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Bar code identification with the best recommendations from AI

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Using Artificial Intelligence (AI) can be very worthwhile when it comes to identifying the bar codes on goods. Interfering factors can be identified quickly and easily both during commissioning of a system and during operation.

No need for time-consuming searches

Bar code readers are sensors used to identify goods and materials in production or logistics. They do so by detecting bar codes that meet one of a number of standards and then supplying the IDs of the bar codes to a superior system. When using these devices in automated applications, the main objective is to achieve the highest possible reading quality: Essentially, when bar code readers detect the labels, the quality with which they perform this task varies, and this quality can be indicated as a percentage. The percentage relates to the contrast detected. If the value is below a certain threshold, the label is no longer read. One challenge faced by system operators is to find bar code readers as quickly as possible when they are no longer providing sufficient reading quality, and to determine the reasons for this – without additional data regarding the possible sources of error, this can be a time-consuming task. Particularly in large systems, for example in intralogistics, that have up to 1,000 bar code readers and kilometer-long transport routes, the search is like looking for a needle in haystack: If in doubt, a technician must trace the entire route of a transport material in order to identify a poorly aligned sensor or the interfering factors in its direct environment, all while under time pressure. The situation is made worse by borderline cases, such as when the bar code reader is somewhat aligned and reads successfully most of the time, but occasionally does not detect labels. This may be because the bar code reader is slightly inclined or only reads in the border area, or other factors may play a role, for example labels of insufficient quality.

Factors that influence reading quality

However, generating corresponding data to find the causes of errors using the bar code reader itself is only possible under certain circumstances. It is true that the sensors monitor their own status and transfer data to the superior system via OPC UA if required. However, this self-monitoring has only very limited functionality – a sensor only considers its own view. This means that it sends information such as “I’m currently reading,” “Excellent reading,” or “Very poor reading” – i.e. its calculated percentage reading quality. The reason for the poor reading quality cannot be identified by the individual device. There are three possible influential factors in this case: The device itself, the bar code label and interfering factors in the environment. Possible sources of error relating to the bar code reader itself include poor alignment to the labels to be detected or a technical fault. In turn, labels can be damaged, soiled or poorly printed, which, depending on the degree of damage or printing quality, may only reduce the reading quality or may prevent identification entirely. Interfering factors in the environment include vibrations, dust, and glare caused by sunlight or emitters in the background. Humidity, for example in cold stores, can be an interfering factor if this causes fogging on the scanning window of the bar code reader.

Artificial intelligence provides the context

AI can help to distinguish the various causes from one another and in doing so identify the reasons for interferences or poor reading quality. Leuze is working with an automobile manufacturer to develop a solution that enhances sensors with data from the overall context. The advantage of this is that the bar code readers remain operational as usual without additional work being generated for the customer during installation. The data volumes are large: Many labels pass by many bar code readers during the process and are read at various installation locations. This is where the overall context comes from. In mathematical terms, this overall context can be described as an equation with many unknowns – countless bar code readers, labels that crop up even more frequently and the various installation locations of the readers. At every station and for every label there is a different result in terms of reading quality percentage. AI solves this complicated equation system and answers the questions about whether a poor reading quality occurs always with a particular bar code reader, only with one label or a particular label type or always at a particular installation location.

Machine learning via recommendation algorithms

To achieve this, Leuze uses recommendation algorithms, i.e. AI-based recommendation methods. These are the same methods that are used by streaming services, for example, to evaluate user behavior and recommend corresponding films or series based on this analysis. In this user behavior analogy, the bar codes correspond to the films and the bar code readers to the users of the streaming services. The recommendation algorithm rates a label as more or less “attractive” for different bar code readers. In this way, it is possible to determine which sensor or which label with a certain percentage is “unattractrive”, i.e. borderline or noticeably problematic.

