ManufacturingMachine Learning in Robotics

Companies are in a race to embrace new technologies to drive digital transformation and enable industry 4.0. The potential of artificial intelligence (AI) and Machine Learning is huge, but so are the challenges.

Artificial Intelligence will become a key factor in the manufacturing space. According to a review by BCG and MIT Sloan Management 84% of executives expect AI to enable them to obtain or sustain a competitive advantage. By allowing machine learning algorithms to utilize IoT, sensor data and images, configurations can be implemented in real-time to further optimize production facilities and reduce downtime to a minimum, improve quality, reduce cost and accelerate time to market.

AI and Smart Robotics

In this context, automation and robotics set the precedents and groundwork for using AI. The common applications of robotics can be categorized by the following:

  • Product Assembly – One of the most common applications of robotics in contract manufacturing is in the assembly of products. Robotic manipulation of components in this environment is difficult. However, when paired with computer vision systems and products designed for automation, accurate distinctions can be made with greater consistency and efficiency. Examples here would be Printed Circuit Board Assembly (PCBA) or higher level assemblies, such as enclosures or single use medical devices.
  • Production Processes – Leveraging robotics for a single process across multiple products is another great use for industrial robotics. Processes in the electronic manufacturing industry such as bonding, fastening, welding, soldering, spraying and masking help to create repeatable actions which are helpful in controlling key process or performance indicators.
  • Assembly and Process Inspection – Using computer vision with a robot can provide inspection for robotic assembly or robotic process to ensure the value intended to be added by the robot has indeed been added. By using a robot to move around a camera, the limitations of 2D inspection can be mitigated by taking images at multiple angles with multiple lighting applications. Furthermore, advanced 3D systems metrology and digital inspection tools significantly increase product and manufacturing quality, rapidly identify process issues and build productivity.
  • Machine Tending and Packaging – Automation is an everyday element of the packaging industry. From pick & place to automated palletizing, robotic systems are making packaging faster, flexible and easier. Performing mundane, repetitive tasks for workers they improve product quality and worker safety.

Automated Optical Inspection (AOI) for PCBA

Leveraging AI in the manufacturing space sometimes seems to lack practical feasibility. By focusing on applying machine learning to one robotic application at a time, manufacturers can fast-forward the implementation of new technologies and use it as the starting point to evaluate potential further investments. Leveraging AI algorithms in machine vision inspection, for example, has become a way of incorporating this trend into 3D AOI. This key technique is part of the manufacture and test of electronics printed circuit boards (PCBs). In general, computer vision is used to inspect images before the boards enter the solder process. Those assembly line systems require skilled engineers to build and hard-code algorithms to help the system identify good and defective components – each and every time a board is introduced for the first time to a production line. In addition, the algorithm programming has limitations and is only as robust as the engineer is talented at creating it. And while effective, the systems usually lack the ability to learn or adapt. Still today, algorithms frequently mistake good assemblies with bad assemblies and deliver "false-calls", which impacts the efficiency of overall production. In fact, the false positive rate can reach as high as 30%, resulting in “humans in the loop” to check component diagnoses for hours each day.

Today, manufacturers are faced with new AOI challenges:

  • False Call Reduction - More than 50% of false calls were due to inappropriate programs (un-optimized), partly due to inexperienced programmers or human error. Auto-programming is expected to overcome such programming challenges by setting optimization thresholds.
  • Escape Reduction - While false calls are not actually defective, escapes are defects that are not indicted. In a good AOI system, both of these should be low. AOI escapes can be divided into machine escapes and review escapes. Machine escapes, a direct escape from a machine, can be minimized with auto-programming. Review escapes, an escape from a review operator during image buyoff, can be minimized with auto-buyoff.
  • Programming Time Reduction - Auto-programming can help speed up AOI programming time for NPI (New Product Introduction). Machine learning algorithms can replace the original engineered algorithms and are able to learn the nuances and parameters which define a “good” product, thus reducing the number of false calls and identifying defects to prevent escapes.

It is recommended that an integrated AI library is built internally via machine learning.  This library generates the output of threshold values, inspection requirements and the pass/fail criteria for various types of component packages and enables AOI auto-programming and AOI auto-buyoff. Once at scale on all inspection machines, the data, or training set, will continue to grow and the algorithm will gain accuracy. The continuously compounding dataset will train the machine learning algorithms and continue to produce better results.

Building the IT Framework

In order to ensure that this process is valid for production, it’s necessary to store very large images from the production floor to create the database that will eventually be a training set. Today, in most factories, most of the implemented databases simply do not have the capability to adequately store such large amounts of data. A team of database analysts, control engineers, and machine learning experts have to work closely together on a proof of concept database and storage hardware infrastructure. Furthermore, manufacturers of security-critical applications such as aerospace, defense and healthcare have to weigh issues with compliance and data security and protection.

The validation process surrounding these platforms when upgrading to Machine Learning based algorithms has some risk as well. Manufacturers have to determine the best method for recreating the IQ, OQ, PQ (Installation Qualification, Operational Qualification and Performance Qualification) processes. Industrial standards around machine learning will need to be investigated to ensure that customer’s regulatory compliance protocols are all met. The value of machine learning diminishes if the system cannot be validated effectively.

Focusing on one application area to roll out a production wide AI initiative can pay off in the long run. The automation and robotics team is able to collect information and scope programs for additional robotic systems to benefit from similar algorithm development. Leveraging the success of each narrow machine intelligence victory should result in a more robust production solution – not only for assembly and process inspection but across all four of the previously mentioned categories.

Contributing authors of the article include:
Kirk VanDreel, Sr. Dir. Manufacturing Technology | Neenah, WI
Dawson Tan, Sr. Staff Engineer | Penang, Malaysia

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