Excessive-mix manufacturing poses many challenges for robotic automation. We’ve got seen many spectacular demonstrations of robotic automation in high-mix functions over the past 10 years. Typically these demonstrations are at know-how readiness stage (TRL) 5 or 6 stage. These demonstrations generate an excessive amount of curiosity in know-how and other people begin anticipating speedy know-how transition.
Nonetheless, know-how maturation on this space has been very sluggish. Only a few robotics applied sciences have been really deployed in high-mix functions. This text explores the explanations behind this sluggish transition.
Robotic automation for high-mix functions requires a basically totally different method. Parts of this method embody:
- 1. Sensor-based techniques for constructing half and workspace fashions
- 2. Automated robotic trajectory technology based mostly on half fashions constructed from sensing
- 3. Management system to deal with sensor uncertainties
Most know-how demonstration tasks deal with growth of notion, planning, and management capabilities to automate the duty. Typically, novel human-robot interplay capabilities are developed as a part of these demonstration efforts. Success metrics throughout demonstration typically deal with exhibiting that acceptable course of high quality might be achieved utilizing a small variety of consultant components.
Listed below are the explanation why robotics demonstrations fail to transition to deployments in high-mix manufacturing environments.
1. Lack of knowledge to successfully use AI-based approaches
Excessive-mix manufacturing requires use of sensors to localize components and assess high quality. So, utilizing an AI-based notion system turns into a pretty choice to complement conventional machine imaginative and prescient approaches. Solely a restricted quantity of knowledge might be collected through the demonstration venture to coach a mannequin to carry out notion operate. Sensor noise is fastidiously managed throughout demonstrations to make sure success. Subject deployments inherently have a excessive quantity of sensor noise that breaks the notion system educated on restricted information.
Creating a sturdy system able to functioning effectively within the discipline requires coaching the notion system with a considerable amount of information and choosing an structure that may successfully cope with the sensor noise. Constructing a sturdy notion system able to performing effectively within the discipline requires accessing many robotic cells and accumulating information from these cells below all kinds of situations.
This isn’t possible through the proof-of-concept demonstration techniques. Utilizing artificial information is a viable method, Nonetheless, artificial information is simply helpful if it matches actuality. So, constructing an artificial information technology pipeline just isn’t helpful throughout demonstration phases. Subsequently, the notion system developed throughout demonstrations typically requires important redesign. This takes important time and assets.
2. Restricted half variety makes it troublesome to design strong algorithms
Demonstrations are carried out on a restricted variety of half geometries. Which means that the planning and management capabilities are usually not examined rigorously. New half geometries encountered throughout deployment pose challenges for planning and management algorithms, typically requiring main upgrades to the method that may take a very long time to finish. Correctly validating planning and management capabilities requires testing with a number of hundred half geometries. This scale of testing just isn’t doable through the demonstration part. Subsequently, conclusions drawn relating to the feasibility of planning and management approaches don’t generalize throughout deployment.
3. Processes are usually not optimized for robots
Many handbook processes are designed based mostly on human capabilities. Robots have basically totally different capabilities. Demonstrations that concentrate on robotic techniques which might be human-competitive by way of pace are sometimes removed from being cost-effective throughout deployment. Efficiently integrating robotic automation requires course of improvements by growing new course of recipes. For instance, robots can apply a lot larger forces and subsequently can use cheaper abrasives and dramatically cut back abrasive prices.
Robots are very constant and, subsequently, can use aggressive course of parameters with out the chance of inflicting half harm. This has the potential to dramatically cut back the cycle time. Automation may use instrument motions that might not be possible for people to execute as a result of pace or vibration issues. Most demonstration tasks deal with automation and don’t have assets to appreciate course of innovation wanted for profitable deployment. It’s typically doable to attain superhuman efficiency by investing enough assets in course of innovation for robotic automation and creating pathways to favorable ROI for profitable deployment.
