While disruption in this industry is nothing new, technology in manufacturing today is moving faster than ever.
As the global economy has grown, manufacturers face increasing demand to become more productive, agile and affordable. New technologies enable them to achieve these goals, improving virtually every part of the industry. Here are seven examples of technology improving different manufacturing processes.
1. Robotics automating electronics soldering
Automation is one of the most prominent examples of technology in manufacturing. Manufacturers have embraced robotics for decades, but now they’re applying them to more processes than ever. One of the most impactful of these new use cases is soldering electronic components.
Experts predict consumer spending on electronics will surpass $2 trillion by 2023. As consumers buy more devices, they demand more options, additional functions and smaller sizes. These trends have led to increasingly dense and complex electronics, which can be challenging for people to put together.
Robotic arms can perform these sophisticated soldering operations far faster than human hands. They also provide the same level of quality and speed every time, delivering consistent results. With these advantages, manufacturers can solder smaller and more complex electronics without sacrificing time.
2. 3D printing reducing waste in fabrication
Machining has long been the standard for fabricating parts in manufacturing, but this process is wasteful. Since machining cuts materials down to shape, it doesn’t use them in their entirety. The leftover material, called swarf, adds up to 3,391 tons of metal waste annually.
3D printing has emerged as a less wasteful alternative in metal and plastic fabrication. Instead of cutting material away to produce a final shape, it adds it. This additive process lets manufacturers use only what they need when fabricating parts, reducing costs and becoming more sustainable.
These benefits have caught the attention of many manufacturers. Despite 3D printing’s relative novelty, roughly two-thirds of manufacturers have already implemented it to some degree. Many use it to fabricate entire products, not just parts.
3. RFID tags verifying material shipments
Another prominent example of technology in manufacturing is radio-frequency identification (RFID) systems, which first appeared in the 1980s. Today, manufacturers use RFID tags to find or identify parts, materials or products across the entire process. One of its newer and more impressive use cases is in verifying the authenticity of materials.
Counterfeit parts are a $509 billion problem in the manufacturing industry. RFID tags make it easier to identify where packages came from and what they contain. When genuine suppliers assign their products a specific RFID tag, it reveals its authenticity, as counterfeit parts won’t have these identifiers.
Verifying materials with RFID systems before putting them into production lines helps manufacturers ensure product quality. Scanning these tags is also quick and straightforward, so facilities can verify their incoming shipments without taking too much time.
4. Automated material handling increasing safety
Material handling is an integral part of any manufacturing operation, but it can be dangerous. Overexertion and repetitive motion injuries, the most common type of workplace injury, are highly likely when employees have to move heavy materials manually. As workplace safety standards have risen, many manufacturers have turned to automated material handling systems.
Motorized material handling comes in various forms, from automated guided vehicles (AGVs) to treadmill systems. What all of them have in common is minimizing the human factor in this potentially hazardous process. When workers don’t have to pick up or carry heavy objects from one station to another, they stay safe from overexertion.
Automating just this one process can help manufacturers save a considerable amount of lost time and money. In addition to harming workers, injuries are expensive and time-consuming. By motorizing this high-injury process, companies improve safety, productivity and cost-efficiency.
5. Machine learning optimizing smelting operations
Manufacturers in industries like aerospace that rely on high-quality metals need optimized smelting operations. Metal components often require wear and abrasion resistance or meet stringent strength requirements. Providing these qualities means using reliable, high-performing furnaces, which machine learning can help ensure.
Furnaces’ extreme conditions and high standards mean upkeep is a prevalent issue in smelting operations. Machine learning models can analyze performance data to determine when a furnace will need repairs. These analytics enable predictive maintenance, helping manufacturers solve problems before they grow and affect production quality or efficiency.
Machine learning can also look at furnace data over time to notice how different changes affect product quality. It can then suggest improvements to optimize a manufacturer’s smelting process.
6. AI enabling effective rapid prototyping
In today’s fast-paced business environment, manufacturers must release new products quickly to stay competitive. This has led to the trend of rapid prototyping, which often relies on 3D printing. AI can take this process a step further, guiding faster, more effective techniques.
AI features in modeling software spot weaknesses in the digital model, sometimes even suggesting possible fixes. This automatic flaw-checking saves time and money workers would otherwise spend making later adjustments. Teams can produce reliable, working prototypes faster, speeding product development cycles.
These same AI systems can determine which production methods would be most cost- and material-efficient. Teams can produce prototypes even faster and for less money with this information.
7. Machine vision improving quality control accuracy
End-of-line quality checks are essential in any manufacturing operation. As crucial as this process is, it’s often slow and far from perfect. Manually checking for defects is time-consuming, and humans can make mistakes after hours of repetitive work. Machine vision has recently emerged as an effective solution.
Machine vision systems use cameras and sensors to detect defects quickly and accurately. Even highly trained humans need to take time to observe objects close enough to spot all flaws. Robots can work much faster, and since they don’t get distracted or bored, they’re often more accurate, too.
A Heineken bottling facility in France installed a machine vision quality control system in 2018. The system can analyze a remarkable 22 bottles per second with a near-zero error rate.
Technology in manufacturing is reshaping the industry
Technology in manufacturing is experiencing something of a golden age. Innovation and adoption are happening on an unprecedented scale. Manufacturers have a wider variety of technologies at their disposal than ever before, and it’s transforming the sector.
Thanks to technology like AI and automation, assembly is safer and more efficient, and it produces higher-quality products. The scale and speed of modern manufacturing wouldn’t be possible without these innovations.