Category: Resources

High Mix Manufacturing – Simplified with Task Canvas Blocks

With a revolution in robot programming coming in the form of easy to use collaborative and industrial robots, more machine shops are taking on automation projects. Although some of the initial setup and programming becomes much easier, the problems with integrating configuring and programming these peripheral devices still persist. 

READY simplifies the processes of integrating the robot with those peripheral devices. READY makes the process of programming the entire task easy enough that anyone on the factory floor can do it. Using Forge/OS, a cross platform automation operating system, it enables anyone to configure and control all aspects of the automation cell no matter the brand of robot or peripheral.

High Mix using Forge/OS

Automaton can be difficult to justify In a high mix  manufacturing environment.  When a machine runs a wide variety of parts, the automation needs to be just as versatile. Many machinists are accustomed to adjusting features of the job to get the best result. Commonly machinists often modify parameters in the CAM software and changing tooling on the machine depending on the job. Robots traditionally don’t work the same way. During changeover it can be complicated and  difficult to modify a program or change a peripheral. This can lead to the potential loss of time due to the lack of knowledge and skill in robot programming. 

Forge/OS vastly simplifies the changeover process in a high mix environment. Automation becomes much more accessible utilizing Task Canvas, the flowchart programming application. In Task Canvas each action is controlled by a block that has customizable control built in. We’re going to look at a few blocks within Task Canvas that will simplify changeover. 

Camera Block

The Camera Block can be a great  solution for a high mix manufacturing environment. Vision systems have a wide variety of uses, most often allowing the robot to locate parts within the workspace. The camera is programmed in the vision system’s native software. This is used to  locate the part specified in the software and give the robot a point in space to grasp the part.

READY makes these vision systems much more useful with the Camera Block. In Task Canvas these blocks are used  to send and receive signals from the vision systems controller after the camera takes the image. When part changeover happens the vision system will need to be updated for the new part, but the blocks and the logic in Task Canvas stay the same. This vastly reduces the time it takes to change to a new part.   

Grid Block

The grid block lets the robot to move the end effector (end of the robot)  from one position to another following a customization grid pattern. Since the camera is mounted to the end of the arm, the vision system can be programmed to take an image every time it gets to another point in the grid. With only creating 3 waypoints and changing a few parameters, the robot can continuously scan for parts on the work surface, and even alert the operator if the parts need to be refilled. 

Gripper Block

Once you can locate the parts using the grid move and camera blocks its time to pick the part up. In this high mix environment your parts will change so your gripper will need to be just as flexible. The gripper block will allow you to control a gripper no matter the actuation type. If you have a suction gripper or an electric gripper they both are programmed the same way. This vastly opens up the possibilities in your manufacturing space. If your parts change you can easily modify the program to accommodate the parts. If the parts are different enough, the gripper can be changed and the program can be updated in minutes. 

Check out our video that puts these principles into action. 

Forge/OS can help boost your production by simplifying the difficult parts of automation. Using the blocks built into Task Canvas anyone in the shop can learn and master automation

Learn More about Forge/OS here

How No Code Programming Democratizes Automation

From website creation to mobile app production, No Code programming solutions have significantly reduced the barriers to programming, and democratized the production of websites, html emails, mobile apps, and more. No Code tools have enabled anyone to build previously complex elements, by replacing the skill needed to write code with easy-to-use drag and drop user interfaces. No Code programming has empowered businesses to build and manage their own high quality websites, design/build/test landing pages, build and deploy email campaigns, and even build apps. No Code programming is poised to revolutionize manufacturing.

What does No Code programming have to do with manufacturing?

Until recently, nothing. Programming robots and automation required a very specific skillset, that demanded months if not years of training and experience. This programming barrier has limited robotic adoption, but it makes manufacturing an ideal candidate for a No Code revolution. How would robotic adoption be impacted if programming wasn’t limited to expensive and scarce robot programmers or integrators? What if anyone on the factory floor could program, deploy and manage robots? What if instead of an urgent call to an integrator, troubleshooting automated workcells could be performed by multiple machinists currently on the factory floor? What if even third shift workers were comfortable managing changeover, or troubleshooting? This is the future of manufacturing that we will see with No Code programming!

How will manufacturers benefit from No Code programming?

