Product Specific Process Knowledge

 

 

 

Process optimisation is a methodology of using existing process knowledge to discover new process knowledge by studying patterns in data. Process optimisation relates to the ability of the process to achieve the business goal whether that is defect reduction, enhancing mechanical properties of products, improve margins or for any other technical / commercial benefit.     

 

Product specific process knowledge is defined as:

  • the actionable information
  • in form of optimal list of measurable factors and their ranges (Niobium: 0.77% – 0.827%; Aluminium: 3.24% - 3.306% Zirconium: 0.026% – 0.05%;  Carbon: 0.095% – 0.113%; )
  • in order to meet desired business goals (process responses) (e.g. minimize defect rates, porosity scores or rework time etc. and/or maximize mechanical properties)         

 

Need for defining product specific process knowledge:

  • The optimal process conditions to manufacture a product (or cast component) are not same for every company.
  • The intellectual property on optimal process conditions and design specification is a closely guarded secret.                

 

Benefits of recording and reusing product specific process knowledge:

  • Stimulates the culture of innovation within the company by involving everyone who could influence the process.
  • The simultaneous adjustment of multiple factor settings is called as ‘tweaking the process’ or ‘tolerance limit optimisation’. Foundry experts do this all the time. 7Epsilon gives a formal evidence based structure to this process so that the entire process improvement team benefits by gaining new knowledge that has potential to save money.
  • Recording company and product specific insights gained over a period of years will help companies to be able to assign a value to the intellectual property on their balance sheet.          

          

Opportunities for discovering product specific process knowledge:

  • Visualisation of multivariate data analysis projects for most casting processes where in-process data is collected within a casting process and several factors are linked with one or more process responses via date and time of manufacture along with a corresponding batch/heat number or a unique identifier.  
  • Creation of new hypotheses. New insights are turned into actionable information when a process engineer undertakes a literature review and selects optimal levels of combination of several factors to design a Confirmation Trial . New hypotheses are tested by performing a confirmation trial showing reduction in variation of response. This process creates new product specific process knowledge and maintains an environment where innovation / creativity can flourish.