With continuous advancement of technologies and inventions of smarter products, the engineering processes are also getting more complex and sophisticated that need constant optimizations.
As seeing in automotive industry, the rapid transition to electric vehicles (EVs) in recent years has driven many technological developments with new challenges. Optimizations are essential through all engineering stages of development from design to production process and manufacturing systems.
However, the methods of optimizations are also advancing with developments of new technologies.
The traditional method of optimization is known for trial-and-error tests by human experts. Engineers take very long time to gain experiences and learn the skills to become experts. A big pity is that the expertise achieved by human learning is stored in human brains. It is difficult to transfer to other engineers.
With developments of computer technologies, the optimizations are gradually made more and more by computers and decisions made by more contributions of computer systems.
As illustrated in the graph, we can divide the development of optimization methods in manufacturing industry into 5 stages based on the contributions of computer technologies.
- Human expert
– This is the old way of working by human experts conducting all optimizations. The decision making is by human experts only.
- CAD – computer aided design
– In this stage, computer systems are used for engineering design and design optimizations. It is already state of the art with CAD systems widely applied in all industries.
- CAE – computer aided engineering
– In this stage, computer systems are used for production process optimizations to achieve optimal and reliable manufacturing processes.
- CAM – computer aided manufacturing
– In the stage, computer systems are adopted for production control optimizations to realize the manufacturing of products with good and consistent quality.
- AI – artificial intelligence and digital factory
– In this stage, computer systems will be able to make autonomous decisions for optimizations of designs, production processes and manufacturing systems with automatic control to produce the best quality products.
Some industries have already achieved higher levels of CAM integrated with CAD, such as additive manufacturing. This is mainly because the CAM systems for 3D printing can be linked directly with CAD.
However, for welding, there is a big gap between CAD and CAM due to complexity of the welding processes. This gap need to be filled by optimizations of the welding processes. There are still very much work for optimizations done by human experts but with increasing applications of computer technologies.
SORPAS is used as a CAE tool in automotive industry and electrical industry for optimizations of the resistance welding processes.
We hope the welding optimizations will be conducted with more and more contributions by computer systems with CAE and machine learning to gradually integrate CAD with CAM towards artificial intelligence (AI) and digital factories.