There are many ways in which No Code programming will positively impact manufacturing. We’ll outline them in this article. For a more complete review of No Code programming, and its impact on manufacturing, download our free whitepaper: The No Code Revolution.

Download No Code Whitepaper

The benefits of No Code programming in manufacturing.

  1. By developing the application yourself, you know exactly how it operates.

There is no part of your automation application that your team won’t understand. This also enables you to leverage institutional knowledge of the task you are automating, to build a successful automation solution more quickly, with less trial and error.

  1. You are self sufficient.

Need to update the program? Your team can do it! They can also rapidly troubleshoot any issue that might arise.

  1. Your team gets to continuously improve and upgrade their skillset.

With each task they implement, your team grows more competent and more confident in automation. We often hear from customers that while initially apprehensive about automation, their team actually enjoys programming automation in a No Code environment. In addition to increased productivity, this can also lead to higher job satisfaction.

  1. Community

A universal operating system with a No Code programming environment has the potential to unify a fragmented industry. And, when anyone can program any robot or automation task, automation in becomes much more accessible.

Expert knowledge transfer: the old way vs No Code

One of the most valuable aspects of a No Code environment is how it can empower an expert to transfer their knowledge of a task to an automation program without a middleman. Traditional knowledge transfer often doesn’t go smoothly, because those with the most experience and knowledge regarding a particular task (the machinists and operators) are generally not highly involved in automating the task. No Code programming breaks an over-reliance on outside integrators to observe, understand and program a task. Instead, factory workers with deep knowledge of the task are directly involved in programming automation. This can greatly improve the likelihood of success with an automation project out-of-the-gate, and it empowers those most familiar with a task to manage the automation ongoingly.

No Code robotic programming is a revolutionary change to how robotic automation can be implemented. Not only can programs be written exponentially faster than existing robot programming languages, but they can be owned and maintained by the workers managing the workcell. The benefits of No Code programming are tremendous, and we believe that No Code programming is critical to democratizing automation and unlocking the Fourth Industrial Revolution.  

To learn more about No Code programming and the impact it will have on manufacturing, download our free whitepaper.

Download No Code Whitepaper

Robot Selector – A Resource for All Manufacturers

Recently, we released Robot Selector to the world. Featuring more than 700 robots from 70 robot brands, it is the first searchable library of every 6 degree of freedom (6 DOF) robot. In addition to robots from major OEMs like FANUC, Kuka, and ABB, it includes smaller brands with limited market presence, but unique and potentially “right” robots for various applications. Robot Selector enables anyone to search all 6 DOF robots by reach, payload, brand, and collaborative vs industrial. The objective is to make it significantly easier for manufacturers to find the right robot for a given automation project.

Why does Robot Selector matter?

In the top-heavy but highly fractured robotic market, it has never been easy to objectively find the correct robot for a particular task. It is hard to know which robot OEMs to search, and there is no single, searchable collection of all robots. This is a barrier to automation. Automating can be complex and intimidating, and any barrier that can be removed makes automation more accessible – especially to those automating for their first time. Having an easily searchable robot library gives manufacturers transparency into all available robotic options, and makes it easier to find the right robot.

Why did READY build it?

READY is brand agnostic. Whether with FANUC, or Universal Robots, or any brand in between, we want to unlock automation for all manufacturers. One barrier that must be addressed is the programming barrier. It increases automation costs, and limits manufacturers to robot brands that they (or their integrators) are familiar with. This can mean that a more affordable or appropriate robot isn’t considered, because it can’t be easily programmed. The promise of Forge/OS, READY’s universal operating system for industrial automation, is that anyone can program any robot or peripheral via a single, user-friendly interface. In addition to reducing programming time, this will enable manufacturers to choose the right robot for the job – regardless of brand. 

While every robot brand isn’t currently supported by Forge/OS, eventually they will be. This is why READY was in a unique position to build Robot Selector. We want to make it as easy as possible for manufacturers to deploy the correct robot for any application. We hope the manufacturing community will find Robot Selector to be a valuable resource in their effort to deploy the best robotic solution.

Should you automate? Calculating ROI on your next Automation Project

Does it make financial sense to automate a particular task? Should you automate task A or task B? It’s important to know the answer to these questions before launching an automation project. Properly calculating projected ROI is one key to making a good decision on your next project. So, how do you calculate projected ROI?

