Confirmed sessions 2022
Stay tuned for the full agenda

Sub-surface Defects Detection During Manufacturing Through Sound-based Machine Learning Approach at Hindustan Shipping Limited

Sub-surface Defects Detection During Manufacturing Through Sound-based Machine Learning Approach at Hindustan Shipping Limited

Track:

Summary:

The sound-based machine learning solution developed for Hindustan Shipping Limited helps identify defects that are sub surface or interior to the part. The identification of defect is real time, during the part production. Thus, enabling one to take actions immediately and not wait to produce a scrap part. Takeaways of this presenation are:(1) insights into the sound based machine learning approach for sub surface and interior defect detection; (2) how to identify the location and magnitude of defect in real time.

Machine Learning at Production in the Real World: Chances & Limits, Challenges & Solutions

Speakers:

Walter Huber

Machine Learning at Production in the Real World: Chances & Limits, Challenges & Solutions

Summary:

Artificial intelligence and machine learning are well known in industry. When we look on production side, however real used solutions are very limited. In this table discussion, we like to discuss reasons for the actual situation. A blocking point for a rollout is that for business people machine learning and AI is hard to understand. On the other side, heuristics often generate similar results like complex machine learning algorithms and are much easier to understand for non data scientists. So, is machine learning just a hip? Also we will discuss another critical questions, because in general there two ways of implementing solutions: make or buy. So what is the best way in what situation?

Is AI Too “Spooky” to be Trusted? – Explainable AI with semantic networks at ZF, Knauf & Co.

Speakers:

Britta Hilt

Is AI Too “Spooky” to be Trusted? – Explainable AI with semantic networks at ZF, Knauf & Co.

Track:

Summary:

Can you trust “artificial brains” and thus, “artificial” decisions or recommendations? Well-known deep learning with neuronal networks is normally a black box – thus, people cannot understand why AI decided this way. Therefore, new AI methods are on the rise: deep learning with semantic networks. This explainable AI makes subject matter expert understand and thus, trust AI. In this presentation, case studies are shown from discrete manufacturing (i.e. ZF) and process industry (i.e. Knauf).

Developing the 2nd Generation of AIML Models for Demand Planning at Beiersdorf AG

Developing the 2nd Generation of AIML Models for Demand Planning at Beiersdorf AG

Track:

Summary:

International FMCG manufacturer Beiersdorf needs to forecast 1000s of products every month . In 2021, 10 years after the 1st Neural Networks were introduced, Beiersdorf set out to improve automatic forecasting further by reviewing the latest developments in technology. Surprisingly, some of the most recent and hyped algorithms such as DeepLearning, XGBoost, Prophet, BSTS and others did not perform well, but simple AI-methods customised to their data properties improved accuracy significantly.

Machine Learning Techniques to Preempt IPTV Service Downtime with Time Series Anomaly Detection on DSLAM Systems at Telefonica

Machine Learning Techniques to Preempt IPTV Service Downtime with Time Series Anomaly Detection on DSLAM Systems at Telefonica

Track:

Summary:

Telefónica, the biggest Spanish telecommunications company, asked us to provide a machine learning solution capable of detecting when one of their DSLAMs has an anomaly in registered customers indicating a loss in customer IPTV service. You will be shown how to deal with thousands of time series data by combining clustering algorithms, smoothing methods and deep learning tools to obtain efficient and high-performance results.

Machine Learning with Humans for Cyber Security: Integrating Experts into the Learning Process

Machine Learning with Humans for Cyber Security: Integrating Experts into the Learning Process

Track:

Summary:

Human In The Loop (HITL) is a process in which, as part of the Machine Learning (ML) workflow, experts are asked their opinion about the model’s predictions in order to improve it. We’ll discuss how we created a mechanism to automatically predict the best security policies for network DDoS to protect our customers, and explain how we integrated security experts into our ML process, in order to both optimize the labeling of security policies, and move to production quickly with minimum risk.

Taking Data-Driven Process Optimization to the Next Level at Bitburger

Taking Data-Driven Process Optimization to the Next Level at Bitburger

Track:

Summary:

A malt yield forecast with an excellent prediction performance, as well as first transfers to Augustiner Bräu, were successfully implemented to optimize the beer brewing process. The crucial next step is getting our ready-to-use analysis modules with built-in requirements into the running production. For this, we are creating an architecture for robust deployment, considering model resilience and automated detection of data drift and performance decay to eventually trigger new model training.

