Excessive-mix manufacturing poses many challenges for robotic automation. We’ve seen many spectacular demonstrations of robotic automation in high-mix purposes during the last 10 years. Typically these demonstrations are at know-how readiness degree (TRL) 5 or 6 degree. These demonstrations generate an excessive amount of curiosity in know-how and other people begin anticipating speedy know-how transition.
Nevertheless, know-how maturation on this space has been very sluggish. Only a few robotics applied sciences have been really deployed in high-mix purposes. This text explores the explanations behind this sluggish transition.
Robotic automation for high-mix purposes requires a essentially completely different strategy. Parts of this strategy embrace:
- 1. Sensor-based programs for constructing half and workspace fashions
- 2. Automated robotic trajectory era based mostly on half fashions constructed from sensing
- 3. Management system to deal with sensor uncertainties
Most know-how demonstration initiatives give attention to growth of notion, planning, and management capabilities to automate the duty. Generally, novel human-robot interplay capabilities are developed as a part of these demonstration efforts. Success metrics throughout demonstration typically give attention to displaying that acceptable course of high quality could be achieved utilizing a small variety of consultant elements.
Listed below are the reason why robotics demonstrations fail to transition to deployments in high-mix manufacturing environments.
1. Lack of information to successfully use AI-based approaches
Excessive-mix manufacturing requires use of sensors to localize elements 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 information could be collected in the course of the demonstration venture to coach a mannequin to carry out notion perform. Sensor noise is fastidiously managed throughout demonstrations to make sure success. Area deployments inherently have a excessive quantity of sensor noise that breaks the notion system skilled on restricted information.
Creating a strong system able to functioning effectively within the area requires coaching the notion system with a considerable amount of information and deciding on an structure that may successfully take care of the sensor noise. Constructing a strong notion system able to performing effectively within the area requires gaining access to many robotic cells and gathering information from these cells beneath all kinds of situations.
This isn’t possible in the course of the proof-of-concept demonstration programs. Utilizing artificial information is a viable strategy, Nevertheless, artificial information is just helpful if it matches actuality. So, constructing an artificial information era pipeline isn’t helpful throughout demonstration phases. Due to this fact, the notion system developed throughout demonstrations typically requires important redesign. This takes important time and sources.
2. Restricted half variety makes it tough to design sturdy algorithms
Demonstrations are carried out on a restricted variety of half geometries. Because of this 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 strategy 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 isn’t attainable in the course of the demonstration part. Due to this fact, 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 guide processes are designed based mostly on human capabilities. Robots have essentially completely different capabilities. Demonstrations that concentrate on robotic programs which might be human-competitive by way of velocity 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 greater forces and due to this fact can use cheaper abrasives and dramatically scale back abrasive prices.
Robots are very constant and, due to this fact, can use aggressive course of parameters with out the chance of inflicting half injury. This has the potential to dramatically scale back the cycle time. Automation may also use device motions that may not be possible for people to execute as a result of velocity or vibration issues. Most demonstration initiatives give attention to automation and shouldn’t have sources to comprehend course of innovation wanted for profitable deployment. It’s typically attainable to realize superhuman efficiency by investing ample sources 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 purposes, full automation isn’t possible. Typically, we are able to understand important advantages if we are able to 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. Due to this fact, many demonstration initiatives goal automation of 90% or 95% of the duty. The remaining activity is carried out by people.
This mannequin works in precept. Nevertheless, most demonstration initiatives 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 could be saved very excessive, it’s tough to justify robotic automation price. For instance, if a human employee can help a number of cells, then human employee utilization could be excessive and automation could be justified.
Alternatively, a robotic cell could be designed to maintain the robotic busy for half-hour or extra and due to this fact giving the human operator enough time to work on different duties Most demonstration initiatives give attention to the design of a single cell. Due to this fact, human integration matters are ignored. This results in design of programs that can’t be justified as a result of they result in lots of idle time for human staff.
5. Workforce readiness points are usually not addressed
Workforce associated points are sometimes not addressed throughout demonstration initiatives. Sensible automation is usually introduced as an answer to labor scarcity. Nevertheless, people are an integral a part of the manufacturing course of. To get the total worth of automation, we’d like staff with the appropriate ability units. For instance, human operators could have to work together with automated machines and robotic cells by feeding elements into them or eradicating elements from them. If human staff can’t successfully make the most of the automated tools, it can’t ship worth.
