Industrial Internet of Things

The state of manufacturing is changing rapidly. Regardless of sector or location, manufacturing decision-makers across the world are signaling a desire for better supply chain resiliency, manufacturing flexibility, increased speed of innovation and stronger environmental sustainability. Singapore’s manufacturing sector, a significant contributor to its gross domestic product, is always evolving and today is shifting away from its traditional focus on producing highly customized products using flexible manufacturing processes, but at significantly lower efficiencies. Today, with Industry 4.0, we can design manufacturing systems that optimize both efficiency and flexibility. And this is possible because of the convergence of technologies such as artificial intelligence (AI), data analytics, robotics and the Industrial Internet of Things (IIoT). This blend of technologies helps reduce the cost of technological solution ownership – a derivative of Right’s Law – as a function of cumulative production. In HP Singapore, driving innovation in our product and processes is part of our DNA, and over time our products have grown in complexity and breadth. We have embraced Fourth Industrial Revolution (4IR) technologies in our advanced manufacturing lines. We started our Industry 4.0 journey in 2016 with Vision and Mission 2020 to modernize our production facilities to smart factories that strengthen our competitive edge. Our focus was on upskilling our employees with future skill sets, build new technological capabilities and partner with higher education institutes. To drive these transformations, we have formulated five pillars: Additive Manufacturing Data Analytics Cyber-Physical Integration Digitalization Workforce Transformation These five pillars have enabled us to move from labor-intensive and reactive processes to processes that are highly digitized, automated, and AI-driven, enabling us not only to increase quality and productivity but also to reskill our people in anticipation of jobs they will need in the future. Technicians have been upskilled and promoted to techno-operators which has, in turn, freed up technical specialists to explore other roles. Engineers have retrained as data scientists, or have moved to new product development, for instance. In 2017, HP’s Ink Supplies Operations (ISO) set up Smart Manufacturing Applications and Research Centre (SMARC) to adopt 4IR technologies and implement these innovations in production lines. Today, SMARC is the home ground for HP engineers to experience, trial and prototype solutions, bringing innovative and sometimes unexpected solutions to manufacturing. It is also a showcase for industry partners, government agencies and schools. Here is how each pillar of the SMARC contributed to transformation to augment the manufacturing workforce: Cyber-Physical Integration – Move Role of robotics/automation – By standardizing automation standards for robotics, we have deployed collaborative robots (Cobots) and autonomous intelligent vehicles (AIVs) to perform manual and routine tasks to drive productivity, while reducing errors from operator fatigue and protecting our operators’ physical well-being. Digitalization – Sense Role of IIoT – Devices are a treasure trove of data that can provide clarity on how the entire manufacturing line is performing in real time. Building a platform that connects devices and collects data while allowing factory floor managers to dynamically visualize on an Integrated Command Centre (ICC) and manage factory performance is central to HP’s digital transformation journey. And IIoT is not restricted to just devices that are already wired for data sharing. HP has also connected off-the-shelf analogue devices using a standardized data transportation protocol, allowing HP to collect essential data across all types of devices and eliminating manual data entry. Additive Manufacturing – Build By embracing additive manufacturing (use of HP MultiJet Fusion 3D printers), HP introduced more flexibility in operations through on-site rapid prototyping, light production, and replacement of parts needed on our manufacturing floors, shortening production timelines. We 3D printed pallets, which are cheaper and faster to produce, and replaced original pallets for transportation on conveyor belts, improving the efficiency and productivity of our operators. Director Jamie Neo with HP’s MultiJet Additive Manufacturing Printer. (Photo Credit: HP) The HP Multi Jet Fusion 3D printing technology has helped HP to replace traditional manufacturing methods and streamline processes in our supply chain. For example, HP is 3D printing the Drill Extraction Shoe, a tool that is essential to the removal of waste products from laser-drilling in HP’s printhead manufacturing line. Through 3D printing, HP has consolidated the production of the tool from nine parts to one 3D printed model, thereby optimizing the design of the tool and reducing its production time from three to five days to 24 hours. Data Analytics – Think By deploying advanced analytics and machine learning models, HP has enabled real-time detection, diagnostics, and prediction of product quality across our manufacturing lines. Predictive models are replacing traditional “destructive testing,” reducing waste and allowing HP to meet unique product specifications more accurately. Machine learning is diagnosing and recommending the right set up for tools and manufacturing lines, when necessary, to reduce downtime and increase precision. Workforce Transformation – Grow The pivot to becoming an advanced manufacturing leader not only requires HP to invest in 4IR technologies but also skill sets to operate 4IR technologies. We embarked on a Workforce Transformation program to help our employees stay competitive in a fast-changing world. Today 35% of HP technical workforce have had the opportunity to take on new roles even as needs evolve, thanks to internal and external training and reskilling. Beyond technology and training, the glue that binds these together and makes it successful is our culture at HP. We are ambition-led, which means that we do not see the world as it is, but what we can be. And we do so by collaboration. Plans for the Future After accomplishing our Mission 2020, in late 2020 we launched Mission 2025 to extend our end-to-end smart factory capabilities through advanced connectivity, intelligence and automation to optimize and drive sustainable manufacturing flexibility and efficiency. Pyramid of HP’s smart manufacturing focus Advanced technologies such as additive manufacturing, IIoT, automation and robotics, data analytics, machine learning and AI are central to the connectivity and the end-to-end intelligence of our smart factories, enhancing production efficiency and flexibility while improving the quality of our products. For example, the deployment of IIoT sensors in our wafer plant has helped to reduce downtime in replacing CO2 gas cylinders. What’s more, AI enables us to more accurately monitor the dispensing of structural adhesive to eliminate