Cloud manufacturing represents the convergence of information, learned processes, and intelligent motion or activity.
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This is part one of a two-part piece on cloud computing.
Manufacturers and processors of anything from snack foods to automobiles are being driven to offer higher levels of variety in what they offer their customers. Invariably, supply chain power is shifting to buyers and consumers. This shift has been driven by numerous factors including the proliferation of information available to shoppers on all forms of digital devises effectively creating larger consideration sets. As the choices have increased, sellers and ultimately suppliers are forced to increasingly adhere to fads and rapidly changing consumer sentiment to retain market share. If we were to define the optimal supply chain to meet this trend it would be one where any item, no matter its level of complexity, would be produced on demand. Further, even the most commoditized and low costs items such as confectionary would be produced in a batch size of one to permit mass customization. This would enable a buyer to select a picture of the family dog from their iPad, upload it to a manufacturer’s website and a few days later a delivery van would arrive at your door with a box of chocolates each in the shape of Scruffy.
Markets are best served by catering to the individual tastes and preferences of the consumer. Therefore, we are beholden to understand how manufacturing must adapt to move past today’s batch processes to achieve a batch size of one. The innovation required to enable manufacturers to offer this, the ultimate level of production flexibility, will be drawn from fast-paced/cutting edge/advanced industries such as gaming and information technology.
Enter cloud manufacturing as technologies exist in all facets of packaging, labeling and decorating product to permit rapid change of color patterns and form. The pacing item is process control and ultimately the information from the consumer. And with the unprecedented speed of digital connections between people and the commercial world through social networks and alike, this valuable information can now be made more quickly available to manufacturers through the cloud. Cloud manufacturing represents the convergence of information, learned processes, and intelligent motion or activity.
Definition
Cloud Manufacturing is a model for enabling convenient, on-demand network access to a shared pool of configurable manufacturing resources (e.g., robotics, control systems, networks, applications, and services) permitting the comparison of digital process control with physical operation. The networking of sensory input, databases, and computing resources facilitates the management of sufficient data to recognize complex patterns and execute algorithms to evolve behaviors. Reconciliation of environmental conditions and information available in the cloud permits mechatronics to serve as the conduit between the digital and physical world.
Dynamic Process Control
To illustrate the concept, consider a robot tasked with dispensing icing on a cake. Today a robot is programmed to process certain patterns and graphics taught by its user. The robot would be outfitted with the ability to dispense various colors with different nozzles and the system would produce cakes with various images. In a highly controlled environment, a cell with set programmed paths will produce the images without incident. However, what happens if the environment and/or process changes. Consider the impact of the following;
- Viscosity of the icing
- Temperature within the facility
- Humidity
Viscosity of the icing is critical and most closely controlled. To keep the viscosity the same, the cake decorator is most likely locked into a single supplier to ensure consistency. In the event that the decorator produces his or her own icing, the level of process control to maintain the consistency is costly.
Temperatures in facilities affect a large number of parameters in the process. If the cake is cooled in ambient conditions it is subject to the changes in the plant’s temperature, which may impact the dimensions of the cake when it comes to the automated decorating station. If the icing sits in the delivery system for a period of time and the facility’s temperature varies day to day, the viscosity and properties of the icing are altered in kind.
Like temperature, humidity can impact numerous steps in the process. A higher level of moisture in the batter during one shift changes how the cake rises or its dimensions as it cools from the cakes produced the previous shift.
In some cases simple localized sensory input can adjust for environmental changes. If the cake height changes due to temperature, humidity, or upstream process changes, a variety of sensors could provide an input to the robot to offset the dispense height to accommodate the change. This signifies a defined rule-based solution and is far from complex.
Viscosity can be measured though numerous sensing devises located in the delivery equipment or lines. With the appropriate delivery mechanisms, the manner in which the icing is pumped through the lines and ultimately though each nozzle can be profiled and managed to adjust for the changes in viscosity.
Therefore, it would appear through a rule-based system and the implementation of sensors that the cake-decorating system can accommodate environmental changes, correct? Not so fast. As humidity changes and temperature goes up or down the icing exhibits different properties of adhesion and set-up. So, while the robot is dispensing at the right height and the right volume, the desired flower image changes from a carnation to a dandelion. The interaction of the various environmental factors and effects now represent complex patterns.
When the inputs or variables become sufficiently large, this model exceeds what manageable rule-based solutions are capable of solving. In our cake-decorating example we are now going to add 3D vision which will record the decorated cake. The image will be analyzed with set parameters to determine if the critical features are within spec. The image will be correlated to environmental data to catalog plant temperature, humidity, icing, viscosity, cake height, and any other inputs we care to monitor. As the process compares predicted outcomes to actual outcomes, the system has the ability to dynamically adjust the process. Over time as patterns develop with the information, the process will evolve to accommodate for all the combinations of environmental conditions and the system will learn how to modify the path of the robot to reliable draw a carnation.
Cloud manufacturing enables machine learning given the networking of expected results or those stored in a database, input from what is observed in the environment, and comparison of the predicted outcome with the actual. In this model, the robotic system is the networking device that provides sensory input and ultimately uses the information processed in the cloud to intelligently process the part. Hence the robots act as the connection between the digital and physical.
For more information, visit www.adept.com .