Excessive-mix manufacturing poses many challenges for robotic automation. We have now seen many spectacular demonstrations of robotic automation in high-mix functions during the last 10 years. Typically these demonstrations are at expertise readiness degree (TRL) 5 or 6 degree. These demonstrations generate a substantial amount of curiosity in expertise and folks begin anticipating fast expertise transition.
Nevertheless, expertise maturation on this space has been very gradual. Only a few robotics applied sciences have been really deployed in high-mix functions. This text explores the explanations behind this gradual transition.
Robotic automation for high-mix functions requires a essentially totally different method. Elements of this method embody:
- 1. Sensor-based techniques for constructing half and workspace fashions
- 2. Automated robotic trajectory technology primarily based on half fashions constructed from sensing
- 3. Management system to deal with sensor uncertainties
Most expertise demonstration initiatives concentrate on 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 concentrate on displaying that acceptable course of high quality might be achieved utilizing a small variety of consultant components.
Listed below are explanation 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 components and assess high quality. So, utilizing an AI-based notion system turns into a sexy choice to complement conventional machine imaginative and prescient approaches. Solely a restricted quantity of information might be collected through the demonstration venture to coach a mannequin to carry out notion perform. Sensor noise is fastidiously managed throughout demonstrations to make sure success. Discipline deployments inherently have a excessive quantity of sensor noise that breaks the notion system educated on restricted knowledge.
Creating a sturdy system able to functioning nicely within the discipline requires coaching the notion system with a considerable amount of knowledge and deciding on an structure that may successfully take care of the sensor noise. Constructing a sturdy notion system able to performing nicely within the discipline requires getting access to many robotic cells and gathering knowledge from these cells underneath all kinds of situations.
This isn’t possible through the proof-of-concept demonstration techniques. Utilizing artificial knowledge is a viable method, Nevertheless, artificial knowledge is just helpful if it matches actuality. So, constructing an artificial knowledge technology pipeline just isn’t helpful throughout demonstration phases. Due to this fact, the notion system developed throughout demonstrations typically requires vital redesign. This takes vital time and sources.
2. Restricted half range makes it troublesome to design sturdy algorithms
Demonstrations are carried out on a restricted variety of half geometries. Because of this the planning and management capabilities aren’t 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 potential through 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 aren’t optimized for robots
Many guide processes are designed primarily based on human capabilities. Robots have essentially totally different capabilities. Demonstrations that target robotic techniques which are human-competitive when it comes to pace are sometimes removed from being cost-effective throughout deployment. Efficiently integrating robotic automation requires course of improvements by creating new course of recipes. For instance, robots can apply a lot greater forces and subsequently can use inexpensive abrasives and dramatically cut back abrasive prices.
Robots are very constant and, subsequently, can use aggressive course of parameters with out the danger of inflicting half injury. This has the potential to dramatically cut back the cycle time. Automation may use device motions that will not be possible for people to execute because of pace or vibration issues. Most demonstration initiatives concentrate on automation and should not have sources to comprehend course of innovation wanted for profitable deployment. It’s typically potential to realize superhuman efficiency by investing sufficient sources in course of innovation for robotic automation and creating pathways to favorable ROI for profitable deployment.
4. Human-system interplay points aren’t thought-about
In lots of functions, full automation just isn’t possible. Typically, we are able to understand vital advantages if we are able to automate 90% or 95% of the duty. This ensures that the automation answer doesn’t change into 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 job 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 determine 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 saved very excessive, it’s troublesome to justify robotic automation price. For instance, if a human employee can help 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 ample time to work on different duties Most demonstration initiatives concentrate on the design of a single cell. Due to this fact, human integration matters are ignored. This results in design of techniques that can not be justified as a result of they result in a whole lot of idle time for human staff.
5. Workforce readiness points aren’t addressed
Workforce associated points are sometimes not addressed throughout demonstration initiatives. Good automation is usually offered 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 want staff with the proper 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’t successfully make the most of the automated tools, it can’t ship worth.