Per edge device or cloud

In technical terms, an AI-based solution of this kind can be implemented via edge devices or a cloud, depending on the customer requirements and the respective system. An edge device is a separate device that is located in the vicinity of a sensor group and gathers, analyses and passes on the data of the sensor group. Multiple edge devices can be connected to one another. Since an edge device is capable of two-way communication, not just gathering and evaluating data but also sending the analysis back to the sensors, a bar code reader can also pass on this information and report that there is a problem. The advantage of this is there is no need to make any changes to the IT architecture of the customer. Alternatively, the solution can be operated via a cloud if data from separate locations is to be merged.

Significant potential for savings

Leuze’s approach of using AI-based recommendations to identify errors offers huge advantages both during commissioning and during operation of a system. Fast commissioning saves time and money. In this case, it is useful if the causes of poor reading quality are identified immediately. During operation, this method enables predictive maintenance. This means that if a shutdown will soon be required, system operators can take suitable measures in good time and, for example, manufacture and outsource in advance so that they can continue to supply their customers. In some cases, data from multiple years can be used to facilitate this early detection. In addition, the system learns continuously. Therefore, using AI is always worthwhile when it comes to quickly and reliably identifying factors that interfere with the identification of bar codes on goods.

Genel

Researchers to Develop Solid Lubricant Coatings for Conveyor Systems

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A research and development team led by Min Zou, professor of mechanical engineering and an Arkansas Research Alliance Fellow, has received a $550,000 grant from the National Science Foundation to develop low-friction, durable, graphite-lubricant coatings for industrial conveyor systems.

Belt conveyors comprise about a quarter of the $7.65 billion global conveyor market, which has expanded significantly in recent years because of e-commerce. However, an enormous amount of energy is wasted in these systems. High sliding friction between conveyor belts and slider bed materials is responsible for more than half of the total energy losses in a flat conveyor system.

The researchers will develop novel graphite coatings that will significantly reduce energy consumption and equipment failure in conveyor systems. The research will also deepen a fundamental understanding of the novel coating technology to enable applications in other fields, which could lead to significant savings in many U.S. industries.

The technology is based on a unique, patented bonding approach, developed by Zou’s group, in which graphite coatings adhere tightly to a substrate material.

After developing and optimizing fast-coating deposition processes for conveyor materials, the researchers will build scalable coating processes for full-sized belt conveyors. They will then build a prototype for evaluating the coating performance and demonstrate the feasibility of the coatings for industrial applications.

The new project is a collaboration between university researchers and industry leaders. Zou’s team at the U of A will partner with researchers at Arkansas State University and Hytrol Conveyor Company Inc., the largest conveyor manufacturer in the U.S.

The researchers use a special machine to apply novel graphite coatings that will reduce energy consumption and equipment failure in conveyor systems.

Robert Fleming, assistant professor at Arkansas State; Ty Keller, Hytrol’s manager of product innovation; and Boyce Bonham, Hytrol’s chief engineer, will serve as co-principal investigators.

The project will support a doctoral student at the U of A, who will serve as the entrepreneurial lead, a master’s student at Arkansas State, and undergraduate students from underrepresented groups. They have benefited from site and national NSF I-Corps training and Office of Entrepreneurship and Innovation support and training, as well as mentoring by Cynthia Sides, assistant vice chancellor for research and innovation at the U of A, and Douglas Hutchings, director of the Arkansas Research Alliance Academy.

Zou’s research focuses on nanoscale materials and manufacturing. She is an international expert on surface engineering and tribology — the study of friction, wear and lubrication in the design of bearings and interacting surfaces in motion. Zou has designed, refined and tested solid lubricant coatings for various applications. The coatings are thinner, more durable and environmentally superior to petroleum-based oil lubricants.

Zou holds the Twenty-First Century Chair of Materials, Manufacturing and Integrated Systems.

