“…Molcho et al [154] create a computer aided manufacturing analysis tool that captures the knowledge and incorporates it within the CAD tool, enabling improved product timeliness and profitability. It makes the ''know-how'' available to designers in the context of their specific design activity and can thus influence decisions before the product design in the early stage.…”
Section: Knowledge Generation For the Ips 2 Redesignmentioning
“…Molcho et al [154] create a computer aided manufacturing analysis tool that captures the knowledge and incorporates it within the CAD tool, enabling improved product timeliness and profitability. It makes the ''know-how'' available to designers in the context of their specific design activity and can thus influence decisions before the product design in the early stage.…”
Section: Knowledge Generation For the Ips 2 Redesignmentioning
“…The knowledge base will contain human cognition useful for problem solving in the form of rules relating to modern plastic materials' selection and correlated manufacturing processes, assisted by the field of Design for Manufacturing (DFM) (Molcho et al, 2008;Sevstjanov & Figat, 2007). Different approaches to knowledge acquisition (McMahon, Lowe, & Culley, 2004) and the appropriate formalisms for the presentation of acquired knowledge (Valls, Batet, & Lopez, 2009) within the computer program will be of special importance.…”
Section: Intelligent Decision Support System For Designing Plastic Prmentioning
“…Regulatory authorities are demanding a greater level of process characterisation and robustness in the biopharmaceutical industry as a means of ensuring consistent supply of safe, efficacious medicines to patients. However there remains a gap between the huge quantities of manufacturing data available and how much knowledge the industry derives from this data [15]. Regular changes to the production processes are inevitable in a manufacturing industry particularly when the strong culture of continuous improvement inherent in 6 exists.…”
Section: Vaccine Manufacturing Challenges and Opportunitiesmentioning
Traditionally, the Six Sigma framework has underpinned quality improvement and assurance in biopharmaceutical manufacturing process management. This paper proposes a Neural Network (NN) approach to vaccine yield classification. The NN is compared to an existing Multiple Linear regression approach. This paper shows how a Data Mining framework can be used to extract further value and insight from the data gathered during the manufacturing process as part of the Six Sigma process. Insights to yield classification can be used in the quality improvement process.
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