4. Human-system interplay points are usually not thought-about
In lots of functions, full automation just isn’t possible. Typically, we will understand important advantages if we will automate 90% or 95% of the duty. This ensures that the automation answer doesn’t grow to be overly costly to automate the toughest a part of the job. Subsequently, many demonstration tasks goal automation of 90% or 95% of the duty. The remaining job is carried out by people.
This mannequin works in precept. Nonetheless, most demonstration tasks ignore points associated to human integration with robotic cells. For instance, you will need to work out what work a human employee would do when the robotic is engaged on the half. They can’t be merely watching the robotic and ready for his or her flip to do the work. Except the human employee utilization might be stored very excessive, it’s troublesome to justify robotic automation price. For instance, if a human employee can assist a number of cells, then human employee utilization might be excessive and automation might be justified.
Alternatively, a robotic cell might be designed to maintain the robotic busy for half-hour or extra and subsequently giving the human operator enough time to work on different duties Most demonstration tasks deal with the design of a single cell. Subsequently, human integration subjects are ignored. This results in design of techniques that can’t be justified as a result of they result in numerous idle time for human staff.
5. Workforce readiness points are usually not addressed
Workforce associated points are sometimes not addressed throughout demonstration tasks. Sensible automation is commonly introduced as an answer to labor scarcity. Nonetheless, people are an integral a part of the manufacturing course of. To get the complete worth of automation, we’d like staff with the correct ability units. For instance, human operators might must work together with automated machines and robotic cells by feeding components into them or eradicating components from them. If human staff can not successfully make the most of the automated gear, it can not ship worth.
For current staff to carry out successfully, the interface to the automation system should be intuitive and easy to make use of. Ease of consumer interface and coaching is a key to getting buy-in from the workforce. One other problem is the upkeep and servicing of automation applied sciences. Typically growing in-house expertise to keep up automation gear turns into cost-prohibitive and the techniques fail to transition as a result of lack of workforce readiness.
6. Low system availability as a result of failures and time wanted to restore
Robotic cells which might be deployed in high-mix functions are complicated cyber-physical techniques working in dynamic environments. Subsequently, there may be important potential for the onset of hostile situations that if not dealt with promptly can function a precursor to failure. Beneath are just a few consultant examples. Stress within the airline can fluctuate and might result in the malfunction of pneumatic parts; Suboptimal particles removing can result in issues with imaging techniques; Elevated friction within the rail drive system can result in overheating of motors; Human errors can result in the loading of improper instruments or inadequate clamping of components. Any of those errors can result in critical failure and trigger harm. For instance, if the sensing system is performing suboptimally, then it might result in a collision that will break a cable or the instrument.
Recovering from critical failures requires appreciable human experience and important downtime. This limits system availability. Delivering excessive system availability requires growing and deploying an AI-based Prognostics and Well being Administration (PHM) system. A single robotic cell implementation throughout demonstration won’t be able to provide enough quantities of coaching information to implement a PHM system to ship an enough stage of system availability. Subsequently, PHM associated points are usually not addressed throughout demonstration. Growing a PHM system wanted for profitable deployment requires a considerable quantity of extra assets.
7. Lack of service infrastructure
A PHM system can challenge alerts and convey the system to a secure state. Typically, recovering from hostile occasions detected by the PHM system requires service. Subsequently, the PHM system must be complemented by a service infrastructure. This requires fielding a service workforce to assist robotic cells. If a company has deployed only a few cells, then it’s economically infeasible for them to develop an in-house service workforce. They may more than likely want an outdoor firm to service the robotic cells. These service associated points are usually not addressed through the demonstration tasks. With out addressing this challenge, it isn’t doable to deploy robotic options in high-mix manufacturing functions.
8. Robotic cells are usually not optimized to ship acceptable efficiency
For a robotic cell to carry out effectively, the general cycle time must be optimized. This requires addressing automation of numerous auxiliary features equivalent to instrument change, particles assortment, calibration and many others. This typically requires including extra {hardware} and software program capabilities. This in flip can improve prices. Deploying a system requires a trade-off between cycle time and value and discovering a system design idea that delivers helpful worth. Demonstration tasks typically ignore all these system design points and narrowly deal with the method automation. Subsequently, numerous new technological growth must happen to automate auxiliary features earlier than a system might be efficiently deployed.