Implementing a Predictive Maintenance System for Trumpf Laser

Speakers:

Oliver Bracht

Implementing a Predictive Maintenance System for Trumpf Laser

Track:

Summary:

By predicting problems the laser machine availability can be increased significantly. This will not only reduce the costs of the maintenance. Started as a pure condition monitoring portal, the project for Trumpf Laser evolved into a hollistic predictive maintenance system, which allows facilitating the work of other departments (e.g. customer support).It also was the starting point for a new service: proactive support. Those practical examples shows the importance of empowering data-driven intelligence for machine manufacturers.

Use of PLC Data for Early Detection of a Serious Production Failure at Kampf

Speakers:

Niklas Haas

Use of PLC Data for Early Detection of a Serious Production Failure at Kampf

Track:

Summary:

Machines from Kampf Schneid- und Wickeltechnik GmbH & Co. KG are used worldwide for winding and cutting a wide variety of materials. A tear-off of the flow materials during the production process means an expensive loss of production. We present an approach on how an “AI” can detect an imminent tear-off at an early stage. We present (1) the winding machine, process and infrastructure for data collection (2) the ML pipeline for unsupervised pattern recognition and (3) the business case briefly.

Data-driven, Networked Quality Management in the Business Unit Laundry at MIELE

Data-driven, Networked Quality Management in the Business Unit Laundry at MIELE

Track:

Summary:

Within the research project AKKORD, MIELE, IPS and RapidMiner are working on developing a modular expandable and holistic reporting and analysis system that creates transparency about the present and future quality situation. In field data management, quality analyses are enabled by setting up standardized analysis modules and user-specific dashboards. MIELE designs the system to measure, monitor and forecast various KPIs to holistically improve the business unit’s quality management. The implementation at MIELE in particular shows the direct application in a business area where the use of predictive analytics is effective.

How Data Science Assists Volkswagen in Benchmarking and Identifying Similar Work Plan Descriptions

Speakers:

Edin Klapic

How Data Science Assists Volkswagen in Benchmarking and Identifying Similar Work Plan Descriptions

Track:

Summary:

Assembling a car is a complex task consisting of many steps usually grouped and organized in work plans. Based on the car model and its specifications, creating a key performance indicator (KPI) optimized work plan can be very time consuming. This case study at Volkswagen shows how data science can assist and speed up this process. After using various text analytics methods to identify similar work plans descriptions, a semi-automated benchmarking approach provides a KPI-driven recommendation.

The Rise of AI for Space – Learning from Earth Observation Data to Understand Our Planet

The Rise of AI for Space – Learning from Earth Observation Data to Understand Our Planet

Summary:

New streams of earth observation (EO) data (e.g. from Copernicus and New Space missions) lead to a far more comprehensive picture of our planet. These new global data on our planet offer new possibilities for scientists to advance our understanding of the Earth System. It also represents new opportunities for entrepreneurs to turn big data into new types of information services. In this table discussion, we discuss the chances and challenges as well as use cases and resources (tools, methods, data sets) for AI4EO.

Building Trust as a Service: A Shared Responsibility Approach for Data Platforms at CentralNic Group

Speakers:

Mirco Pyrtek

Building Trust as a Service: A Shared Responsibility Approach for Data Platforms at CentralNic Group

Summary:

Building a reliable single source of truth in a company typically comes with the following dilemma: focusing the responsibility around a dedicated data engineering team (data lake) versus distributing the ownership among the product engineering teams (data mesh). This talk will focus on lessons learned from implementing a shared responsibility model for the trusted flow of information within a data platform.

Markov-based Predictive Quality Analytics for Mass Lens Production at ZEISS

Speakers:

Kai Kümmel

Markov-based Predictive Quality Analytics for Mass Lens Production at ZEISS

Summary:

Quality improvement for mass production lines is an ongoing topic for many years. The target is to reduce the rate of defects and scrap during production having an impact on sustainability, delivery time and cost. For the example of mass lens production at ZEISS we introduce a Markov-based method, that allows us to trace the movement of a given product through the production line to help us understand potential root causes for quality losses and thus being able to predict defects. In the end we aim to achieve a closed loop quality control avoiding quality losses by an improved understanding of root causes and proactive actions enabled by Industry 4.0 technologies such as Machine Connectivity and Artificial Intelligence.

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