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Dematic automates warehouse of kitchen manufacturer Schmidt Groupe

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Dematic has automated the picking warehouse of French kitchen manufacturer Schmidt Groupe S.A.S. at its site in Lièpvre in Alsace. The automation technology provider developed a space-saving solution, featuring a multishuttle system for multiple-deep storage in nearly 6,000 locations as well as special tote conveyor technology. Within the multishuttle system, 12 shuttles control order sequencing. The conveyor system z requested items to various workstations. In this way, the Dematic solution provides complete automation of the previously manual storage and retrieval operation as well as picking goods for the entire material flow.

“The Dematic Multishuttle significantly increases speed, storage density, accuracy, and availability within the picking warehouse,” says Boris Herrmann, Process Manager at Schmidt Groupe S.A.S. “In addition, the overall system enables high throughput rates as well as error-free picking, guaranteeing us efficient and reliable order processing.” As one of the international market leaders for furniture manufacturing and distribution, Schmidt Groupe faced several intralogistics challenges. For example, managing the side panels, doors, fittings, and handles of a customized kitchen required more effective processes, so the company decided to automate the material flow at its Alsace site.

Order picking starts at the small parts workstations. There, operators put required parts into cartons using a pick-by-light system. Cartons are then transported to the subsequent stations by conveyor using special roller and belt conveyor technology that support the logistical processes. If larger items are needed for an order, the small parts carton is loaded onto a tray and stored or buffered in the Dematic Multishuttle, which provides space for 5,760 storage locations on 12 levels. Within the multishuttle system, a dozen shuttles handle automatic order sequencing as well as storage, transfer, and retrieval. The conveyor system then transports the filled totes and trays to the other workstations. A continuous scale checks the weight. When an order is complete, the sequenced totes are checked, cartons are closed, and shipping labels applied.

Dematic has also installed a WMS (Warehouse Management System) that optimally manages stocks and orders according to priority, with the most frequently requested items stored in the most accessible locations. A WinCC process visualization system developed by Siemens was also integrated into the solution for monitoring the technical processes. It enables a simple and clear information flow of all accruing data and provides a user interface. As Thomas Meyer-Jander, Director EMEA and Head of Marketing & Communications at Dematic, explains: “In this way, users have access to the current operation status and can use that data to derive optimizations for improved performance.” The Schmidt Groupe’s assessment is correspondingly positive. Herrmann sums it up, “Our throughput and delivery accuracy goals have been more than met. With Dematic, we have the right supplier – due to their intralogistics know-how and expertise, they have been valuable resource, and our relationship has been characterized by cooperation and partnership.”

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Orion’s Compact RTC Rotary Tower Automatic Wrapper Integrates with Existing Conveyance

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Orion Packaging Systems, a division of ProMach, responded to industry demands by creating a space-saving rotary automatic wrapper that easily integrates with new or existing conveyance.

Orion’s Rotary Tower Automatic (RTC) Stretch Wrapper with Conveyance is the ideal solution for easy integration with 18″ pass-height conveyors due to its compact size, affordability, and the option to expand with additional conveyance.

The RTC is fully automatic and attaches the stretch film at the cycle start, cutting it at the end. The forklift operator simply places the pallet-load on the infeed conveyor and pulls a lanyard switch while moving away to collect the next load. This design increases employee safety by removing them from proximity of the moving rotary arm.

In addition to the compact size, expandability and increased employee safety, the RTC has:

  • 20″ Insta-Thread™ Film Carriage standard with 260% pre-stretch
  • Revo-Logic technology with photo-eye sensor carriage ensuring precise application of programmed wraps and maximizing load containment and film yield
  • Separate up and down film carriage speed control and top and bottom adjustable wrap counts, customizing wrapping for each load
  • Long lasting AC motors and Variable Frequency Drive (VFD) controllers, providing low maintenance
  • Labor saving film tail clamp with cut & wipe that automatically secures film, allowing faster output by reducing per-load wrapping

Orion’s RTC Stretch Wrapper delivers performance and cost-savings to new or existing wrapping systems, providing increased production and output.

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