9. The general manufacturing system just isn’t streamlined to allow the automation answer to ship its true worth
Demonstration tasks take a look at the method automation in insolation with out contemplating upstream or downstream steps. Usually, a course of step that faces high quality points or is difficult from an ergonomic perspective is focused for automation. Even when this course of step might be efficiently automated, its general efficacy might be restricted by downstream processing steps. For instance, if a downstream course of is inefficient, it should grow to be a bottleneck. Even when the automated course of operates at excessive pace, it is not going to be absolutely utilized as a result of downstream bottlenecks and therefore it can not ship its full worth.
Moreover, if the downstream course of is handbook, then it’d neutralize the prime quality produced by the automated course of. However, if an upstream course of is handbook and displays important variability in high quality, it could pose a problem for the automated course of. Variability might power the automated course of to carry out extra work, slowing it down, or end in decrease high quality outputs. Automation typically can not repair high quality issues originating from upstream processes. Subsequently, when deploying an automatic course of step, it’s essential to think about the complete workflow. This may occasionally require adjustments within the general course of circulation and system-level optimization to make sure the automated course of step can ship the anticipated worth. This step can take important time and assets and therefore delay deployment.
10. Infrastructure to replace/improve software program doesn’t exist
Automation in high-mix functions makes use of a major quantity of software program. This software program must be maintained and up to date at common intervals. Demonstration tasks don’t account for these wants. Constructing infrastructure for steady upgrades might be costly for particular person websites. However sadly, automation in high-mix functions can’t be deployed with out this infrastructure.
11. ROI can’t be justified based mostly on labor saving alone
Typically, when efforts are made to mature an illustration system right into a manufacturing system, the associated fee will increase quickly due to the entire components talked about above. Subsequently, ROI turns into laborious to justify purely based mostly on the labor financial savings. ROI can grow to be extra favorable if extra values are delivered. For instance, automated options can cut back use of consumables and provide important course of innovation. These components are usually not thought-about throughout demonstration tasks and integrating these throughout deployment requires important time and assets.
Most pilot demonstration tasks primarily deal with demonstrating the feasibility of automating a course of step. We’ve got seen numerous reinvention of recognized applied sciences/ideas throughout demonstrations tasks. All these demonstration tasks don’t add a lot worth to know-how deployment. Efficiently, deploying robotic automation in high-mix manufacturing functions requires numerous supporting know-how growth, system design, and consideration of workforce points. All of those require substantial assets and time. With no correct answer deployment roadmap, demonstration tasks are more likely to be shelved.
It’s extremely unlikely that the event of some robotic cells will allow a company to create the financial system of scale mandatory to achieve success in deployment. Subsequently, a company focused on deploying robotic automation in high-mix manufacturing both must have calls for for a lot of robotic cells to create the financial system of scale internally or associate with an exterior group that has already addressed the scaling challenge.
In regards to the writer
Dr. Satyandra Ok. Gupta is co-founder and chief scientist at GrayMatter Robotics. He additionally holds Smith Worldwide Professorship within the Viterbi Faculty of Engineering on the College of Southern California and serves because the Director of the Middle for Superior Manufacturing. His analysis pursuits are physics-informed synthetic intelligence, computational foundations for decision-making, and human-centered automation. He works on functions associated to Manufacturing Automation and Robotics.
He has printed greater than 5 hundred technical articles in journals, convention proceedings, and edited books. He additionally holds twenty one patents. He’s a fellow of the American Society of Mechanical Engineers (ASME), Institute of Electrical and Electronics Engineers (IEEE), Stable Modeling Affiliation (SMA), and Society of Manufacturing Engineers (SME). He has acquired quite a few honors and awards for his scholarly contributions. Consultant examples embody a Presidential Early Profession Award for Scientists and Engineers (PECASE) in 2001, Invention of the Yr Award on the College of
