In the industry 4.0 and digital transformation era, PLM is transforming the manufacturing companies. Dot com bubble promoted ERP implementations and Digital transformation is doing the same for PLM. Within last 20 years PLM evolved from product data management (PDM) tool to portfolio of applications support entire product development process.
In the last 5 years, with the introduction of Webgl, IoT, Augmented Reality/Virtual Reality, Machine learning and Artificial intelligence tools, PLM is transforming into a product innovation platform. We will see some of the advancements which is happening in PLM in the below sections.
PLM applications core data is CAD and related details based on its attributes. Before the introduction of Webgl 2.0, all the major PLM vendors were using native applications to display the CAD model. If you have to display the CAD model in a web browser, you need separate Adobe/Java plugins.
With the introduction of Webgl all the PLM vendors started migrating their applications to browser-based interfaces as there is no need for plugins to display the CAD models. Teamcenter active workspace, Thingworx Navigate and complete suite of 3D experience platforms all run in browsers. Major advantage of this function is downstream consumption of CAD data across the organization and no need of application installation in client system. Cloud transformation is another factor fueled by Webgl.
Ultra-lightweight precision (ULP) models are the enablers of browser-based PLM applications. By using ULP, Native CAD files can be compressed from 1GB to 100KB with precise geometries. Due to their smaller size, ULP files load much faster in browsers. We can measure the ULP models, change face color specifications and include PMI.
1. Browser based PLM applications
2. Downstream CAD consumption
3. Ultra-lightweight Precision models
Data is the core element of IoT, either it is from sensors or system of records, viz (PLM, ERP, MES, SCADA etc.). Variety of applications are developed on top of PLM applications combined with IoT data. This hybrid applications are under early stages of development and we can see a huge improvement in near future. Connecting with IoT platform is done by using integration connectors and mash-up applications are compiled by using data from PLM and other applications. Below are some of the example applications built based on PLM and IoT data.
PTC - Thingworx manufacturing apps
Siemens – Intosite
Actual performance overlayed on CAD models
Data collection definition from devices and reporting
Product validation in the early stages of product development is hard because there is no way we can touch and feel the product. VR enables virtual prototyping of the product, interaction, and merge with immersive user experience.
There were tools (Virtalis, Un-Real engine etc.) which enabled VR experience, but we need to take the data out of PLM and import into these tools. With the introduction inbuilt VR capabilities in PLM, there is data continuity and effective change management. By adding ergonomic features, we can validate the product as well as production processes in real time. Teamcenter and Tecnomatix VR capabilities and 3D experience VR capabilities are few examples.
Virtual prototyping for new product introduction
Ergonomic study and workplace optimisation
All the discrete manufacturing companies need work instructions to manufacture and service the product. Training materials are created to train the workers in new products and train the new employees. Traditionally work instruction authoring is done in PLM system and delivered through paper printed or through computer screens.
Within last 3 years, content development for AR within PLM system is getting improved. Creating content for different devices and integration of MES system are major challenges in delivering AR based instructions. With the closed loop manufacturing (CLM) & closed loop quality (CLQ) integration, this issue will be solved. Content creation for various devices are under development and will take another few year to mature.
Work instruction creation for service and manufacturing departments
Training delivery for new products and new joiners
Machine Learning & Additive Manufacturing
Machine learning and artificial intelligence plays huge role in our day to life. Use cases within the PLM/CAD industries are exciting and tools are available in the market. Part classification by using geometry, Fashion’s Future demand model and generative shape design are key examples.
Most of the large and medium sized OEM’s were using PLM more than 15 years. Huge amount of data is created in PLM and it is a mammoth task to classify the parts based on its geometry. ML&AI algorithms solve this problem by giving suggestion based on its geometry. Above video gives an example, how part classification is performed within the PLM system.
Fashion’s Future demand
Creative designers in fashion industries use enormous amount of time to search the materials, trims, colors, and styles. By using AI enabled image search, designers can take a picture and compare with the existing library. Results are generated within few minutes and if a similar item is used or approved. If there is no closer item found, it will give a supplier who provides the similar match.
Generative Shape Design
Product design is an iterative manual process, where multiple concepts are generated and validated against size, loads, weight, materials, manufacturing feasibility and cost. By using AI enabled generative shape design engineers can create 1000’s of designs in fraction of time. Apart from that complex designs can be generated for additive manufacturing as well. With the introduction of light weight convergent modeling techniques, designs can be visualized in a short period of time.
Changing customer needs and customized products, need product innovation platforms to deliver products in a shorter period. PLM technology is improving, and additional features based on these new technologies enable shorter NPI time and enable customized product development.