- 10 Haz 2020
When you analyze a metal forming process, you begin to see the many attributes that influence the quality of a stamper's processes and components.
Controlling manufacturing variables leads to improved production results
A system analysis of the forming process reveals the many variables that lead to success or failure. Most of the variables in forming are related to:
- Material behavior.
- Boundary conditions.
Stamping as a SystemWhen you look beyond the hardware, stamping is primarily a system of inputs, processes, and outputs. When you analyze them, you begin to see the many attributes that influence the quality of your processes and components. When you’ve identified these attributes, you can:
- Develop a plan to understand those attributes and their influence on forming,
- Measure the properties of inputs and supporting process components to determine their compliance with specifications, identify correlations between attribute values and production events, and determine possible causes of undesirable events.
- Manage operations and material inputs for better production results.
Once you know the general categories of inputs and outputs, you can start to break down each category into the information and attributes that influence forming results. A brief, but not comprehensive, list of measurable attributes is shown in Figure 2.
If you measure your important attributes, software can help identify the properties, control attributes, and process measurements most likely to lead to equipment and component quality failure, as well as the range of attributes most likely to build quality components. Using this information, you can:
- Manage the design, specification, receipt, and quality of coils and blanks.
- Improve your simulation results for die design.
- Identify issues and manage press controls and operations.
- Plan equipment maintenance for reliability.
Manufacturing for DesignBoth the design and production organizations must build channels to share and listen to each other’s needs and experiences. Better design comes from greater learning and understanding of the results from previous components.
Design for manufacturing is a common term that defines the need to design components in a way that supports efficient manufacturing. Conversely, production also needs to support design through feedback loops—sharing lessons learned in production with the design team. Production lessons can be valuable to the design team for improved component and production design. And of course, improved design will in turn help improve production results.
In addition to feedback loops, the Internet of Things offers tremendous opportunity for continuous learning and improvement. Both design and production can identify design-related problems and learn to develop better component designs. This can lead to fewer die tryouts and better manufacturing processes.