Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. We apply a newly proposed nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA) and demonstrate its flexibility and out-performance relative to its competitors in a variety of optimization problems in the statistical sciences. In particular, we show the algorithm is efficient and can incorporate various cost structures or multiple user-specified nonlinear constraints. Our applications include (i) finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, (ii) estimating parameters in a commonly used Rasch model in education research, (iii) finding M-estimates for a Cox regression in a Markov renewal model and (iv) matrix completion to impute missing values in a two compartment model. In addition we discuss applications to (v) select variables optimally in an ecology problem and (vi) design a car refueling experiment for the auto industry using a logistic model with multiple interacting factors.

JST ERATO "Kawahara Universal Information Network Project" is a unique research program that unites researchers in the fields of human-computer interaction (HCI), robotics, and electrical engineering in Japan.1 The program aims to explore the technologies needed for the next generation of the Internet of Things and HCI. The program's cross-disciplinary approach has brought unique research results through collaboration among different fields. Since the Asian region, including Japan, is a unique global manufacturing hub, this crossroads brought together experts from various fields of manufacturing, including top fashion designers, architects, and manufacturers of building machinery and materials. Developing soft robots required a unique control method and new materials. One such notable success story is found in wireless power transfer technology. We devised a new structure called the Multi-mode Quasistatic Cavity Resonator to create a space that can transmit tens of watts of power anywhere in a three-meter square room.2

In manufacturing settings, data collection and analysis are often a time-consuming, challenging, and costly process. It also hinders the use of advanced machine learning and data-driven methods which require a substantial amount of offline training data to generate good results. It is particularly challenging for small manufacturers who do not share the resources of a large enterprise. Recently, with the introduction of the Internet of Things (IoT), data can be collected in an integrated manner across the factory in real-time, sent to the cloud for advanced analysis, and used to update the machine learning model sequentially. Nevertheless, small manufacturers face two obstacles in reaping the benefits of IoT: they may be unable to afford or generate enough data to operate a private cloud, and they may be hesitant to share their raw data with a public cloud. Federated learning (FL) is an emerging concept of collaborative learning that can help small-scale industries address these issues and learn from each other without sacrificing their privacy. It can bring together diverse and geographically dispersed manufacturers under the same analytics umbrella to create a win-win situation. However, the widespread adoption of FL across multiple manufacturing organizations remains a significant challenge. This study aims to review the challenges and future directions of applying federated learning in the manufacturing industry, with a specific emphasis on the perspectives of Industry 4.0 and 5.0.

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.

AI in Manufacturing to surpass USD 42.5 billion by 2031 from USD 1.7 billion in 2021 at a CAGR of 37.6% in the coming years, i.e., 2021-31. Artificial Intelligence technology is widely being adopted in manufacturing industries to analyze complex sets of data, changes in consumer behavior, and demand for detecting anomalies and improving supply chains and distribution networks. Further, AI can improve decision making by using advanced software to gain more insights and visibility in the operation process, which is driving the market growth of AI in Manufacturing. Based on Offering, the AI in Manufacturing Market is divided into Hardware, Software, and Services, of which the Software segment is expected to lead. Specific programs can be run by software alone without the need for additional hardware.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Databricks, a company specializing in data lakehouse technology, announced on Tuesday a new platform designed for the manufacturing industry. Called lakehouse for manufacturing, the platform aims to unify data and artificial intelligence (AI) for various analytics use cases such as predictive maintenance, quality control and supply chain optimization. The platform builds on Databricks' core data lakehouse platform, which leverages Delta Lake, Apache Spark and MLFlow, open-source projects that enable scalable data processing and machine learning (ML) workflows. The platform also integrates with model serving, a service that Databricks introduced last month to simplify the deployment and management of ML models in production.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Databricks, a company specializing in data lakehouse technology, announced on Tuesday a new platform designed for the manufacturing industry. Called lakehouse for manufacturing, the platform aims to unify data and artificial intelligence (AI) for various analytics use cases such as predictive maintenance, quality control and supply chain optimization. The platform builds on Databricks' core data lakehouse platform, which leverages Delta Lake, Apache Spark and MLFlow, open-source projects that enable scalable data processing and machine learning (ML) workflows. The platform also integrates with model serving, a service that Databricks introduced last month to simplify the deployment and management of ML models in production.

Quality assurance is crucial in the smart manufacturing industry as it identifies the presence of defects in finished products before they are shipped out. Modern machine learning techniques can be leveraged to provide rapid and accurate detection of these imperfections. We, therefore, propose a transfer learning approach, namely TransferD2, to correctly identify defects on a dataset of source objects and extend its application to new unseen target objects. We present a data enhancement technique to generate a large dataset from the small source dataset for building a classifier. We then integrate three different pre-trained models (Xception, ResNet101V2, and InceptionResNetV2) into the classifier network and compare their performance on source and target data. We use the classifier to detect the presence of imperfections on the unseen target data using pseudo-bounding boxes. Our results show that ResNet101V2 performs best on the source data with an accuracy of 95.72%. Xception performs best on the target data with an accuracy of 91.00% and also provides a more accurate prediction of the defects on the target images. Throughout the experiment, the results also indicate that the choice of a pre-trained model is not dependent on the depth of the network. Our proposed approach can be applied in defect detection applications where insufficient data is available for training a model and can be extended to identify imperfections in new unseen data.

The digital transformation in manufacturing represents a significant opportunity to improve operational efficiency, reduce costs, and enhance the customer experience. The manufacturing industry is undergoing a profound transformation driven by rapid advances in technology and the growing demand for more efficient, sustainable, and data-driven operations. This transformation often referred to as Industry 4.0, marks the beginning of a new era of digitalization, data-driven decision-making, and smart automation. The adoption of Industry 4.0 technologies and practices is critical for manufacturers looking to remain competitive in a rapidly changing business environment. In an era where data is becoming the new currency, manufacturers that can leverage data and analytics to drive innovation and improve their operations will be the ones that succeed.

China's massive investment in industrial robotics has put the country in the top ranking of robot density, surpassing the United States for the first time. The number of operational industrial robots relative to the number of workers hit 322 units per 10,000 employees in the manufacturing industry. The world s top 5 most automated countries in manufacturing 2021 are: South Korea, Singapore, Japan, Germany and China. "Robot density is a key indicator of automation adoption in the manufacturing industry around the world," says Marina Bill, President of the International Federation of Robotics. "The new average of global robot density in the manufacturing industry surged to 141 robots per 10,000 employees – more than double the number six years ago. China's rapid growth shows the power of its investment so far, but it still has much opportunity to automate."