In basic terms, ROI stands for Return On Investment. ROI models vary from company to company, but it’s common for manufacturers to target an ROI of 18-24 months on their automation investments. Sometimes, ROI targets can be as short as 12 months. Properly calculating projected ROI is an important step for any project engineer or plant manager, as it ensures that 1) the selected task will pay for itself in an acceptable time frame 2) that the project with the biggest ROI is selected over other possible projects. 

Common inputs to computing ROI in manufacturing  uses many of the following inputs

  • Machine loading time
  • Machine cycle time
  • Machine unloading time
  • Parts produced per week
  • Part shape
  • Part width: min and max
  • Part length: min and max
  • Part weight: min and max
  • Parts produced per week
  • Number of parts held in hopper
  • Time to load parts hopper
  • Time before refill
  • Time spent refilling
  • Fully loaded hourly rate of machine operator

Why must you know the part shape, part dimension to calculate ROI? The parts dimensions inform the robotic arm required, which is a major cost factor in any automation project. Other variables are closely tied to machine operator requirements for supporting the automated task. With these inputs, it is possible to choose an appropriate automation solution (and the associated cost), and calculate how long the automation investment will take to achieve ROI. READY has built an ROI calculator that automatically performs the complex ROI calculations. Simply input the data associated with your task to receive an instant ROI projection informing how quickly your project should achieve ROI.

CalculatorROI CALCULATOR

ROI Calculator

Answer a few questions about your production situation, and get an instant ROI projection.

To get a better understanding of the factors that influence the ROI of a project, so you can better plan, and maximize success on future projects, download our ROI-focused whitepaper with the deceptive title: Automation is Too Expensive. Software is the Solution.

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Speed Test: How Fast is Forge/OS?

Introduction

Even with extensive training, robot programming is typically complex and time consuming. With PolyScope, Universal Robots made programming significantly faster and easier – as long as you are programming their robot. Unfortunately, 99% of the global robot installed base is made up of other robot brands, each with their own programming language.

Forge/OS enables easy programming on not one, but multiple brands of robots. How fast and easy is programming with Forge/OS? We conducted a “speed test” against PolyScope to find out.

Overview

The Forge/OS programming application Task Canvas is a significant improvement to the programming paradigms in use today for controlling industrial robots. Task Canvas solves the problem that industrial robots are too hard to use and require too much training. In addition, Task Canvas is cross-brand, meaning that once you learn to program one brand of robot, you can program on many other brands.

In this article we provide quantitative support for how much easier Task Canvas is than UR’s (Universal Robots) PolyScope environment for programming a common task, picking and placing parts into a grid.

In summary, our findings show that in comparison to PolyScope, TaskCanvas

  • Reduces training time by 22% 
  • Enables users to program a grid task 23% faster
  • Results in less points of confusion while developing the application

Not only is it easier to learn, faster to program, and results in users having less frustrations when using Task Canvas, but users are also able to program any supported robot too. We even get comments that programming in Task Canvas is fun!

The Study

The study consisted of 20 users who had not programmed robots before and also did not have any automation experience. We randomly divided them into two groups, one for Task Canvas and the other group for PolyScope. During the study one of the Task Canvas participants dropped out.

Each participant was given training on how to program a grid task and taught each node/block that they’d need to program the task. Participants were trained using plastic disks and then had to program a similar grid task without guidance from the instructor.

When participants programmed the task, we measured the time it took them to develop it as well as the “confusion points” that they ran into. 

Training Details

The instructor trained the participants individually in how to program a grid task using 12 plastic disks, a suction gripper, and a 3×4 grid. Participants were taught about all of the features and steps that they’d have to use to program the grid task as well as the order that those nodes/blocks should be added. They were free to ask any questions and taught about features in the order in which one would most efficiently program the task. 

 The training task is programmatically identical to the task that they were asked to program during the task programming section. The only exception is that since the gripper was changed from a suction gripper to a 3-finger Schunk gripper, participants did have to be taught how to actuate them. Training sessions were timed.

