Impact of Automating Quality fueled by Artificial Intelligence
Data: The Fuel of Today for A Better Future.
Automation Impact on the industrial Sector
Since the 3rd industrial revolution and there is a big change in the industrial sector in both of the manufacturing operations and business process. Automation reduced the human errors by standardized operating procedures and keep employees safe and increasing productivity with availability to work 24/7. In a report of McKinsey Global Institute (2017) estimated that ‘automation could raise productivity growth globally by 0.8 to 1.4 percent annually’.
Today, there is a big data come from machines, sensors, people, systems, …etc. So, what if we turn these data into useful insights and actions! That is a part of industry 4.0 mission: analyzing the data and turning it into actionable & useful insights.
Industry 4.0 means building connected environment of data, people, processes, services, systems and IoT-enabled industrial assets with the generation, leverage and utilization of actionable information as a way and means to realize smart industry and ecosystems of industrial innovation and collaboration.
Data analytics framework consists of four stages: Descriptive [from Data to information] – Diagnostic Metrics [from information to knowledge] – Predictive [predict failure and specify what should be done to address or change the outcome, and usually include some level of autonomous behavior.] and the fourth stage is Prescriptive [autonomous level].
Predictive and prescriptive levels realized with Machine Learning (ML) and Artificial Intelligence (AI).
Quality Inspection Process
"Manual process" vs "Automated process" vs "Automated process + AI"
Quality is the main factor for a competitive product. Let’s have a look at the different ways of quality control: Manual inspection, Automated Inspection and what if added AI to Automated inspection fueled by AI. Which is better for business scalability.
1.Manual Quality Control
Simply, a human inspector detect the defects on the product. But it has many problems such as: accuracy – Missing standardization, Not suitable for the high speed lines – High cost of business scalability.
2.Automating Quality inspection process
Automating the quality control by replacing the human inspection with Industrial camera/sensor for capturing images for the product in any phase of production and then making processing for the images then taking actions [Machine Vision System]. Although automating quality control is solving the manual inspection problems of accuracy, increasing productivity, suitable for high speed with ability to real time monitoring and inspecting from the production line to your PC and extracting customized and standardized reports but it missed flexibility, for example in Textile industry, classic machine vision systems can’t work on all types of fabric!.
3.Automating Quality fueled by AI:
Under the umbrella of Industry4.0, quality meaning changed to include parameters of quality related to Product, machines, Business processes, supply chain, people, technology, …etc.
Applying Artificial Intelligence [Deep learning or machine learning] enables machines to adapt to very heterogeneous contexts just like humans while doing tasks and learn how to communicate, identify visual patterns through combining and analyzing data of sound, Vision System [images or videos], outputs & reports of sensors, machines and maintenance logs as well as external sources. And it can be trained to perform highly value-adding tasks such as predictive maintenance or performance optimization at unprecedented levels and Continuous improvement of accuracy and efficiency by increasing the datasets.
Automated quality testing can be realized using AI. By employing advanced image recognition techniques for visual inspection and fault detection, productivity increases of up to 50% are possible. Specifically, AI-based visual inspection based on image recognition may increase defect detection rates by up to 90% as compared to human inspection. [According to Mckinsey report of Germany industrial sector click Here]
Co-Creation Programs: Startups Solutions solve leaders Challenges
Today Enterprises/Manufacturers leaders are taking actual steps toward digital transformation, Most of them are looking for flexibility of applying Artificial Intelligence!. And it leads to create programs connect Enterprises/Manufacturers leaders with startups to solve their problems “Co-creation Program”. Examples: Bind4.0, Beyond Conventions And VivaTech challenge
DevisionX is selected – as startup – to build smart inspection system to solve quality problems for the globally leading technology group Heraeus in collaboration with Vinci Energies.
In many manufacturing processes at Heraeus an inspection system is used to assess the quality of the intermediate and final products measurements are still evaluated and performed manually by trained personnel, Learn more about the challenge “1“ from here.
DevisionX is building Quality Inspection systems – integrated with industry 4.0 – for solving the quality challenges of the manufacturing processes that is related to product, machine, suppliers, …etc by using mix of Machine Vision and Deep Learning technologies. Our solutions can be applied to many industries such as Food & Beverage, Automotive, Pharmaceutical, Textile, Packaging, Printing and many other industries.
Want to build a product with global quality standards! we would like to learn about your challenges and how can we solve it through our technologies and solutions. Contact us at firstname.lastname@example.org
References: Mackinsey report – VivaTech Challenges