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Vision Technology in Medical Manufacturing

2023-12-14 05:00:04
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Illustration: © IoT For All



In the world of modern medicine, there’s one tiny component that plays a colossal role. It’s the kind of component that finds its way into your arteries during procedures like an angiogram or takes center stage in life-saving organ surgeries.

The mere thought of its importance might send a shiver down your spine, and for a good reason. The level of precision and quality in these small parts is quite literally a matter of life and death for the patient on the operating table.

As medical science advances and we uncover the intricacies of ever more complex diseases, the demand for these minuscule yet critical components is surging. They’re the unsung heroes of the operating room, the silent champions of our health. But producing these parts is no small feat. Factories must churn them out in bulk, and they must meet not only the highest standards of quality but also do so quickly and without room for error.

So, how do we rise to the occasion? Enter the cutting-edge world of vision technology and the transformative force of Industry 4.0. In this article, we’ll explore how the integration of robotics and vision systems is revolutionizing medical manufacturing, ushering in a new era of precision and safety.

Visual Inspection

Vision has emerged as a critical tool in ensuring the quality of products within the realm of the manufacturing industry. Over the past decade, computer-aided vision systems have progressively taken over human roles in quality inspection, marking a significant advancement in this sector. Continuous innovation and improvements in vision technology have opened avenues for inspecting intricate components, thereby minimizing human errors to a considerable extent.

The current capabilities of visual inspection in medical manufacturing extend to the examination of both 2D and 3D dimensions with a predefined scale. This technological prowess plays a pivotal role in maintaining the high standards required for medical devices and equipment. The impact is not only qualitative but also extends to cost savings, realizing an annual saving of approximately $200 million across five major sectors, including medical manufacturing.

Companies can contribute to the seamless integration of computer-aided vision into the medical manufacturing landscape, ensuring precision, efficiency, and compliance with stringent quality standards. As the medical manufacturing sector continues to evolve, the reliance on cutting-edge visual inspection technologies is expected to grow, further enhancing the overall quality and reliability of medical products.

Quality Metrics & SPCs

The utilization of advanced vision machines in the manufacturing industry yields two distinct types of data as output. First is the quality metrics data, representing analog and individual measurement data in numeric form. In medical manufacturing, where precision is paramount, these metrics serve as crucial indicators. They are subject to predefined limits that determine the eligibility of each part for use in real-time medical environments.

Industry 4.0 application, tailored for the intricacies of medical manufacturing, seamlessly integrates with these advanced machines. Through standard protocols, the application retrieves quality metrics data and compares it against predefined specifications. This real-time analysis allows for swift conclusions regarding the quality outcome of each part. The gathered data can be stored and visually represented as graphs, facilitating in-depth data analysis for continuous improvement.

Moreover, these advanced machines are equipped to provide decisive data such as Accepted or Rejection based on predefined settings. This information is not only valuable in terms of immediate decision-making but also aids in the segregation of parts into respective bins.

The granularity of metrics data, coupled with specific decisions, proves instrumental in further categorizing parts. This, in turn, facilitates informed decisions regarding rework and rejection, ensuring that only components meeting the highest quality standards proceed through the manufacturing process.

Vision Technology, Robots, and Classifications

With the advent of IoT and connected machines, we can go one more step to remove man-made errors completely from the system. Robots at the production line can do the monotonous and predefined work of placing the medical part on the vision table and placing it back into the bins. Sustainability plays a key role in medical manufacturing not only from an environmental or waste perspective but also from a profit and revenue angle.

Every factory, including medical, should and will have two levels—rejection and scrap. Sending everything directly to scrap increases the raw material cost and hence the overall cost. Rejections can be classified into multiple groups based on the issues caused or the kind of rework required. The segregation of the parts into rejections and scrap based on the data should use the Industry 4.0 and data analytics application with the data and the signals received from the vision system.

Significance of Industry 4.0 Application

The digital transformation of the factory is an essential component of the Industry 4.0 application. It involves the consolidation of critical information encompassing processes, products, machines, and quality metrics within a unified platform tailored to the unique demands of medical manufacturing.

The application’s strength lies in its ability to perform correlation analyses on medical components, drawing insights from comprehensive data spanning multiple operations and including raw material information. This depth and breadth of data enable the application to make informed decisions regarding the acceptance or rejection of medical parts. In a sector where precision and quality are non-negotiable, this analytical capability proves invaluable.

