Canvas Category Software : Operational Technology : Manufacturing Execution System
Autonomous manufacturing everywhere. Enterprise-scale AI platform that makes autonomous manufacturing a reality. Quartic helps manufacturers predict and control the current and future state of their manufacturing operations to improve batch operations, reduce waste, reduce the need for quality checks, and deliver better product faster. We help manufacturers on the path to autonomous manufacturing, where vaccine manufacturers run continuous operations, brewers reduce fermentation times, and chemical factories respond to changes in raw materials.
Manufacturing needs MVDA: An introduction to modern, scalable multivariate data analysis
In most settings, a qualitative/semi-quantitative process understanding exists. Through extensive experimentation and knowledge transfer, subject-matter experts (SMEs) know a generally acceptable range for distinct process parameters which is used to define the safe operating bounds of a process. In special cases, using bivariate analysis, SMEs understand how a small number of variables (no more than five) will interact to influence outputs.
Quantitative process understanding can be achieved through a holistic analysis of all process data gathered throughout the product lifecycle, from process design and development, through qualification and engineering runs, and routine manufacturing. Data comes from time series process sensors, laboratory logbooks, batch production records, raw material COAs, and lab databases containing results of offline analysis. As a process SME, the first reaction to a dataset this complex is that any analysis should be left to those with a deep understanding of machine learning and all the other big data buzzwords. However, this is the ideal opportunity for multivariate data analysis (MVDA).
Build an Anomaly Detection Model using SME expertise
Batch Optimization using Quartic.ai
Bridge the gap between Process Control and Reinforcement Learning with QuarticGym
Modern process control algorithms are the key to the success of industrial automation. The increased efficiency and quality create value that benefits everyone from the producers to the consumers. The question then is, could we further improve it?
From AlphaGo to robot-arm control, deep reinforcement learning (DRL) tackled a variety of tasks that traditional control algorithms cannot solve. However, it requires a large and compactly sampled dataset or a lot of interactions with the environment to succeed. In many cases, we need to verify and test the reinforcement learning in a simulator before putting it into production. However, there are few simulations for industrial-level production processes that are publicly available. In order to pay back the research community and encourage future works on applying DRL to process control problems, we built and published a simulation playground with data for every interested researcher to play around with and benchmark their own controllers. The simulators are all written in the easy-to-use OpenAI Gym format. Each of the simulations also has a corresponding data sampler, a pre-sampled d4rl-style dataset to train offline controllers, and a set of preconfigured online and offline Deep Learning algorithms.