By Emily Newton, revolutionized.com
The rise of artificial intelligence has resulted in numerous instances of physical AI changing manufacturing. These use cases concern autonomous machines engaging with the real world, including in production facilities. The associated gains improve process engineers’ results, making their efforts more adaptable to evolving demands and environments. What are some of the most compelling opportunities to pursue?
1. Supporting existing production processes
Many process engineers seek to implement robots in flexible manufacturing within operational facilities. However, those efforts may require dedicating floor space to the machines’ charging stations, ensuring robotic arms can manipulate objects without hitting anything in their path or verifying that the placement of stationary equipment will not disrupt traffic flows.
Digital twins are among the most valuable tools for receiving those details, and some of them use AI to work. One that was built for pharmaceutical manufacturing ingests real-time plant data and combines physical and information-driven models. The resource facilitates better processes by detecting issues like abnormal tank levels or mismatched flow rates.
Users can simulate production processes, monitor facility performance in real time and test potential responses to common faults. These options support manufacturing flexibility by allowing parties to see the effects of proposed alterations before approving them. Information from digital twins also helps decision-makers recognise the best ways to implement robots for measurable results, whether to solve existing challenges, prepare for anticipated growth or meet other goals.
2. Handling specific processing steps
Many executives initially assume the best approach is to use robots in repetitive tasks to overcome challenges. Some examples of physical AI changing manufacturing highlight the possibilities, especially when brands require external support. As producers investigate the optimal ways to meet their output requirements, they may hire toll manufacturers to treat raw materials because these entities lack the resources to do those steps independently.
Producers may also take similar approaches if their locations lack specialised machines to process goods at scale. One company providing contract processing for dairy industry clients recently scaled up its capacity by purchasing new robotic equipment for spray drying infant food ingredients.
The setup includes four towers, complete with access gates to maintain the mandated conditions between each production area. Containment systems ensure items receive the correct doses, supporting consistency and trustworthiness for the consumables. Separate areas for sanitising the system’s components support the necessary cleaning procedures.
Because the new system can spray 650 to 750 kilograms of powder per hour, it is a strong example of how robots in flexible manufacturing help executives ramp up operations as demand requires. Depending on the stated needs, plant employees can combine several processing techniques — such as emulsification and heat — when interacting with the highly automated equipment.
The company also extends the robotic assistance to packaging, using its system to accommodate aluminium tubular bags with capacities from 5 to 25 kilograms. The filling steps occur in the plant’s most hygienic zone. A conveyor bridge then transports the products to an adjacent, fully automated warehouse.
3. Boosting and unlocking capacity
Adequate capacity means manufacturers can respond more rapidly to anticipated and surprise events. One of the world’s leading confectionery brands will use robotics and AI to reveal concealed capabilities within its supply chains and plants. Leaders mapped out a plan expected to boost maximum production capacity by 5%, driven in part by the installation of four new automated lines.
This case study of robots in flexible manufacturing allows the brand to meet existing requirements and stay prepared for the future. These automation purchases accompany previous AI and data analytics efforts to find concealed supply chain capacity. When decision-makers applied those technologies to six pieces of equipment that make a popular wafer chocolate bar, they saw potential schedule and item changes to open $35 million worth of additional volume accessible without significant costs.
The sweets maker also made gains by looking for underutilised lines and pinpointing goods better suited for co-manufacturers to make. In one case, the producer outsourced the creation of one of its white chocolate bars from its internal United States plant to a third-party Canadian entity. That freed up options for making a different product variety, raising the item’s capacity by 25% and contributing to approximately one-fifth of the brand’s 2022 growth.
As the company upgrades 11 production lines, it will prioritise advanced configurations that support quick changeovers and minimal downtime. Even as the business automates many processes with those enhancements, its employee upskilling goals should keep its workforce maximally competitive.
4. Improving waste segregation processes
Many businesses experience more production flexibility when leaders find feasible ways to accelerate the most time-consuming tasks and maintain high accuracy rates. That is the aim of a United Kingdom-based robotics company furthering AI-powered robots and motion intelligence technologies for waste management. It has partnered with an environmental services provider to see how its humanoid robot performs in a demanding real-world setting.
The chosen test site handles 2,800 tonnes of waste weekly, ranging from plastic to glass. Workers wear high-tech headsets that capture posture and hand and finger movement details, providing data necessary to train the robot. This method shows how successful examples of physical AI changing manufacturing may require employee insight and assistance. Those machines will then take over hazardous, repetitive or unsanitary garbage selection duties.
Although humans can sort at speeds averaging 30 to 40 items per minute, physical tiredness and decision fatigue can affect their performance. Leaders hope the robotics innovation can categorise waste and recyclable products by material and brand. If the first trial succeeds, they want to deploy the technology at 1,000 other European locations. The executives aspire to change waste management by enabling a greater understanding of the processed materials.
Problems such as single-stream recycling contamination interfere with many sustainability efforts. Robotic systems that support employees could detect those issues sooner so workers don’t have to spend as much time dealing with them. This technology connects to the cloud, so its developer will create a database of plant-related activities, using the information to improve future outcomes.
Advancing processes with robots in flexible manufacturing
Whether companies make pharmaceutical products or baby foods, flexibility in the processes can enable growth, raise productivity, maximise employee safety and retain competitiveness. These examples highlight fascinating ways to implement and benefit from physical AI changing manufacturing.
Leaders can optimise outcomes by investigating current bottlenecks and suitable technologies to overcome them. Such strategised approaches generate the biggest wins to elevate motivation.