The integrated Artificial Intelligence (AI) and Machine Learning (ML) components within the application further elevate its functionality. These advanced technologies can raise alarms in real time, providing immediate feedback to the preceding production processes.

In instances where rejection rates exceed acceptable thresholds, the system can take decisive action, including stopping the production process to prevent the proliferation of substandard components. This proactive approach maintains the highest levels of quality and adherence to industry standards in medical manufacturing, ensuring that production standards are not compromised and underscoring the application’s role in this process.

Impact on the Medical Manufacturing & the ROI

The impact of implementing advanced technologies in medical manufacturing is transformative, yielding substantial improvements in overall productivity. In this context, the sector can anticipate a minimum 50% increase in productivity, marking a significant leap forward. Particularly noteworthy is the elimination of errors and the need for re-inspection in the quality control process, contributing to a more streamlined and efficient production workflow.

The return on investment (ROI) is expedited due to the immediate enhancements in productivity. With the implementation of these technologies, the RoI period likely ranges between 3 to 6 months, contingent on the intricacies of the specific medical manufacturing process and product involved. This rapid RoI underscores the tangible benefits and cost-effectiveness of integrating advanced technologies into the medical manufacturing landscape.

This comprehensive improvement in productivity and quality is especially well-suited for shop floors engaged in mass inspection of similar medical components. The streamlined processes and increased efficiency contribute to a seamless integration of these technologies, resulting in a substantial positive impact on the overall manufacturing landscape.

For smaller volumes in medical manufacturing, there is still an enhancement in quality, although the configuration and setup costs may vary depending on the unique characteristics of the product for inspection. Despite potential cost variations, the overall impact on quality remains a significant advantage, showcasing the adaptability of these technologies across different scales of medical manufacturing operations.

Conclusion

In conclusion, the amalgamation of vision technology and Industry 4.0 is not merely a technological evolution; it is a revolution in precision and reliability within the medical manufacturing domain. As we continue on this path, we anticipate not only further advancements in technology but also a profound impact on patient outcomes and the overall landscape of healthcare. The journey to precision in medical manufacturing is ongoing, and through the lens of vision technology and Industry 4.0, the future holds the promise of unparalleled advancements and life-changing innovations.



