The rapid growth of the Industry 4.0 paradigm is increasing the pressure to
develop effective automated monitoring systems. Artificial Intelligence (AI) is
a convenient tool to improve the efficiency of industrial processes while
reducing errors and waste. In fact, it allows the use of real-time data to
increase the effectiveness of monitoring systems, minimize errors, make the
production process more sustainable, and save costs. In this paper, a novel
automatic monitoring system is proposed in the context of production process of
plastic consumables used in analysis laboratories, with the aim to increase the
effectiveness of the control process currently performed by a human operator.
In particular, we considered the problem of classifying the presence or absence
of a transparent anticoagulant substance inside test tubes. Specifically, a
hand-designed deep network model is used and compared with some
state-of-the-art models for its ability to categorize different images of vials
that can be either filled with the anticoagulant or empty. Collected results
indicate that the proposed approach is competitive with state-of-the-art models
in terms of accuracy. Furthermore, we increased the complexity of the task by
training the models on the ability to discriminate not only the presence or
absence of the anticoagulant inside the vial, but also the size of the test
tube. The analysis performed in the latter scenario confirms the
competitiveness of our approach. Moreover, our model is remarkably superior in
terms of its generalization ability and requires significantly fewer resources.
These results suggest the possibility of successfully implementing such a model
in the production process of a plastic consumables company.

提出了一种新型的自动监控系统，在分析实验室中生产过程中的塑料耗材，通过使用手动设计的深度网络模型来增强对试管内透明抗凝剂物质存在与否的控制过程的有效性，并通过实验结果证明该方法与最先进的模型相比具有竞争力，表现出优于其他模型的分类能力和泛化能力，为塑料耗材公司的生产过程成功实施类似模型提供了可能性。

一种基于计算机视觉的用于分析实验室耗材自动控制的质量评估技术

A Computer Vision-Based Quality Assessment Technique for the automatic  control of consumables for analytical laboratories

In manufacturing, rework refers to an optional step of a production process
which aims to eliminate errors or remedy products that do not meet the desired
quality standards. Reworking a production lot involves repeating a previous
production stage with adjustments to ensure that the final product meets the
required specifications. While offering the chance to improve the yield and
thus increase the revenue of a production lot, a rework step also incurs
additional costs. Additionally, the rework of parts that already meet the
target specifications may damage them and decrease the yield. In this paper, we
apply double/debiased machine learning (DML) to estimate the conditional
treatment effect of a rework step during the color conversion process in
opto-electronic semiconductor manufacturing on the final product yield. We
utilize the implementation DoubleML to develop policies for the rework of
components and estimate their value empirically. From our causal machine
learning analysis we derive implications for the coating of monochromatic LEDs
with conversion layers.

本文利用双重 / 无偏机器学习方法估计了光电半导体制造中颜色转换过程中重新加工步骤对最终产品收益的条件处理效应，并提出了重新加工组件的策略。

因果学习最佳返工策略

Causally Learning an Optimal Rework Policy

Automated storage and retrieval systems are principal components of modern
production and warehouse facilities. In particular, automated guided vehicles
nowadays substitute human-operated pallet trucks in transporting production
materials between storage locations and assembly stations. While low-level
control systems take care of navigating such driverless vehicles along
programmed routes and avoid collisions even under unforeseen circumstances, in
the common case of multiple vehicles sharing the same operation area, the
problem remains how to set up routes such that a collection of transport tasks
is accomplished most effectively. We address this prevalent problem in the
context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale
producer of commercial vehicles, where routes for automated guided vehicles
used in the production process have traditionally been hand-coded by human
engineers. Such ad-hoc methods may suffice as long as a running production
process remains in place, while any change in the factory layout or production
targets necessitates tedious manual reconfiguration, not to mention the missing
portability between different production plants. Unlike this, we propose a
declarative approach based on Answer Set Programming to optimize the routes
taken by automated guided vehicles for accomplishing transport tasks. The
advantages include a transparent and executable problem formalization, provable
optimality of routes relative to objective criteria, as well as elaboration
tolerance towards particular factory layouts and production targets. Moreover,
we demonstrate that our approach is efficient enough to deal with the transport
tasks evolving in realistic production processes at the car factory of
Mercedes-Benz Ludwigsfelde GmbH.

使用 ASP 基于运输任务的优化输送路线对梅赛德斯奔驰 Ludwigsfelde 的汽车制造过程进行优化。