For present staff to carry out successfully, the interface to the automation system have to 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 tools turns into cost-prohibitive and the programs 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 purposes are advanced cyber-physical programs working in dynamic environments. Due to this fact, there may be important potential for the onset of adversarial situations that if not dealt with promptly can function a precursor to failure. Beneath are a number of 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 programs; 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 elements. Any of those errors can result in critical failure and trigger injury. For instance, if the sensing system is performing suboptimally, then it might result in a collision that will break a cable or the device.
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 will be unable to provide ample quantities of coaching information to implement a PHM system to ship an ample degree of system availability. Due to this fact, PHM associated points are usually not addressed throughout demonstration. Creating a PHM system wanted for profitable deployment requires a considerable quantity of further sources.
7. Lack of service infrastructure
A PHM system can challenge alerts and convey the system to a protected state. Generally, recovering from adversarial occasions detected by the PHM system requires service. Due to this fact, the PHM system must be complemented by a service infrastructure. This requires fielding a service staff to help robotic cells. If a corporation has deployed only a few cells, then it’s economically infeasible for them to develop an in-house service staff. They may probably want an outdoor firm to service the robotic cells. These service associated points are usually not addressed in the course of the demonstration initiatives. With out addressing this challenge, it isn’t attainable to deploy robotic options in high-mix manufacturing purposes.
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 lots of auxiliary features equivalent to device change, particles assortment, calibration and many others. This typically requires including further {hardware} and software program capabilities. This in flip can enhance prices. Deploying a system requires a trade-off between cycle time and value and discovering a system design idea that delivers helpful worth. Demonstration initiatives typically ignore these kinds of system design points and narrowly give attention to the method automation. Due to this fact, lots of new technological growth must happen to automate auxiliary features earlier than a system could be efficiently deployed.
9. The general manufacturing system isn’t streamlined to allow the automation answer to ship its true worth
Demonstration initiatives have a look at the method automation in insolation with out contemplating upstream or downstream steps. Sometimes, 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 could be efficiently automated, its total efficacy could be restricted by downstream processing steps. For instance, if a downstream course of is inefficient, it would grow to be a bottleneck. Even when the automated course of operates at excessive velocity, it is not going to be absolutely utilized as a result of downstream bottlenecks and therefore it can’t ship its full worth.
Moreover, if the downstream course of is guide, then it’d neutralize the top quality produced by the automated course of. However, if an upstream course of is guide and reveals important variability in high quality, it may possibly pose a problem for the automated course of. Variability could drive the automated course of to carry out further work, slowing it down, or end in decrease high quality outputs. Automation typically can’t repair high quality issues originating from upstream processes. Due to this fact, when deploying an automatic course of step, it’s essential to contemplate all the workflow. This will require adjustments within the total 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 sources and therefore delay deployment.
10. Infrastructure to replace/improve software program doesn’t exist
Automation in high-mix purposes makes use of a big quantity of software program. This software program must be maintained and up to date at common intervals. Demonstration initiatives don’t account for these wants. Constructing infrastructure for steady upgrades could be costly for particular person websites. However sadly, automation in high-mix purposes 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 price will increase quickly due to all the elements talked about above. Due to this fact, ROI turns into arduous to justify purely based mostly on the labor financial savings. ROI can grow to be extra favorable if further values are delivered. For instance, automated options can scale back use of consumables and supply important course of innovation. These elements are usually not thought-about throughout demonstration initiatives and integrating these throughout deployment requires important time and sources.
Most pilot demonstration initiatives primarily give attention to demonstrating the feasibility of automating a course of step. We’ve seen lots of reinvention of recognized applied sciences/ideas throughout demonstrations initiatives. These kinds of demonstration initiatives don’t add a lot worth to know-how deployment. Efficiently, deploying robotic automation in high-mix manufacturing purposes requires lots of supporting know-how growth, system design, and consideration of workforce points. All of those require substantial sources and time. With no correct answer deployment roadmap, demonstration initiatives are more likely to be shelved.
It’s extremely unlikely that the event of some robotic cells will allow a corporation to create the financial system of scale crucial to achieve success in deployment. Due to this fact, a corporation fascinated about 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 companion with an exterior group that has already addressed the scaling challenge.
Concerning the writer
Dr. Satyandra Okay. 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 purposes associated to Manufacturing Automation and Robotics.
He has revealed 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 embrace a Presidential Early Profession Award for Scientists and Engineers (PECASE) in 2001, Invention of the Yr Award on the College of