Automated quality assurance for SMBs

Lacking the technical and financial resources of big enterprises, small to medium businesses (SMBs) might think that machine vision is not a viable option for them. Their perception is that automating quality assurance (QA) is complex and expensive. As a result, many SMBs rely on manual inspection and do not automate. Here Ofer Nir, VP products & marketing of Inspekto, explains why Autonomous Machine Vision represents a new era of industrial quality inspection.

SMBs are facing growing challenges. Smaller production lot sizes, frequent line changeovers and increased competition are pressuring them to become increasingly efficient, agile and productive. This is why many small to medium manufacturers are moving towards automating production. Quality inspection must be automated as well to ensure that it is not a limiting factor in manufacturers’ ability to automate end-to-end.

Current QA technologies and solutions are of limited use to small manufacturers, due to the fact that traditional machine vision projects are notorious for being complex, long and expensive to set up and train, as well as rigid and tailored to the specific part in production. On the other hand, SMBs are known to offer versatility and agility, which means constant diversification in the parts they produce or assemble. The result is that manual inspection is still the preferred QA method in many SMBs.

The shortcomings of manual inspection

Manual inspection comes with a series of pain points. In many cases, this method is based on sampling, meaning that for every inspected item, there are dozens of items that don’t undergo any checks at all.

Moreover, manual inspection is a tedious, repetitive process, so inspectors could get tired or distracted and let their guard down, missing errors. Also, as with any manual task, different people might not have the same quality standards and so inspection may vary from one shift to another.

Finally, some items are simply too complex to be efficiently inspected by the human eye, and hardly visible defects could easily go unnoticed through the QA process.

Autonomous Machine Vision

The solution to the shortcomings of manual inspection lies in automating QA processes with a machine vision system that is cost-effective, easy to set up and train, and flexible enough to tackle a wide variety of QA challenges.

The INSPEKTO S70 Gen.2, the only Autonomous Machine Vision (AMV) system currently on the market, offers all of these characteristics. Unlike traditional machine visions solutions, which are built ad hoc by an expert, the S70 Gen.2 is an off-the-shelf product which includes everything the user needs — camera, lighting equipment, PC, mounting system, software and more. It is ready to use and can inspect a very wide range of products, in a variety of materials, produced through many different manufacturing and assembly processes. It can be deployed to inspect incoming good at different production steps or at the end of the line and can be installed as a standalone station or integrated on the production line.

The cost-effectiveness of AMV systems makes them ideal for small production runs, as well as production lines that are frequently modified and reconfigured to produce new items. These systems can also inspect several different products on the same location, easily switching from one to the other in real time.

Data record

As opposed to human inspectors, AMV systems remember the details of each and every item they inspect, storing images in an archive that manufacturers can consult at any time.

This offers SMBs the invaluable chance to see exactly where and why defects happen and optimise their production processes accordingly. Over time, this can substantially increase production quality and have a positive impact on the bottom line of the business.

To stay competitive in the era of Industry 4.0, SMBs cannot overlook the importance of smart, automated quality inspection. For more information on how AMV can boost quality in your SMB, visit www.inspekto.com.

Automation Update