|
No-Code Robots Are Essential for the Manufacturing Industry to Easily Adopt Automation Technology
![]() |
|
Extensive programming and engineering skills are often required for traditional industrial robots. No-code robots, in contrast, democratize the process of robotic automation by lowering the barrier to entry for organizations without extensive programming expertise, enabling them to automate repetitive tasks and streamline workflows more efficiently. These platforms often provide intuitive graphical interfaces or drag-and-drop functionality to enable users to design robot behaviors and workflows visually, rather than writing codes.
Here are 3 major barriers to automation when it comes to integrating a traditional robot into a workflow process:
High Initial Cost: Traditional industrial robots often come with significant upfront capital costs for hardware, software, and integration into existing systems.
Complexity of Programming: Programming traditional robots typically requires specialized knowledge of programming languages such as C++, Python, or Robot Operating System. Every robot is different and uses a particular programming language. In order to program each user interface correctly, the programmer must learn the specific language for that brand of robot.
Training Challenges: Introducing robots into the workforce requires training employees to operate and interact with them safely and effectively. Resistance to change can be barriers to adoption, particularly in environments where workers are accustomed to manual processes.
To a large extent, no-code robotic solutions overcome these limitations. Instead of traditional text-based coding, users interact with the robot’s logic and functionalities through visual programming languages or flowchart-style representations. No-code robot platforms often come with a library of pre-built templates, actions, and components that users can leverage to quickly create complex robot behaviors without starting from scratch. With no-code programming, tasks are represented with building blocks that work across different types of robots. Anyone at any skill level can easily learn how to use the system.
With the endless possibilities of integration with AI powered machines, future iterations of no-code robotics platforms enables them to learn from data and improve their performance over time. This could include features such as automatic task recognition, adaptive decision-making, and predictive maintenance.
According to one market research data, by the end of 2024, up to 500,000 industrial robot units will hit factories annually. No-code robotics platforms could play a significant role in enabling the adoption of collaborative robots (cobots) in workplaces and take a significant chunk of the market share. These robots are designed to work alongside humans safely and efficiently, and no-code programming interfaces could make it easier for non-experts to teach and deploy cobots in various collaborative tasks.
With so many advancements in the field of robotics automation, the future for the users and the suppliers of robotics solutions is bright!
|
|
Technology Spotlight: Confocal Imaging Method for In-Line Bottle Wall Thickness Measurement
![]() |
|
An alternative to terahertz wall thickness at-line or lab testing system is the GP Resources, LLC Lateral Chromatic (Confocal) Imaging WTS technology. It has the advantage of testing 100% of translucent plastic bottle line production at line speed of bottles (up to 10,000 measurements per second speed). Using this Patented Line Confocal Imaging method, the surface is illuminated by a near infrared light source integrated with customized optics that focuses different colored lines of light split to focus on the inner and outer walls of the bottle. With its proprietary optics/software this imaging system then reads the distance between these outer/inner wall light beams and automatically adjusts for the bottle distance from centerline while also correcting for any bottle tilt from nominal on the conveyor. The result is to optimize the individual wall thickness repeatability that otherwise would be impacted by the bottle wall test read location varying from a minimal location on the conveyor line. A thickness measurement accuracy of +/- 0.0008″ with +/- 0.0006″ repeatability is achievable. Once the software calculates the actual thickness data, it is then sent to the integrated 24” touch CRT screen for operator review. This technology thus provides ability to measure individual wall thickness versus other older technologies on the market that employ Infrared and light intensity vision based technologies that average the walls 180 degrees opposite each other to provide a nominal wall thickness on a bottle from the two walls instead of the Confocal Imaging providing individual wall thickness measurements on the walls 180 degrees from each other.
For more information on this technology, contact TeTechS Inc. |
|
Future of the Automated Quality Control Solutions with AI-Powered Robots
![]() |
|
The future of quality control automation with AI-powered robots is both promising and transformative, pointing towards significant advancements in efficiency, accuracy, and productivity across various industries.
Here are some key trends and developments expected:
Integration of Advanced Technologies: The integration of AI with other cutting-edge technologies such as computer vision, IIoT (Industrial Internet of Things), and predictive analytics will enhance the capabilities of quality control robots. For example, computer vision enables robots to visually inspect products with high detail, while IoT connectivity allows for real-time monitoring and data collection across the production chain.
Customization and Flexibility: By leveraging machine learning and deep learning algorithms, AI-powered quality control robots will continuously improve their performance over time. They will learn from each inspection task, adapting to new products and defects more efficiently. This continuous learning process will reduce the need for manual programming and intervention, making quality control processes more streamlined and adaptable to changing product lines.
Increased Precision and Consistency: Automated quality control solutions are becoming increasingly precise, capable of detecting and analyzing defects with higher accuracy than human inspectors. Their ability to learn from vast datasets and improve over time through machine learning algorithms means they can adapt to new quality standards and inspection criteria quickly.
Real-Time Data Analysis: AI powered QC automation systems will be capable of analyzing data in real-time, providing immediate feedback to the production line. This will allow for instant adjustments and corrections, significantly reducing the time and cost associated with rework and waste. Real-time data analysis will also facilitate predictive maintenance, identifying potential equipment failures before they occur and preventing downtime.
Enhanced Worker Safety and Efficiency: By taking over repetitive and hazardous inspection tasks, quality control robots will improve workplace safety. They will also free up human workers to focus on more complex and strategic tasks that require human insight, fostering a collaborative work environment where robots handle the monotonous aspects of quality control.
