Artificial Intelligence Trends Changing the Industrial Landscape
It’s easy to say that you need to increase the number of digital solutions your business uses. However, many people feel overwhelmed when determining which programs would best help their business. Don’t let the availability of options intimidate you and prevent you from adding digital improvements to your enterprise. Keep reading to learn about some of the biggest AI trends in the industrial sector and how they can benefit your business.
Rise of the Machine: AI and Robotics
When most people think of AI applications, they think of software programs, but synthetic intelligence is also used with specialized hardware. Robots are gaining traction in the industrial sector and revolutionizing the workflow of manufacturing plants and factories.
Artificial intelligence allows industrial robotic systems to perform tasks correctly and repeatedly without the increased error rate employees experience as they become fatigued. The robots utilized in an industrial setting are not making decisions or thinking independently; instead, their intelligence technology allows them to automate highly specialized tasks, such as assembly, packaging, and quality control.
Robotic Processing Automation
The term robotic processing automation is frequently used concerning robotics operating with manufactured intelligence systems. When used in this context, the term refers to automating tasks for software, not hardware. By applying the principles used to design assembly line robots to software systems, tasks pertaining to the business aspect of manufacturing can be automated.
Processes that can be automated via software applications include file migration, form completion, data extraction, inventory management, and processing. In the future, we can expect to see the manufacturing industry handle more complex business elements this way, resulting in lower error rates and increased productivity.
Computer Controlled: Intelligent Quality Control
Quality control practices have been vastly improved by advancements in artificial intelligence. Since even the most conscientious workers will still make mistakes, and robotic production technology can still err, having a modern quality assurance method in place prevents flawed products from exiting the factory. Defective products cost money, and the expense increases the further they travel down the manufacturing chain.
Catching faulty items before they leave production is essential to minimize the costs associated with defects. Traditional quality control methods involve having an employee monitor the line for defective items. However, things will still slip past due to human factors like eye and mental fatigue or distraction. Synthetic intelligence provides a level of quality control that employees cannot match.
Pairing hardware like high-resolution cameras and loT sensors with synthetic intelligence is the best way to detect defective products accurately and efficiently. AI software can spot a defective item immediately and instantly decide what to do with it. Not only does this save money, but it can improve customer perception of a brand. Brands with a reputation for only providing perfect products will easily dominate brands with mediocre quality control methods.
Communication Is Key: Reporting and Support Chatbots
Utilizing a manufactured intelligence domain designed to emulate natural conversation allows employees to report issues and request support more easily. For example, when workers communicate their problems and questions to a chatbot, artificial intelligence can assist them in filing the correct reports using a user-friendly form. Not only does this give employees increased accountability, but it also makes the reporting and inquiry process more straightforward.
Synthetic intelligence can also be used to evaluate and optimize the operations of the whole business. NLP is a manufactured intelligence program that turns text or voice communication into data that can be analyzed. This process is called web scraping, and it allows for the collection of relevant industry information, as well as transportation, labor, and fuel costs. When utilized appropriately, this intelligence can significantly improve operations business-wide.
Optimal Performance: Condition Monitoring
Heavy hitters in the manufacturing industry can’t afford to have their machines operating less than optimally. However, using people to monitor machine conditions presents numerous difficulties and isn’t the most optimal use of funds or staffing. An alternative to an employee physically checking each machine is self-governed machine monitoring. Machines outfitted with this technology perform status checks on themselves using various equipment.
The information collected by data sensors, microphones, vibration detection equipment, and temperature sensors combines to create a real-time health profile of the machine. The ability of synthetic intelligence to adapt and improve monitoring systems enables equipment to pick up on warning signs earlier. In addition, addressing issues when they are as small as possible prevents manufacturers from experiencing decreased machine output and more costly repairs.
Since one of the most recognizable signs that something is amiss with manufacturing machines is the presence of irregular vibrations, self-governed systems that detect vibration variations precluding mechanical issues can catch mechanical problems immediately. This awareness enables action to be taken before production is adversely affected.
Equipment that runs for lengthy periods, in extreme conditions, or requires a particular temperature range for peak performance will benefit from temperature sensors. Whether sensors alert staff about temperature issues or perform an automatic shutdown, temperature sensors that work without human direction can prevent machine malfunctions.
Microphones, or sonic sensors, listen for sounds of friction that indicate insufficient lubrication and mechanical breakdown. When sensors are used in isolation, they can monitor vibrations and mechanical noises from an individual machine. When manufactured intelligence is paired with these sensors, the ability to recognize sound patterns that correlate with mechanical failure or deteriorating lubrication can identify issues before they escalate into serious problems.
When equipment operates under conditions or in a location that is unsafe for humans to monitor, machine vision systems can visually evaluate the state of the equipment. Using “electronic eyes” to assess remote conditions is also a cost-effective method of inspecting machinery that would otherwise go uninspected due to the difficulty and expense associated with accessing it.
Condition Monitoring Analytics
The data collected by these systems must be organized and analyzed to be genuinely valuable. Having all the sensors communicate and compile data together allows digital intelligence programs to learn from the information and adapt their actions for improved performance.
Individuals in the industrial sector can expect self-monitoring programs to become the norm as the prevalence of manufactured intelligence programs increases. The benefits gained from preventing downtime, machine breakdown, and unnecessary staffing are compounded in large-scale operations, making the implementation of self-monitoring systems the logical choice for any company that is too large for traditional equipment monitoring.
Techniques Gaining Traction in the Industrial Sector
All three of these technologies are manufactured intelligence techniques used in the industrial sector for various purposes. These techniques make the trends discussed above possible and can serve as stepping stones to future intelligence systems.
Machine learning is a technique in which an algorithm learns from training materials to recognize patterns and make decisions in real time. As machine learning grows more common in the manufacturing industry, companies have begun to realize this technique’s potential to overhaul traditional manufacturing operations.
This technique uses “artificial neurons” to receive input information and hold it in an input layer. From there, the input will be passed to a hidden layer that evaluates and grades the input and sends it to the output layer as appropriate.
Deep learning is the process of software mimicking the human brain, similar to how neural networks operate. However, in this intelligence method, data travels from layer to layer for higher processing.
As application development continues, fully automated factories, product concepts designed entirely by AI, and improved data collection in the industrial sector will become more prevalent. Furthermore, with more innovative trends becoming mainstream, companies will fully embrace utilizing intelligent hardware and software in all departments.
At SAAB RDS, we help you implement the technology you need to thrive in the present and prepare for the future. If your company hasn’t begun exploring how a digital transformation can improve your business, it’s time to make a change.
Access the technology your company needs to reach the next level. Reach out to SAAB RDS and find out how our team can create a custom digital transformation plan for your company.