Intelligent manufacturing is a new model that uses advanced technologies such as the Internet of Things, big data, and artificial intelligence to improve the efficiency and quality of manufacturing production. As an important support to promote the transformation and upgrading of the manufacturing industry, cloud service optimization has received the attention of researchers. In recent years, remarkable research results have been achieved in this field. For the sustainability of intelligent manufacturing platforms, in this paper we summarize the process of cloud service optimization for intelligent manufacturing. Further, to address the problems of dispersed optimization indicators and nonuniform/unstandardized definitions in the existing research, 11 optimization indicators that take into account three-party participant subjects are defined from the urgent requirements of the sustainable development of intelligent manufacturing platforms. Next, service optimization algorithms are classified into two categories, heuristic and reinforcement learning. After comparing the two categories, the current key techniques of service optimization are targeted. Finally, research hotspots and future research trends of service optimization are summarized.

Although the applications of artificial intelligence especially deep learning had greatly improved various aspects of intelligent manufacturing, they still face challenges for wide employment due to the poor generalization ability, difficulties to establish high-quality training datasets, and unsatisfactory performance of deep learning methods. The emergence of large scale foundational models(LSFMs) had triggered a wave in the field of artificial intelligence, shifting deep learning models from single-task, single-modal, limited data patterns to a paradigm encompassing diverse tasks, multimodal, and pre-training on massive datasets. Although LSFMs had demonstrated powerful generalization capabilities, automatic high-quality training dataset generation and superior performance across various domains, applications of LSFMs on intelligent manufacturing were still in their nascent stage. A systematic overview of this topic was lacking, especially regarding which challenges of deep learning can be addressed by LSFMs and how these challenges can be systematically tackled. To fill this gap, this paper systematically expounded current statue of LSFMs and their advantages in the context of intelligent manufacturing. and compared comprehensively with the challenges faced by current deep learning models in various intelligent manufacturing applications. We also outlined the roadmaps for utilizing LSFMs to address these challenges. Finally, case studies of applications of LSFMs in real-world intelligent manufacturing scenarios were presented to illustrate how LSFMs could help industries, improve their efficiency.

Manufacturing advanced materials and products with a specific property or combination of properties is often warranted. To achieve that it is crucial to find out the optimum recipe or processing conditions that can generate the ideal combination of these properties. Most of the time, a sufficient number of experiments are needed to generate a Pareto front. However, manufacturing experiments are usually costly and even conducting a single experiment can be a time-consuming process. So, it's critical to determine the optimal location for data collection to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This paper presents a novel data-driven Bayesian optimization framework that utilizes sequential learning to efficiently optimize complex systems with multiple conflicting objectives. Additionally, this paper proposes a novel metric for evaluating multi-objective data-driven optimization approaches. This metric considers both the quality of the Pareto front and the amount of data used to generate it. The proposed framework is particularly beneficial in practical applications where acquiring data can be expensive and resource intensive. To demonstrate the effectiveness of the proposed algorithm and metric, the algorithm is evaluated on a manufacturing dataset. The results indicate that the proposed algorithm can achieve the actual Pareto front while processing significantly less data. It implies that the proposed data-driven framework can lead to similar manufacturing decisions with reduced costs and time.

At Google Cloud, our mission is to accelerate manufacturers' digital transformation by bridging data silos, and turning every engineer into a data scientist with easy-to-use AI technologies and an industry data platform. Manufacturers across all domains, from discrete manufacturing to energy and supply chain, are adopting Google's products and purpose-built industry solutions because of their ease-of-use and scalability, using analytics and machine learning to remotely monitor and optimize operations and make better-informed business decisions. Our customers and partners have made great progress on their data transformation journey and on May 5th they will share their perspective and insights at the Manufacturer Spotlight. Join us to hear leading manufacturers and technology partners discuss how they transformed operations and empowered their teams to solve productivity challenges and build competitive and sustainable businesses with Google Cloud. You will hear from thought leaders how a data platform, data analytics and AI models can create a solid foundation for scaling smart factory initiatives and enable unique optimization models.

How to improve the efficiency while preventing deadlocks is the core issue in designing AGV systems. In this paper, we propose an approach to tackle this problem. The proposed approach includes a traditional AGV scheduling algorithm, which aims at solving deadlock problems, and an artificial neural network based component, which predict future tasks of the AGV system, and make decisions on whether to send an AGV to the predicted starting location of the upcoming task, so as to save the time of waiting for an AGV to go to there first when the upcoming task is created. Simulation results show that the proposed method significantly improves the efficiency as against traditional method, up to 20% to 30%. Index T erms --Automated guided vehicles, efficiency improvement, deep learning, LSTM.

The future of the manufacturing industry will be shaped by a number of ever-evolving trends and technologies. While there's no telling exactly how things will play out in the long term, it seems safe to suggest a few will have a profound impact on manufacturers this year. As a result, it's critically important for those within the industry to develop a keen sense of what they are, how they will change over time and, most importantly, how they will impact organizations in 2019 and beyond. With that fact in mind, let's look at five manufacturing trends to watch in the near term: But in the simplest sense, it can be defined as a large-scale integration of cutting-edge artificial intelligence and advanced manufacturing technology and processes. Ultimately, intelligent manufacturing serves to help companies optimize organizational systems, improve product quality, increase the efficient allocation of resources and positively impact customer service.

Since the start of the First Industrial Revolution, manufacturing has been the force pushing industrial and societal transformation forward. Today, we're in the midst of the Fourth Industrial Revolution, as a new generation of sophisticated technologies is transforming manufacturing into a highly connected, intelligent, and ultimately, more productive industry. The manpowered shop floor of the past is being replaced by smart manufacturing facilities where tech-savvy workers, aided by intelligent robots, are creating the products and services of the future. As we approach 2019, we're looking ahead to the trends that will define intelligent manufacturing, as well as help empower clients to better evaluate and manage operations, build innovative products and services, and grow their manufacturing businesses. These trends are detailed in our new 2019 Manufacturing Trends Report.

China has gathered 120 researchers from around the military to work for its top research institute as part of a push to develop military applications for artificial intelligence and quantum technology. More than 95 percent of the new recruits enlisted into the academy hold PhD degrees and are highly specialized in certain fields, particularly artificial intelligence assisted unmanned vehicles and quantum technology. China has previously plans to spend billions making AI and Quantum computing research centers. They will spend tens of billions to dominate AI and Quantum technology. The above intelligent products have good technology and industrial base.