Boeing's recent incidents have raised concerns about quality and safety. Some blame maintenance issues, not manufacturing. Regardless, it's a cautionary tale.
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The engineering work landscape is rapidly evolving with technologies like artificial intelligence, robotics, and additive manufacturing. Many engineers lack practical skills, and the industry faces a significant skills gap. Read how addressing this gap is crucial for engineering learning and development.
Here are a handful of example quality control processes that focus on the measurement of torque, with emphasis on sectors where testing is highly regulated, FDA 21CFR Part 11 being a prime consideration.
Real-world problems often have multiple causes, so it's beneficial to extend your root cause analysis by considering the problem from various perspectives.
The most frustrating moment in problem-solving is fixing an error only to see it reappear the next day. Until the root cause is identified and addressed, the issue will persist.
A quality management system program ensures that products and services meet predefined standards, customer expectations, and company-wide benchmarks. One key principle is "first time right," addressing errors immediately at every step.
AI, despite its hype, often causes delays in manufacturers' automation strategies due to confusion and fear. Fundamentally, AI complements machine vision, which uses handcrafted algorithms needing new formulas and trial-and-error development for each product type or feature.
Manufacturing for military readiness not only involves the initial production of arms, equipment, and technology but also the ongoing replacement and repair of degraded, outdated, or lost items, necessitating the use of the latest technology to enhance efficiency and effectiveness.