The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automates simulation evaluation for swift adaptations. This paper presents a dynamic manufacturing line reconfiguration framework to handle disturbances that result in operation time changes. The framework incorporates a system process digital twin for monitoring disturbances and triggering reconfigurations, a capability-based ontology model capturing available agent and resource options, a configuration optimizer generating optimal line configurations, and a simulation generation program initializing simulation setups and evaluating line configurations at approximately 400x real-time speed. A case study of a battery production line has been conducted to evaluate the proposed framework. In two implemented disturbance scenarios, the framework successfully recovers system throughput with limited resources, preventing the 26% and 63% throughput drops that would have occurred without a reconfiguration plan. The reconfiguration optimizer efficiently finds optimal solutions, taking an average of 0.03 seconds to find a reconfiguration plan for a manufacturing line with 51 operations and 40 available agents across 8 agent types.

Manufacturing industries strive to improve production efficiency and product quality by deploying advanced sensing and control systems. Wearable sensors are emerging as a promising solution for achieving this goal, as they can provide continuous and unobtrusive monitoring of workers' activities in the manufacturing line. This paper presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line. To handle these multimodal sensor data, we propose and compare early, and late sensor data fusion approaches for multi-channel time-series convolutional neural networks and deep convolutional LSTM. We evaluate the proposed hardware and neural network model by collecting and annotating sensor data using the proposed sensing prototype and Apple Watches in the testbed of the manufacturing line. Experimental results demonstrate that our proposed methods achieve superior performance compared to the baseline methods, indicating the potential of the proposed approach for real-world applications in manufacturing industries. Furthermore, the proposed sensing prototype with a body capacitive sensor and feature fusion method improves by 6.35%, yielding a 9.38% higher macro F1 score than the proposed sensing prototype without a body capacitive sensor and Apple Watch data, respectively.

Travis Jefferies is a Staff AI Research Engineer in the Lockheed Artificial Intelligence Center (LAIC). In this role, he is an individual contributor on an AI consulting team that is tasked with solving problems and proliferating AI adoption to Lockheed Martin business areas. Prior to his role in the LAIC, Travis worked in Sustainment where he used analytics and machine learning to help automate manual processes, reduce uncertainty, and achieve higher availability and mission capability for the warfighter. Travis is committed to lifelong learning and holds a Masters degree in Analytics from Georgia Tech and a Bachelors degree in Engineering Management from the University of Arizona. On the weekends he can be found barbequing, dancing, or summiting mountains in southern Arizona where he grew up and currently lives.

In manufacturing, the production is often done on out-of-the-shelf manufacturing lines, whose underlying scheduling heuristics are not known due to the intellectual property. We consider such a setting with a black-box job-shop system and an unknown scheduling heuristic that, for a given permutation of jobs, schedules the jobs for the black-box job-shop with the goal of minimizing the makespan. Here, the jobs need to enter the job-shop in the given order of the permutation, but may take different paths within the job shop, which depends on the black-box heuristic. The performance of the black-box heuristic depends on the order of the jobs, and the natural problem for the manufacturer is to find an optimum ordering of the jobs. Facing a real-world scenario as described above, we engineer the Monte-Carlo tree-search for finding a close-to-optimum ordering of jobs. To cope with a large solutions-space in planning scenarios, a hierarchical Monte-Carlo tree search (H-MCTS) is proposed based on abstraction of jobs. On synthetic and real-life problems, H-MCTS with integrated abstraction significantly outperforms pure heuristic-based techniques as well as other Monte-Carlo search variants. We furthermore show that, by modifying the evaluation metric in H-MCTS, it is possible to achieve other optimization objectives than what the scheduling heuristics are designed for -- e.g., minimizing the total completion time instead of the makespan. Our experimental observations have been also validated in real-life cases, and our H-MCTS approach has been implemented in a production plant's controller.

As artificial intelligence (AI) matures, adoption continues to increase. According to recent research, 35% of organizations are using AI, with 42% exploring its potential. While AI is well-understood and heavily deployed in the cloud, it remains nascent at the edge and has some unique challenges. Even though a user accesses these services often on a mobile device, the compute results reside in cloud usages of AI. More specifically, a person requests information, and that request is processed by a central learning model in the cloud, which then sends results back to the person's local device.

PALO ALTO, Calif., Nov. 8, 2021 -- Landing AI, which provides tools that make building and deploying AI systems in manufacturing faster and easier than ever, today announced Series A funding of $57 million led by McRock Capital, the first investment firm focused exclusively on the Industrial IoT. In addition, New York-based global private equity and venture capital firm Insight Partners, Taiwania Capital, Canada Pension Plan Investment Board (CPP Investments), Intel Capital, Samsung Catalyst Fund, Far Eastern Group's DRIVE Catalyst, Walsin Lihwa, and AI Fund all participated in the round. "Landing AI will unleash the power of the Industrial IoT one company, one factory, and one manufacturing line at a time." Landing AI, led by artificial intelligence visionary, Andrew Ng, developed LandingLens, a fast, easy-to-use enterprise MLOps platform. It applies AI and deep learning to help manufacturers solve visual inspection problems, find product defects more reliably, and generate business value.

A human resources department now culls through resumes using an Artificial Intelligence (AI) tool. No human eyes see the candidates' credentials until the pool of job seekers is culled down to a manageable number. Elsewhere, an online insurance company has a very quick turnaround and low cost of client acquisition when selling life insurance. Prospective clients provide minimal personal information into a web interface and thereafter the company's AI application crunches the provided information with various relevant databases to automate underwriting and make a go, no-go decision within hours, not days or weeks. Somewhere, a financial organization uses chatbots to securely process banking transactions for customers.

In March, Japan's largest auto parts maker, Denso Corp., was facing the urgent task of how to secure enough face masks for its workers given the mass shortage that was occurring amid the spread of COVID-19 infections. While the company, located in Kariya, Aichi Prefecture, had sufficient stocks of masks back then, executives were getting worried that if the company ran short, its production might be affected, since each factory worker needs five masks a day. At an executive meeting March 2, all eyes turned to Yasuhiko Yamazaki, 56, senior executive officer in charge of production, when he said, "How about making them ourselves?" After returning home, Yamazaki cut a mask he had with a pair of scissors, looked at its three-layered structure with nonwoven material used as a middle layer, and felt certain it could be made by Denso. The following day, he gathered seven to eight employees who were well-versed in auto parts production technology and were engaged in the designing and manufacturing of machinery and equipment.

Are machines going to replace programmers? Or how software creates other software. I started doing some home baking recently. It started, like with a lot of other people. I started doing some home baking recently.

This morning Instrumental, a startup that uses vision-powered AI to detect manufacturing anomalies, announced that it has closed a $20 million Series B led by Canaan Partners. The company had previously raised $10.3 million across two rounds, including a $7.5 million Series A in mid-2017. According to a release, the Series B was participated in by other venture groups, including Series A investors Root Ventures, Eclipse Ventures, and First Round Capital, which also led its Seed round. Stanford StartX also took part in the new investment. Instrumental's technology is a hybrid of hardware and software, with a focus on the latter.