For current staff to carry out successfully, the interface to the automation system have to be intuitive and easy to make use of. Ease of person 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 creating in-house expertise to take care of automation tools turns into cost-prohibitive and the techniques fail to transition because of lack of workforce readiness.
6. Low system availability because of failures and time wanted to restore
Robotic cells which are deployed in high-mix functions are complicated cyber-physical techniques working in dynamic environments. Due to this fact, there may be vital potential for the onset of antagonistic situations that if not dealt with promptly can function a precursor to failure. Beneath are a couple of consultant examples. Strain within the airline can fluctuate and might result in the malfunction of pneumatic elements; 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 injury. For instance, if the sensing system is performing suboptimally, then it could result in a collision that will break a cable or the device.
Recovering from critical failures requires appreciable human experience and vital downtime. This limits system availability. Delivering excessive system availability requires creating and deploying an AI-based Prognostics and Well being Administration (PHM) system. A single robotic cell implementation throughout demonstration won’t be able to supply sufficient quantities of coaching knowledge to implement a PHM system to ship an sufficient degree of system availability. Due to this fact, PHM associated points aren’t 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 problem alerts and convey the system to a protected state. Generally, recovering from antagonistic 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 workforce 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 workforce. They may probably want an outdoor firm to service the robotic cells. These service associated points aren’t addressed through the demonstration initiatives. With out addressing this problem, it’s not potential to deploy robotic options in high-mix manufacturing functions.
8. Robotic cells aren’t optimized to ship acceptable efficiency
For a robotic cell to carry out nicely, the general cycle time must be optimized. This requires addressing automation of a whole lot of auxiliary capabilities equivalent to device change, particles assortment, calibration and so forth. 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 price and discovering a system design idea that delivers helpful worth. Demonstration initiatives typically ignore all these system design points and narrowly concentrate on the method automation. Due to this fact, a whole lot of new technological growth must happen to automate auxiliary capabilities 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 initiatives have 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 total efficacy might be restricted by downstream processing steps. For instance, if a downstream course of is inefficient, it would change into a bottleneck. Even when the automated course of operates at excessive pace, it won’t be absolutely utilized because of downstream bottlenecks and therefore it can’t ship its full worth.
Moreover, if the downstream course of is guide, then it would neutralize the top quality produced by the automated course of. Then again, if an upstream course of is guide and reveals vital variability in high quality, it will probably pose a problem for the automated course of. Variability might drive the automated course of to carry out further work, slowing it down, or lead to 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 your entire workflow. This may increasingly require modifications within the total course of stream and system-level optimization to make sure the automated course of step can ship the anticipated worth. This step can take vital time and sources 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 initiatives 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 primarily based on labor saving alone
Typically, when efforts are made to mature an indication system right into a manufacturing system, the fee will increase quickly due to all the elements talked about above. Due to this fact, ROI turns into arduous to justify purely primarily based on the labor financial savings. ROI can change into extra favorable if further values are delivered. For instance, automated options can cut back use of consumables and provide vital course of innovation. These elements aren’t thought-about throughout demonstration initiatives and integrating these throughout deployment requires vital time and sources.
Most pilot demonstration initiatives primarily concentrate on demonstrating the feasibility of automating a course of step. We have now seen a whole lot of reinvention of recognized applied sciences/ideas throughout demonstrations initiatives. These kinds of demonstration initiatives don’t add a lot worth to expertise deployment. Efficiently, deploying robotic automation in high-mix manufacturing functions requires a whole lot of supporting expertise growth, system design, and consideration of workforce points. All of those require substantial sources and time. And not using a correct answer deployment roadmap, demonstration initiatives are prone 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 needed to achieve success in deployment. Due to this fact, a corporation thinking 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 accomplice with an exterior group that has already addressed the scaling problem.
Concerning the creator
Dr. Satyandra Ok. Gupta is co-founder and chief scientist at GrayMatter Robotics. He additionally holds Smith Worldwide Professorship within the Viterbi College 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 obtained 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 12 months Award on the College of
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