Non-invasive AI performance solution for injection moulding machines

Hayley Everett
Aisemo Analytics in action at the K trade fair. Photo via Aisemo.

Manufacturing hardware and software systems developer Aisemo has unveiled its new non-invasive and manufacturer-independent technology for improved performance in injection moulding that is based on artificial intelligence (AI). Called Aisemo Analytics, the software does not require any intervention in machine controllers or IT networks and is reportedly ready for use on any injection moulding machine in less than half an hour. This allows production data to be called up in real-time on all browser-based devices.

Data is collected independently with the aid of a Bluetooth sensor. Self-learning algorithms process this data and detect production irregularities, delays and downtimes in real-time. As a result, rejects and downtimes can be quickly minimised.

Calculating electricity costs realistically

Unveiled at the K trade fair this week, Aisemo demonstrated two new functions for its browser-based software: monitoring of energy savings and an extension with a module for order planning in production.

These functions allow order-specific energy costs to be measured and analysed, and production processes throughout the company to be planned more efficiently. Especially in view of the significant rise in electricity costs, actual energy consumption is an essential calculation variable for the profitability of production.

Ensuring super-simple operation

The most important success factors of the software include the focus on a small amount of meaningful information, its clear presentation and simple and fast operation. The software is therefore designed to be mastered in a short time, which contributes to the high acceptance among plant operators.

Aisemo Analytics consists of a Bluetooth sensor that is glued to the moving side of a clamping unit, a measuring module for power consumption, a tablet computer and an edge gateway. These are used to record data on the temperature, movement of the clamping unit and energy consumption of a machine. The brand, year of construction, drive and controller type of the machine, as well as the mould used and the material processed, are irrelevant. The main beneficiaries are injection moulders with heterogeneous machine parks who want to use Industry 4.0 applications.

The information is transmitted via SSL-encrypted connection form from the edge gateway to the Aisemo Cloud in Frankfurt/Main, where it is evaluated using AI and a large data pool. The smallest, characteristic deviations in cycle times, movements, ambient temperatures and power consumption are detected immediately and displayed to the machine operator on a tablet or browser-based device in an intuitive manner. The operator is then able to react immediately and also enter the cause of a malfunction or downtime.

New functions: Monitoring of energy savings and order planning

In order to be able to detect and avoid irregularities such as spikes in the energy consumption of injection moulding machines, Aisemo has developed a monitoring function that enables the power requirements of machines to be determined, analysed and reduced at any time during the order. All it takes is an additional module to determine the necessary information. This module supplies the data, which is also evaluated by the Aisemo Cloud. Order-specific CO2 balance sheets can then be compiled which serve as a decision-making basis for energy-efficient and sustainable production.

The company also released an extended function for order planning which detects production delays in real-time. This allows orders to be created, edited, documented and evaluated in uncomplicated and paperless form. The analysis of production sequences helps to optimise manufacturing processes and save costs. Incorrect planning, such as the double occupancy of moulds and machines, is thus avoided.

Since Aisemo Analytics is a Software-as-a-Service (SaaS) solution, functional extensions are possible without users having to make updates. Both innovations are available as a free option during the introductory phase and for existing customers.

A success story from agriculture

In 2009, Aisemo founder Wolfgang Auer developed “Smartbow”, a technology in which an ear sensor was used to collect data on the movements and body temperatures of cows. A self-learning algorithm was able to predict an illness three to five days before it broke out - based on the analysis of temperature changes and greater inertia of the animal. Today, AI software is used on several hundred thousand cows around the world. In 2018, Auer sold the technology to one of the world's leading pharmaceutical companies in animal health.

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