Results

Allied Business Intelligence Inc.

31/07/2024 | News release | Distributed by Public on 01/08/2024 02:20

Offline Programming Software: The Vehicle for Industrial Robotics Innovation and Growth

By George Chowdhury | 3Q 2024 | IN-7472

Registered users can unlock up to five pieces of premium content each month.

Log in or register to unlock this Insight.

The Evolution of Industrial Robotics Programming

NEWS

Integrating a new robot into any existing process requires downtime. For some manufacturing plants-notably automotive-an hour of downtime can translate into millions of dollars in lost production. Modern Offline Programming (OLP) and virtual commissioning methodologies have transformed this process. Historically, industrial robots were trained using teach pendants-a device issued with every industrial robot that allows a technician to manually control, configure, and save a series of step-by-step commands to a robot controller. Technologies have since evolved.

Modern programming methodologies-dubbed "Offline Programming (OLP)" as the program is generated prior to deployment on the physical robot-incorporate a Three-Dimensional (3D) simulation of the robot and the work cell or process it operates within. Robot vendors and digital transformation specialists provide intuitive "drag-and-drop" and "click-to-direct" interfaces, allowing engineers to control simulated robots at a high level, without needing in-depth knowledge of programming languages.

Via this technique, a robot program can be generated in a matter of minutes; previously, it may have taken hours or days, thus providing dramatic time and cost saving benefits to stakeholders.

Robot Programming Software Is the Vehicle for Industrial Robotics Innovation

IMPACT

Every robotics manufacturer provides some form of OLP software for programming its robots (examples include FANUC's ROBOGUIDE, KUKA's KUKA Sim, and Yaskawa Motoman's MotoSim). A secondary vendor ecosystem (including vendors such as RoboDK, OCTOPUZ, Visual Components, and Robotmaster) provides interoperability between robot brands and advanced tasking capabilities (such as palletization or welding).

Key capabilities for addressing pain points and increasing accessibility provided by OLP software including the following:

  • Machine Learning Optimization: Some vendors (e.g., ABB and Visual Components) now include Artificial Intelligence (AI) for optimizing robot paths. This can be used to both reduce the cycle time of an automated work cell or process and reduce energy usage by effectively managing robot motion-significant accelerations.
  • Cloud Collaboration: With cloud-hosted simulations, OLP software permits stakeholder tie-in from across an organization. Cloud-hosted analytics enables production managers to assess processes and identify bottlenecks with ease, while simulation for planning large workflows and work cell designs grant decision makers mock-up abilities to validate Proofs of Concept (PoCs), while securing buy-ins from other stakeholders.
  • Computer-Aided Design (CAD): The incorporation of CAD has become table stakes for OLP software. Software vendors provide the ability to import both the CAD model of the robot and other hardware in the workflow and the product being manipulated. This has enabled greater fidelity in simulation, along with a streamlined digital shadow of the entire automation process supporting Product Lifecycle Management (PLM).

Taken together, these features are indicative of a larger paradigm shift toward digital infrastructure supporting robotics hardware.

OLP software, as a digital platform, provides an effective avenue for innovation into the industrial robotics vertical, which-due to its closed ecosystem-may appear resistant to robotics innovation. This is especially true of AI. The prospect of AI control for robotics is more palatable to decision makers if it is delivered piecemeal. Software vendors win favor by demonstrating how AI can improve a particular process, supported by simulating and quantifying efficiency gains.

Digital Frameworks Are Critical for Greater Robotics Adoption

RECOMMENDATIONS

ABI Research forecasts steady shipment growth of at a 7.3% Compound Annual Growth Rate (CAGR) for industrial robots through the end of the decade. Virtual commissioning and OLP paradigms will be instrumental for deploying these assets. The emerging Collaborative Robot (cobot) form factor is expected to see explosive growth throughout the same period with a CAGR of 28.1%. Supporting software will play an equally important role in its uptake.

Many digital transformation specialists (notably Siemens, Dassault Systèmes, and ABB) provide software services that extend beyond programming and into commissioning. Virtual Commissioning allows engineers to configure a robotic asset within the context of a greater workflow, essentially validating integration prior to physical deployment. Facets of virtual commissioning can include Hardware-in-the-Loop (HiL) and Software-in-the-Loop (SiL) where real hardware is tested with artificial software inputs or vice versa. Integrating robots into larger processes remains the most time-consuming stage of a robot deployment. The same trio of companies provide comprehensive PLM solutions that hinge on a digital shadow of an ongoing process. Stakeholders can use PLM software to identify bottlenecks and efficiency improvements in an ongoing process.

Ultimately, OLP software innovations are highly beneficial for increasing industrial robotics value, enabling accessibility and providing transparency for automated processes. Small and Medium Enterprises (SMEs) can enjoy streamlined robotics adoption via OLP software that offers an intuitive, easy-to-use interface and pre-programmed tasks (painting, palletizing, picking, etc.). Larger organizations can enjoy the same benefits by leveraging virtual commissioning functionality to ensure minimal loss of earnings when a new asset is integrated into an existing automated value chain. Both SMEs and large enterprises can benefit from PLM that provides ongoing insight and AI-powered optimization suggestions for ongoing processes.