Task Programming

After participants completed training, they were asked to program a grid task using 9 cuts of steel pipe, a 3-finger Schunk gripper, and a 3×3 grid. This task is more challenging than what they were trained on and necessitated more precision than the training task. Parts needed to be picked up and placed precisely or they wouldn’t fit into the grid.

We measured both the total time that it took participants to complete the task (Total Programming Time) as well as the time that it took them to complete the “logic” of the program (Logic Programming Time). The Logic Programming Time includes not only placing all the nodes/blocks of the task correctly but aligning waypoints roughly where they need to go. The difference between the Logic Programming Time and the Total Programming Time is that the latter includes the additional time that it took to execute the completed task(typically around two minutes and 10 seconds) as well as the time that users spent making grid edits (when applicable). Not all users needed to make grid edits and there was a high degree of variability in the amount of time that these edits took. This skewed the data for Total Programming Time and although READY still outperformed UR by an average of 6 minutes and 57 seconds, this analysis focuses on Logic Programming Time.

In addition to time, we also measured the number of Points of Confusion for participants. Points of Confusion are defined as the sum of:

  1. The number of times that the participant had to ask a question. Participants were encouraged to ask the instructor a question if they couldn’t proceed or if they thought it would take more than 30 seconds to troubleshoot.
  2. The number of errors a program contained when the participant tried to run it for the first time. All of these errors were explained to the participant for them to fix after the initial task execution attempt.
  3. The number of times that the instructor had to step in to prevent the participant from making a major error. 

The Results

We used 1-tailed tests to evaluate these results for statistical significance with an alpha of 0.05.

Training TimeStatistically Significant(p=.0017)Training sessions were 22% longer, being on average 6 minutes and 47 seconds longer for participants in the PolyScope group than in the TaskCanvas group.
Logic Programming TimeStatistically Significant(p=.0313)Task Canvas users took 23% less time to program their task than the UR PolyScope users. The mean time in Task Canvas was 26 minutes and 33 seconds compared to 34 minutes and 16 seconds on UR.
Points of ConfusionApproaching Statistical Significance(p=.0545)Users had 36% less points of confusion when using Task Canvas. This means they had less questions or challenges with a step while doing the task.

Results Analysis

Task Canvas’ favorable scores for Training Time, Logic Programming Time, and Points of Confusion are likely the result of it being more intuitive and easy to understand than PolyScope. Some processes in Task Canvas are simpler and more streamlined than Polyscope. For example, Forge/OS’s native support of pneumatic grippers simplified the process of actuating the Schunk 3-finger gripper. Whereas 3 nodes are required to do this action in PolyScope, only one block is required in Task Canvas. Another factor that contributes, albeit slightly, to this time discrepancy is that Task Canvas only requires that users align 3 grid corners instead of 4.

Conclusion

Anecdotally, we see that Task Canvas is easier for users to program than other robot interfaces. We have customers, such as Alicat Scientific, who were able to implement a task on their own and run lights out in just a week after receiving their system. A common refrain we get from prospects is that after a lot of work in a robot’s native interface, they finally get a program running, but then struggle with how long it takes to get a new program up and running. Task Canvas makes programming robots simpler, and better yet, works on many different robot brands. 

READY Robotics developed Forge/OS, an industrial operating system, to allow for vendor independence and plug and play usage of robots and peripherals. Running on Forge/OS, Task Canvas, a visual, flowchart based programming application for automation that is the only cross-brand general purpose robot programming application on the market. Task Canvas democratizes robotic programming by enabling manufacturers to augment and upskill existing staff. Through these features, READY can help you reach ROIs previously thought unattainable. 

Automation will be as Easy as Connecting a Mouse to a Computer

READY Robotics CEO and co-founder, Benjamin Gibbs, talks with David Perkton about the mission of the company in Control Design article “The Advent of Easier Automation”. With a mission to improve the world’s quality of life and productivity through automation, READY has created a user-friendly, cross-platform software interface that enables factories and manufacturers to easily program robotics and automation.  

Factories will now be capable of leveraging automation like never before. By reducing time spent on the planning and implementation phase of automation, these businesses can focus on increasing their job velocity. Ben goes on to talk about where he sees manufacturing and automation heading.

“Much in the same way when today you just plug your mouse into a computer and the operating system automatically integrates all the drives and in a couple seconds you are up and running using the mouse. We need to reach the same point in robotics, and then we can really unlock this space and help to create a future of ubiquitous robotics.”