参考译文
医疗制造中的视觉技术
插图:© IoT For All 在现代医学领域中,有一种微小的组件却起着巨大的作用。这种组件会出现在像血管造影这样的手术中,进入患者的动脉,也可能是生命抢救器官手术中的关键部分。仅仅想到它的重要性就足以让人胆战心惊,而这种感觉是有充分理由的。这些小零件的精度和质量,对于手术台上病人的生死,可以说是直接相关的问题。 随着医学科学的进步,我们不断揭示越来越复杂的疾病的奥秘,这些微小但关键的组件需求也日益增长。它们是手术室里的无名英雄,是我们健康的无声冠军。但制造这些零部件并不是件小事。工厂必须大量生产,不仅要满足最高质量标准,而且还要快速且无误地完成。 那么我们如何应对这一挑战?欢迎进入视觉技术的前沿世界和工业4.0的变革力量。在本文中,我们将探讨机器人与视觉系统的融合如何革新医学制造,开启一个精准与安全的新时代。 **视觉检测** 在制造业领域,视觉已成为确保产品质量的关键工具。在过去十年中,计算机辅助视觉系统逐步取代人工质量检测,标志着这一行业的重要进展。视觉技术的不断创新与改进,为检测复杂组件打开了新的可能性,从而大幅减少了人为错误。 目前,医学制造中视觉检测的能力已能对2D和3D维度进行预定义比例的检测。这种技术实力在保持医疗设备和仪器高标准方面起着至关重要的作用。其影响不仅体现在质量上,还带来了显著的成本节约——仅在医疗制造等五大行业,每年就可节省约2亿美元。 企业可以推动计算机辅助视觉系统在医学制造领域的无缝整合,确保制造过程的精准、高效,并符合严格的质量标准。随着医学制造行业的不断发展,对先进技术的依赖预计将逐步增加,从而进一步提升医疗产品的整体质量与可靠性。 **质量指标与统计过程控制(SPC)** 在制造业中使用先进的视觉机可产生两种不同类型的数据。第一种是质量指标数据,以数值形式表示模拟和独立测量数据。在医学制造中,精度至关重要,这些指标成为关键指标。它们会根据预定义的限值进行判断,以决定零件是否适合在实时医疗环境中使用。 为满足医学制造的复杂需求而量身定制的工业4.0应用,可以无缝整合这些先进机器。通过标准协议,该应用可检索质量指标数据,并将其与预定义规格进行比较。这种实时分析使我们能够快速得出每个零件的质量结论。所收集的数据可被存储,并以图表形式直观展示,便于深入的数据分析和持续改进。 此外,这些先进的机器还能根据预定义设置提供明确的决策数据,如“接受”或“拒绝”。这些信息不仅对即时决策有价值,还帮助将零件分拣到相应的分拣箱中。精确的指标数据和明确的决策有助于进一步分类零件,从而做出关于返工和拒绝的明智决策,确保只有符合最高质量标准的零件才能继续制造流程。 **视觉技术、机器人与分类** 随着物联网(IoT)和联网机器的兴起,我们可以更进一步地从系统中彻底消除人为错误。生产线上的机器人可以执行单调且预定义的工作,例如将医疗零件放置在视觉检测台上,然后将其放回分拣箱中。 可持续性在医学制造中扮演着关键角色,不仅从环境和废物的角度来看,还从利润和收入的角度来看。每一家工厂,包括医疗工厂,都应且将分为两个层级——“拒绝”和“废料”。将所有零件直接归为废料会增加原材料成本,从而提高整体成本。拒绝的零件可以根据问题的类型或所需的返工方式被分类到多个组别中。基于数据对零件进行拒绝和废料的分类,应结合工业4.0和数据分析应用,利用来自视觉系统的数据和信号。 **工业4.0应用的重要性** 工厂的数字化转型是工业4.0应用的重要组成部分。它涉及将流程、产品、机器和质量指标等关键信息整合到一个统一平台上,该平台可根据医学制造的独特需求进行定制。 该应用的优势在于其能够对医疗组件进行相关性分析,从涵盖多个操作环节的全面数据中提取洞见,包括原材料信息。这种数据的广度和深度使应用能够就医疗零件的接受或拒绝做出明智决策。在精度和质量不可妥协的行业,这种分析能力极具价值。 该应用中整合的人工智能(AI)和机器学习(ML)组件进一步提升了其功能。这些先进技术能够实时发出警报,向之前的制造流程提供即时反馈。在拒绝率超过可接受阈值的情况下,系统可以采取果断措施,包括停止制造流程以防止次优组件的蔓延。这种主动方式确保了医疗制造中最高质量标准和行业规范的遵循,同时突显了该应用在这一过程中的作用。 **对医学制造的影响与投资回报(ROI)** 在医学制造中实施先进技术的影响是变革性的,带来了整体生产力的显著提升。在这一背景下,该行业预计整体生产力将至少提高50%,标志着巨大的飞跃。特别值得注意的是,质量控制流程中错误和复检需求的消除,有助于构建更高效且流畅的制造流程。 由于生产力的即时提升,投资回报(ROI)会加快。这些技术的实施使ROI周期很可能在3到6个月之间,具体取决于特定医学制造流程和产品的复杂性。这种快速的ROI突显了将先进技术整合到医学制造中的实际效益和成本效益。 生产力和质量的这一全面提升,特别适用于负责批量检测类似医疗组件的车间。这些技术的流程优化和效率提升使它们能够无缝整合,对整个制造领域产生显著的正面影响。 在医学制造的小批量生产中,质量方面也有提升,尽管配置和设置成本可能会因待检测产品的独特特性而有所变化。尽管成本可能有所波动,整体质量的提升仍然是一个显著优势,展示了这些技术在不同规模医学制造操作中的适应性。 **结论** 总而言之,视觉技术与工业4.0的融合不仅是一次技术的进化,更是医学制造领域中精度与可靠性的革命。在这条道路上,我们不仅期待技术的进一步发展,还期待其对病人结果和整个医疗领域产生深远影响。医学制造的精度之旅仍在继续,而通过视觉技术与工业4.0的视角,未来将充满前所未有的进步和改变生活的创新。
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