Using AI in manufacturing processes surges quality and design
The addition of AI in manufacturing leads to increased workflow -- from design to production. Production plants are turning to technology to supplement the manufacturing skills gap.
In the manufacturing sector, which many think is slowing, AI is injecting much needed productivity and advancement. AI-powered bots are becoming ubiquitous on factory floors, and computer vision and machine learning software implementations are improving quality control, safety and maintenance of development and manufacturing processes. With over 50% of manufacturers planning to increase AI spending in the coming years, according to Forbes Insights research, the industry is leaving behind its stagnant reputation to dive into automation.
A lights out factory
AI aids the goal of a lights out factory -- one that operates with minimal, if any, human interaction, with reduced lighting and environmental controls. The intention behind using a lights out model for manufacturing plants is to run in an almost completely autonomous mode. Manufacturing already has an autonomous base -- semiconductors are manufactured to be sovereign to comply with requirements for clean rooms and stable operation procedures.
The concept is being expanded throughout the manufacturing enterprise. The Japanese Fanuc factory has robotic workers operating 24/7 with no fatigue and significantly reduced error, outnumbering and outperforming human workers. In much the same way that robotics has revolutionized logistics and warehouse operations -- especially for e-commerce establishments of scale -- completely autonomous robotic manufacturing facilities are revolutionizing the production process.
Improving quality control
AI in manufacturing processes can be complex and difficult to perfect, especially technically complicated products or materials that demand high precision and tolerances. Manufacturing companies use automation to improve quality control through automated visual inspection tools for detecting defects on production lines. This equipment improves production time and reduces defective products.
If manufacturing is plagued by minor defects, AI is the remedy. Visual inspection machines can detect faults with better accuracy and at speeds much faster than humans. Using automated quality control can replace humans who manually spot defects -- a process which is often error-prone.
AI systems can not only detect errors, but help prevent them. AI-enabled predictive maintenance systems self-monitor and report manufacturing issues in real time. Sensors attached to critical equipment gather data about the production process, and algorithms analyze the data to head off problems before they occur. AI algorithms are able to notify manufacturing teams of emerging production faults that are likely to cause product quality issues. The systems can also formulate predictions regarding asset malfunction, drastically reducing costly unplanned downtime.
Helping with the design process
In addition to the physical creation of products, AI is changing the way products are designed. With generative design, AI systems explore all possible designs for a given problem and generate design alternatives to create products that meet goals set by the engineers. Those goals can include material usage and efficiency, structural strength, weight and operation. The process is then repeated until an optimal design solution is reached. The generative design process creates multiple options to explore -- more than human engineers could create -- including unique designs and shapes. As an example of this process, Stanley Black & Decker created a hydraulic crimper for electrical line workers that shed three pounds, while retaining all the material strength.
Creating safer working conditions
Factory floors lined with industrial robots are unsafe working environments for humans. Most industrial robots can apply crushing force to objects and people with no cognitive awareness of humans around them. Most worldwide safety organizations require separation of industrial bots from humans to operate AI in manufacturing safely. However, AI-powered bots are more capable and better at engaging with humans than those traditional industrial robots. The collaborative robot, or cobot, is a small, nimble and spatially aware bot that can operate in close proximity with human operators.
Rather than being preprogrammed to perform a specific set of steps, many cobots are trained by moving the bot arm to demonstrate a repeatable task. The cobot remembers the movements and repeats them, optimizing the task to achieve increasingly better outcomes. Cobots usually have sensors and computer vision to detect and avoid obstacles, enabling them to safely work with humans.
Manufacturing facilities are taking advantage of self-driving smart transport robots to navigate manufacturing floors and send out communications on any critical situation they detect. These bots roam small or unsafe places for humans and can send out alerts and either self-correct or have a human operator intervene. Bots can transport heavy or unwieldy equipment, provide mobile support to stationary industrial bots and otherwise help turn the human-dominated manufacturing floor into a bot-centric one.
AI processes in manufacturing will continue to play a crucial part in the development and production of products and materials in the evolution of the sector. Manufacturing organizations that don't adopt AI in their processes will rapidly be at a competitive disadvantage, as manufacturers will continue to automate the mundane, routine, unsafe tasks to free employees to pursue higher-value tasks.