Download PDF
Automotive Lighting Enhances Rear Lamp Design and Rendering with solidThinking Evolve
Technology Category
- Application Infrastructure & Middleware - Data Visualization
- Sensors - Optical Sensors
Applicable Industries
- Automotive
- Equipment & Machinery
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Smart Lighting
- Vehicle-to-Infrastructure
The Challenge
Automotive Lighting, a global leader in exterior automotive lighting, faced a challenge in delivering brand-specific styling and rendering of rear lamps. The rear lamps are not only crucial safety components but also serve as strong brand indicators, incorporating important styling elements that define the appearance and identity of a particular vehicle model. The company needed to ensure these safety and brand elements received precise attention to detail. The complexity of the components, including metallic reflectors, light bulb or LED sources, embossed and transparent elements such as polycarbonate lenses, made the rendering process challenging. Furthermore, the rendering process had to consider the dual usage of automotive lamps, which are switched off during the day and switched on during the night.
About The Customer
Automotive Lighting is a leading company in the research, development, production, and sale of a complete range of technologies for front and rear lighting, fog lights, headlamp cleaning systems, leveling systems, electronic components, and central high mounted stop lamps. Formed in 1999 from a joint venture that merged the lighting technology divisions of Robert Bosch GmbH and Magneti Marelli Spa (Fiat Group), the company has grown to be a global leader in exterior automotive lighting. Today, it produces more than 66.4 million pieces per year, including headlights, rear lamps, and other lighting components, with over 10,000 employees, nearly 1,000 of whom are involved in R&D.
The Solution
Automotive Lighting adopted solidThinking Evolve’s free-form modeling tools and integrated rendering to address these challenges. The software was used in three phases of the product development cycle: ideation, development, and visualization. In the product concept phase, Evolve’s free-form modeling tools and integrated rendering functionalities allowed for quick styling of rear lamps. The software provided the ability to reliably exchange data with engineering software such as Catia V5, enabling the creation and pinpointing of new style solutions. The best-in-class ConstructionTree history feature of Evolve saved time when making changes to the design. Additionally, solidThinking Evolve’s rendering engine acted as a validation, analysis, and visualization tool, helping to accurately render the complex aspects of the automotive lamps.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Smart Water Filtration Systems
Before working with Ayla Networks, Ozner was already using cloud connectivity to identify and solve water-filtration system malfunctions as well as to monitor filter cartridges for replacements.But, in June 2015, Ozner executives talked with Ayla about how the company might further improve its water systems with IoT technology. They liked what they heard from Ayla, but the executives needed to be sure that Ayla’s Agile IoT Platform provided the security and reliability Ozner required.
Case Study
IoT enabled Fleet Management with MindSphere
In view of growing competition, Gämmerler had a strong need to remain competitive via process optimization, reliability and gentle handling of printed products, even at highest press speeds. In addition, a digitalization initiative also included developing a key differentiation via data-driven services offers.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Premium Appliance Producer Innovates with Internet of Everything
Sub-Zero faced the largest product launch in the company’s history:It wanted to launch 60 new products as scheduled while simultaneously opening a new “greenfield” production facility, yet still adhering to stringent quality requirements and manage issues from new supply-chain partners. A the same time, it wanted to increase staff productivity time and collaboration while reducing travel and costs.
Case Study
Integration of PLC with IoT for Bosch Rexroth
The application arises from the need to monitor and anticipate the problems of one or more machines managed by a PLC. These problems, often resulting from the accumulation over time of small discrepancies, require, when they occur, ex post technical operations maintenance.
Case Study
Robot Saves Money and Time for US Custom Molding Company
Injection Technology (Itech) is a custom molder for a variety of clients that require precision plastic parts for such products as electric meter covers, dental appliance cases and spools. With 95 employees operating 23 molding machines in a 30,000 square foot plant, Itech wanted to reduce man hours and increase efficiency.
