Smart Surveillance

This manufacturer of LED module bonding machines was in the process of developing large vision machines that comprised 17 industrial PCs (IPCs) in a single machine.They had been testing several IPC brands to identify the ideal one that would improve the stability of their machines. Given the number of IPCs in each machine,having compact IPCs was a major priority for their project.

The municipality of Verona had the exigency to enforce the regulations for commercial vehicles for into the historic city center to reduce traffic, vehicles with permits dedicated to city logistics within certain hours often exceeded the permitted period of stay inside the congestion zone. In addition Verona had the goal to eliminate the falsification of disabled parking passes and increase the on street enforcement. The historic city center of Verona was already equipped with a congestion area system comprising of video cameras, but the ratification of the system limited the functionality of the system. It was not possible to measure time of ingress and egress nor the duration of stay of single vehicles. Once entered, it was not possible to verify if the vehicle spent more than the consented period of time inside the city leading to an increased congestion. For disabled people, the existing solution led to various problems: to access to the city center handicapped drivers or passengers had to inform the municipality up to 48 hours in advance for avoiding a fine and falsificated copies of disabled permits (CUDE) were a frequent problem. A new solution needed to cover all this aspects, while digitizing the related processes to increase efficiency and reduce the workload at the cities helpdesks.

QA automation is a cost-effective solution for manufacturers, as it saves time and money while reducing the risk of defective products. However, traditional QA methods are not sufficient for meeting the rigorous standards of Industry 4.0, and traditional machine vision solutions can be expensive and difficult to implement. BSH, a manufacturer of home appliances, faced this challenge and sought to improve the accuracy and efficiency of their batch inspection process without incurring high costs. They partnered with Inspekto to address the issue and reduce detection time of component defects at one of their oven manufacturing plants in Germany.

In this case study, the challenge explored involves LeNet*, one of the prominent image recognition topologies for handwritten digit recognition.   In the case study, we dive into how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.

Electrode slurry is coated onto copper and aluminium foil to facilitate electric flow. The metal surface, separator, and coating must be inspected for potential surface or edge defects as well as uniform shape and thickness.

Smasoft Technology Co., Ltd. is a System Integrator that develops industrial automation software and offers AI application solutions. Its self-developed AINavi-AOI-Seq automated software platform and AINavi-AOI-Semicon AI defect inspection tool have been well-liked and adopted by the semiconductor and electronics industries. Recently, the company was commissioned by a semiconductor equipment manufacturer that manufactures Extreme Ultraviolet Light (EUV) pod inspection machines to implement defect AI inspection features.The EUV pod inspection machines have built-in AOI software, but can only find defective products, not determine what is causing the issue in the manufacturing process. To make products more useful, the semiconductor equipment manufacturer decided to upgrade their EUV pod inspection machines with AI features.According to the customer's request, Smasoft's AI solution must complete the analysis of 380 images for a single pod within two minutes and inspect different materials at the same time. Consequently, multiple sets of AI models (algorithms) were needed for interpretation. In addition, because the solution needed to be installed in a cabinet in the lower half of the machines, the size and configuration of the hardware had to be constrained due to the limited space.To this end, Smasoft would implement two sets of software, AINavi-AOI-Seq, and AINavi-AOI-Semicon, to distinguish the types and locations of defects through AI, and then classify defects by threshold screening. Moreover, the results would be exported into a test report to facilitate identifying the source of the problem.To meet these software requirements, Smasoft needed to purchase a hardware solution with strong computing performance and stable operation. The solution needed to be compact in size and flexible in configuration to overcome physical space constraints.