Read the full article at Control Design

Forge 101: What is Joint Jump, and how will it help you?

When programming automation tasks we’re often confronted with a situation where the robot arm needs to be in a very specific shape or pose. For example, when moving in or out of a tight space in a machine tool. This can be tricky without the ability to direct each joint to a specific angle. It might be necessary to direct each joint to move independently or all at once. Within Task Canvas, programming these highly specific movements is easily accomplished.

This is where Joint Jumps come along. The Joint Jump Block directs the arm to move into a specific pose based on joint angle instead of a waypoint. This will help you avoid singularities, get the arm into a specific pose, or get into and out of tight spaces in a repeatable motion. 

With Task Canvas, programming Joint Jumps on any robot arm is easy. Task Canvas enables you to program Joint Jump blocks on all supported robot arms running Forge/OS. Once in Task Canvas, access Joint Jump by clicking “Add Block” > “Robot Moves” > “Joint Jump”. Once you’ve opened a joint jump generator, change the “Jump To” field inside the generator then accept the changes. You can also control how fast the arm will move with the speed field, and test your joint jump with the Execute button and Initial Position Button. 

Joint Jump Generator

Looking to take your programming to the next level, reduce cycle time, improve repeatability, etc? Visit our YouTube channel for more video tutorials on Forge/OS, Task Canvas, and automation in general.

5 Reasons Programming Robots is Hard

1

Historically, robot programming has been the sole domain of manufacturing engineers, highly trained workers, and integrators with specialized skills. But, with only 1 robotics engineer for every 11 factories, there simply are not enough trained workers to design, install and maintain robotic automation. This shortage of workers capable of programming robots and automation makes them expensive, drives up the overall cost of automation, and limits the adoption of robotic automation. Since the training requirements to learn how to program a robot are very high, it’s unlikely that as an industry we’ll be able to train enough workers to implement the level of automation necessary to solve the overall labor gap. A radical change in how robots are programmed is needed.

Let’s explore the reasons why programming robots is hard, and why READY is so focused on making industrial robots easy for anyone to program.

  1. Every robot has its own programming language

Automation Engineers develop tasks in the robot brands native programming language. Each robot arm manufacturer’s programming language is unique. For example, FANUC has Karel, Universal Robots has URScript, Yaskawa Motoman has INFORM, ABB has RAPID, and Kuka uses KRL. Automation Engineers need to learn a new programming language every time they want to program, modify, or troubleshoot a different brand of arm.

  1. Programming Interfaces are antiquated

Teach Pendants are necessary for controlling the robot arm, safety of the cell, and other peripheral devices. The programming environment on the teach pendants lacks many of the conveniences of modern technology. Conveniences such as multi-touch screens, graphical cues, drag and drop, and even cut and paste are commonly missing features. Because the interface is so different, the person responsible for programming must adapt to a suboptimal interface. This lack of familiarity with the programming environment is a significant barrier for developers to learn automation.

  1. Robot training courses require prerequisites

Classes from robot manufacturers, trade school and private training providers require prerequisites to take a more useful robot programming class. These prerequisite classes often take weeks to complete. This means a lot of time off work to complete hours of prerequisite training. All this time invested will only teach you how to develop simple application programs.

  1. Training is expensive and time consuming

According to the manufacturer’s class we looked at, it takes 72 hours of training to develop a simple application in their programming language. This is not even a fraction of the knowledge you need to develop an automation system. It can cost thousands of dollars to attend these training sessions. To handle advanced integrations like cameras, barcode readers or force sensors, you could have thousands in additional classroom costs. Multiply those costs by a factor of 2-5 if you want to have redundant programming resources on your factory floor!

  1. There’s so much more than the robot arm.

Industrial robot arms do not work in isolation. They must have their own movements which must be coordinated with the items attached to the arm such as the end of arm tooling, sensors, and safety equipment. But there are other components in the workcell that need to work in tandem with the arm. There needs to be communication between the other peripherals such as force sensors and vision systems, machine tools, parts presentation, and other equipment in the cell. This adds to the level of complexity for automating any task.

For a deeper dive into the difficulties of programming robots, download our free white paper.