Siemens modularises robotics for food production

Siemens, Wymbs Engineering, and HMK Automation & Drives have developed a modular robotic cell intended to reduce the engineering complexity of automating food production tasks.

The platform brings robot control into Siemens’ Totally Integrated Automation Portal, allowing the cell to be managed within the same engineering environment used for programmable logic controllers, motion, drives, safety, human-machine interfaces, and other production equipment.

Applications include pick and place, product depositing, cream filling, fruit filling, bakery decoration, customisation, and packing. The modular mechanical design allows the basic cell to be adapted around different tools, processes, and product formats.

Recipe control can alter operating sequences between products, reducing the amount of physical rebuilding required for each new format. Seasonal ranges, shorter campaigns, and products requiring different deposits or decorations can therefore share a common automation platform.

An optional industrial-AI application analyses machine information and contextual images to support investigation of abnormal conditions. A temperature reading outside its expected range, for example, can be assessed against other process data before likely causes and inspection points are presented.

Robot integration remains a practical barrier

Industrial robots have operated in food manufacturing for decades, although deployment remains uneven between high-volume plants and sites handling frequent changeovers, variable product presentation, short runs, or foods that deform during handling.

Vendor-specific controllers add further complexity when a factory already has strong PLC and maintenance capability but still requires specialist external support for robot programming, vision systems, or proprietary interfaces.

Integrating robot control within a familiar automation environment can reduce that separation. Engineers can diagnose the cell alongside the rest of the line, while recipes, alarms, safety states, motion control, and production data can operate through a more consistent architecture.

The completed system must nevertheless remain supportable across every component. Standard control software simplifies part of the installation, but grippers, robot arms, cameras, safety equipment, and food-contact hardware must still operate together without shifting dependence into another proprietary subsystem.

Food production also imposes mechanical and hygienic demands that are less common in dry industrial robotics. Equipment may require washdown capability, controlled lubrication, smooth surfaces, allergen-changeover procedures, and protection against crumbs, powders, oils, sugar, moisture, and temperature variation.

Handling the product can prove more difficult than controlling the robot, since bakery items, filled products, confectionery, and soft foods may change shape, position, temperature, or surface condition while travelling along a belt. Gripping force sufficient to maintain speed can also mark or damage the product.

Depositing and decorating combine motion with process control, requiring product viscosity, pressure, nozzle behaviour, temperature, and belt position to remain synchronised. Automating an unstable upstream process can reproduce variation more quickly rather than improving consistency.

The recipe-led architecture is intended to accommodate those mixed requirements by changing positions, quantities, patterns, and sequences through software while replacing only the tool or product guide needed for the next format.

Commercially available components aligned with IEC 62443 cybersecurity principles have been used across the platform. Standard hardware can support replacement and lifecycle maintenance, while the security framework recognises that connected robotics creates another route into the production network.

Remote access, recipe databases, camera images, AI services, and production records all require controlled connections between the cell and wider factory systems. Authentication, network segmentation, software updates, backups, and external support access must be established before the equipment enters routine use.

Labour availability continues to support robotic investment, although the economics extend beyond replacing one operator. A successful project must improve yield, repeatability, uptime, safety, or flexibility without transferring excessive work into engineering, cleaning, and maintenance.

Consistent depositing, positioning, and decorating can reduce giveaway, misalignment, rework, and packaging faults where downstream machines require products to arrive in a defined orientation. Those gains depend on reliable infeed presentation and feedback when products fall outside the expected position.

Industrial AI may shorten fault diagnosis when its recommendations are grounded in accurate machine data and presented clearly to operators. Engineering judgement will remain necessary where one symptom can result from several interacting mechanical, electrical, software, or product conditions.

Modular automation also changes the investment calculation, since a cell that can be reassigned between tasks carries less risk than equipment built around one product. Reuse will depend on the speed of reconfiguration, validation, cleaning, and operator training between applications.

The platform reflects a wider movement away from isolated robot installations towards equipment that shares recipes, performance data, safety states, and diagnostics with the complete production line. Its strongest commercial case will come from systems that remain useful after the first product and can be maintained by the engineering teams already supporting the factory.