/p>\n\n\n\n Laser technology is highly maximized in surface modification. For example, it is used in removing coatings, which is a process that needs optical guidance. But, again, when done manually, it would be virtually impossible to be accurate and safe, whereas the addition of AI would result in high precision and automation.<\/p>\n\n\n\n Dr. Wilken<\/a> continues elaborating on AI’s benefits in laser technology surface modification. A laser is usually set with specific parameters and variables to enable the recording of data. AI tools bridge the gap between the desired output and the signals of the final component. Consequently, this leads to maximum treatment process performance.<\/p>\n\n\n\n Acoustic Monitoring<\/strong><\/p>\n\n\n\n Multiple surface types are susceptible to deterioration, leading to the product’s eventual demise. It usually starts with minor abrasions that are fortunately detectable via acoustic monitoring. Most manufacturers use noise detection via microphones to correlate external pressure and friction coefficients paired with AI technology for the best tribological process.<\/p>\n\n\n\n Other Material Pretreatment Processes<\/strong><\/p>\n\n\n\n Material manufacturers constantly seek ways to improve their surface technology and incorporate AI in operations. They do so as they are aiming for safety and automation. With AI, pretreatment processes can execute extreme precision and accuracy. Here are some of the ways industries utilize AI for surface modification in their systems:<\/p>\n\n\n\n \u00b7 Managing and monitoring methods<\/p>\n\n\n\n \u00b7 Sensory processes<\/p>\n\n\n\n \u00b7 VUV technologies<\/p>\n\n\n\n \u00b7 Digital Solutions<\/p>\n\n\n\n Plasma Coating<\/strong><\/p>\n\n\n\n Plasma coating<\/a> is a process that needs a maximum energy input to function optimally, mainly since it acts as a thermal barrier only brought about by high temperatures. With that in consideration, it is an industry struggle to develop efficient models since plasma coating is not yet thoroughly researched.<\/p>\n\n\n\n Bobzin et al. (2021)<\/a> define thermal spraying processes in the journal of thermal spray technology as complex procedures that consist of several elements. First, they rely on nonlinear interdependencies, particle properties, and coating structure to perform excellently. Additionally, the team of researchers verified the need for advanced artificial intelligence to measure the correlations and understand the interactions more.<\/p>\n\n\n\n Before AI had become an integral aspect of surface technology, traditional methods took up so much time and made plasma coating tedious and taxing. Not to mention, it costs companies more than it would with the more sophisticated technology they use nowadays.<\/p>\n\n\n\n Typically, classic modeling approaches are pursued to understand these interactions. However, while these approaches can capture very complex systems, the increasingly sophisticated models have the drawback of requiring considerable calculation time.<\/p>\n\n\n\n