Versatility Across Industries: Future developments will focus on creating more customizable and scalable quality control solutions. Although manufacturing is the primary sector benefiting from such solutions, these technologies will expand into agriculture, pharmaceuticals, automotive, and even food and beverage industries. Each sector will see tailor-made solutions for their specific quality control challenges, from detecting imperfections in materials to ensuring compliance with health standards.
Ethical and Workforce Considerations: As AI-powered robots become more prevalent, there will be important ethical considerations and impacts on the workforce. It will be crucial to address issues related to job displacement, re-skilling of workers, and ensuring that the benefits of technology are equitably distributed.
The future of automated quality control with AI-powered robots is bright, with significant potential to transform industries, improve product quality, and enhance operational efficiency. However, it will also be important for businesses and society to navigate the challenges and ethical considerations associated with the deployment of these advanced technologies.
|
|
The pros and cons of different non-contact methods for measuring thickness in the coating industry
![]() |
|
Advanced technologies are used in non-destructive methods to measure the thickness of coatings and
films without damaging the test sample. The thickness of the coating can be determined by using optical
light, electromagnetic waves, or ultrasonic waves to probe the sample. Some of the non-destructive methods
for measuring the thickness of paints and coatings include:
Magnetic and electromagnetic inductive methods are used to measure the thickness of electrically
non-conductive coatings and films on magnetic and non-magnetic metallic substrates. Magnetic and
electromagnetic methods can accurately measure the thickness of coatings and paints on most metals, but
they are not suitable for non-ferrous metals. Eddy current methods are commonly used to measure the
thickness of paint on non-ferrous metals.
Transparent coatings and paints on reflective and transparent substrates are typically measured using
optical methods in transmission mode . Ultrasonic methods are used to detect the reflected wave by
applying ultrasonic vibration to the paint. The travel time is recorded and by knowing the speed of
ultrasonic vibration inside the material, the thickness is calculated. Total coating thicknesses can be
measured using ultrasonic thickness measurement gauges, which have a measuring range of typically 13 to
1000 microns.
Terahertz waves, a form of light with a wavelength that falls between microwave and infrared, are the
power source of terahertz metrology solutions. Terahertz time-of-flight measurement technique is ideal for
multi-layer thickness measurement in reflection mode for paint and coating applications. The layer
thickness is calculated based on the speed of terahertz waves in the material, hence it is a “true
thickness” with no on-going calibration required. Terahertz waves pass through both opaque and transparent
materials, so unlike optical methods, there is no transparency limitation on paint and coating. Unlike
magnetic and eddy current methods, there is no limitation on the electrical or magnetic properties of the
substrates. Another advantage of the terahertz time-of-flight method is that it can accurately measure the
thickness of each individual layer of a multi-layered coating.
Another area in which the terahertz time-of-flight method prevails is in measuring paint on plastics.
The automotive industry has led to a significant increase in the use of plastics in various industries
over the last few years. Plastic parts often need to be painted for various reasons, such as improving
their appearance, producing parts with matching colors, and improving the stability of plastic surfaces.
The thickness of paint on plastic can be accurately measured using the terahertz time-of-flight method. |
|
8 reasons why plastic bottle manufacturers should use terahertz for their wall check quality control
![]() |
|
Until now, plastic bottle and container manufacturers have been limited by labor intensive (and in many
cases destructive) thickness measurement quality control practices. Techniques such as Hall effect gauges,
which are manual and contact-based, have poor gauge repeatability and reproducibility, and can only
measure overall wall thickness– something that is insufficient when a multi-layer structure is involved.
Cutting and measuring oxygen barrier layers is another ineffective technique, as it is both destructive,
wasteful, and prone to human error.
Here are 8 reasons why now is the time for plastic bottle and container manufacturers to adopt
terahertz-based automated measurement solutions for their bottle wall thickness and barrier layer
thickness measurement quality checks:
1. Measures opaque and transparent materials (HDPE, PP, PET, etc.)
Unlike optical measurement techniques that limit their use to transparent materials, terahertz-based
measurement gauges can measure any type of plastic polymers, even those with opaque properties.
2. Measures overall wall thickness
The true thickness measurement is achieved by utilizing the time-of-flight principle to measure the
wall thickness of the bottles. Optical measurement gauges use material absorption to calculate wall
thickness values, which is an indirect thickness measurement technique that requires on-going calibration.
3. Measures EVOH barrier thickness
Unlike optical measurement gauges that only measure overall wall thickness of the samples, a terahertz
based measurement gauge can measure the thickness of the oxygen barrier layer separated from the outer and
the inner layers.
4. Optimizes expensive EVOH resin usage
Manufacturers can avoid resin overuse by optimizing the amount of resin used in production with
high-precision measurement data on the expensive EVOH barrier layer.
5. Frees up staff time for higher value tasks
An automated walk-away solution handles the tedious and time-consuming task of bottle wall checks,
allowing the operator to focus on more important tasks.
6. Improves on employee safety
The cutting of samples for barrier checks is a significant safety risk for employees and can be a
source of liability for employers. This risk is completely eliminated by a non-contact barrier thickness
measurement solution.
7. Brings high quality data into the process
Manually collected measurement data is susceptible to human error, making it less reliable. Measurement
data collected by an automated system with low gauge R&R provides high-quality data for the production
process.
8. Measures a full batch of samples in a cycle
A full cavity shot of bottle samples can be measured in one cycle with an automated and hands-free
measuring system. The measurement data for the entire batch is recorded and automatically saves to the
database for analysis and reporting purposes. |