lost yield. We believe that by enhancing manufacturing efficiency and flexibility, we were able to shorten resolution time, reduce our carbon footprint, and improve the resiliency of our manufacturing and supply chain systems. HP smart factory model In April 2021, two lines in HP Singapore joined the World Economic Forum’s Global Lighthouse Network after being recognized for pivoting from a labor-intensive factory into a digitized, automated one with the help of AI. In doing so, we managed to improve manufacturing costs by 20% and productivity by 70%. Under Mission 2020, we saw the following successes: Improved manufacturing costs by 20% Improved productivity by 70% Brought most HP employees onboard to our smart manufacturing journey Equipped HP employees with skill sets in areas such as additive manufacturing, data analytics, AI, robotics and Internet of Things Established a Model Factory playbook With Mission 2025, we will: Continue to train employees in future skillsets by partnering with institutes of higher learning Scale our Model Factory playbook across more manufacturing lines to reduce costs and improve productivity Enhance our knowledge in additive manufacturing by building an ecosystem as a service platform to help manufacturing companies Enable a sustainable manufacturing system to reduce our carbon footprint and help enable a circular economy We believe in innovating with purpose by focusing on solving real-world problems and creating technology in the service of humanity. That is why we built the SMARC to create the solutions for our lines and showcase these solutions to encourage industry participation. We are driven by values and ambition, which means that it is not just what we do, but also how we execute it. We make sure our values inform everything we do – for instance, helping us make a greater impact to environmental sustainability, people, and our community. We believe this is a crucial step in coalescing industry support, which is necessary to move the needle on advanced manufacturing. Robert Ronald is Master Program Manager, Cost Structure, Model Smart Factory and Sustainability, at HP.

Introduction Automated production in electronics manufacturing can produce high-quality products, but it might lead to a particular failure without human interventions. With the rapid technology development, such as the Industrial Internet of Things (IIoT), big data analysis, cloud computing, artificial intelligence (AI), many manufacturing processes can be more intelligent, and Industry 4.0 can then be realized in the near future[1]. Smart manufacturing adopts real-time decision-making based on operational and inspectional data and integrates the entire manufacturing process as a unified framework. Then, the future manufacturing process transforms cyber-physical systems digitally and responds to any uncertain situations proactively while ensuring higher efficiency. In Surface Mount Assembly (SMA) lines, equipment status and quality data can be collected via IIoT technology. Data-driven solutions, such as AI and machine learning algorithms, can be applied to diagnose abnormal defects and adjust optimal machine parameters in response to unexpected changes/situations during production. Collaborating with various SMT industry partners, the research team at the State University of New York at Binghamton (aka Binghamton University) developed a novel framework based on AI-based closed-loop feedback control and parameter optimization to implement a smart manufacturing solution in the PCB assembly for yield and throughput improvement. This AI-based framework could provide a potential road map for data-driven process control in SMA. Machine Intelligence in SMA Each SMA process has a critical effect on the final PCB product quality and throughput. Notably, the solder printing process is a critical operation because over 60% of the PCB assembly soldering defects can be traced back to this stage. An inadequate volume of solder paste transferred to any PCB pad is a printing fault, which leads to board failure and substantial reworking and repair costs. The pick and place (P P) process is the highest cost procedure, including expensive machine investment and extended production time. In the soldering reflow process (SRP), the reflow oven temperature and other related settings determine the solder joints' quality and reliability. Hence, multiple inspection machines in the SMA processes have been introduced, including solder paste inspection (SPI) and automated optical inspection (AOI) machines. Particularly, two independent AOIs could be employed to detect the components' defects before and after SRP. Because many electronics components become small-scale (e.g., ), more assembly-related failures are often observed in recent SMA processes. The Smart Electronics Manufacturing Laboratory (SEML) at Binghamton University is fully equipped with two solder paste printers, two chip-mounters, and a reflow oven along with SPI and AOI machines. The research team tested more than 8,000 PCB at SEML. The results show that numerical methods based only on physical properties might have practical limitations in explaining small-scale components' behavioral patterns. It might be caused by unknown environmental factors (e.g., temperature and humidity), machine calibration, measurement accuracies, vibrations, etc., which could have influenced the quality of the SMA outcomes. However, recent research shows that AI-based methods can increase product quality up to 35%, reduce scrap rates, and optimize fab operations in semiconductor manufacturing, compared to traditional approaches[2]. It implies that a data-driven intelligent SMA process control has the potential to advance SMA processes. The goal of the smart SMA is to maintain optimized settings in both offline and online scenarios. The AI and data analytics solution can optimize all SMA process parameters before production (i.e., offline control) and during production (i.e., online control). The overall schematic of the AI-based closed-loop feedback control framework is illustrated in Figure 2. Intelligent SMA Modules In the solder printing process, four machine intelligence modules are considered: Printing advising module (PAM) Printing optimization module (POM) Printing diagnosis module (PDM) Dynamic stencil cleaning process control (CPC) Figure 2. A schematic diagram of the AI-based closed-loop feedback platform Figure 3. PAM effectiveness over customer’s best-known printing parameter setting PAM aims to recommend the ideal initial setting of the printer critical parameters, such as printing speed, printing pressure, and separation speed, using hybrid machine learning and heuristics optimization techniques[3]. As a case study, the research team validated the PAM's performance with an automotive PCB testbed and compared the printing results to the best-known printing parameters. The experimental results show that PAM can achieve over 50% higher Cpk (i.e., process capability index, as shown in Figure 3). POM optimizes printing parameters in real-time by monitoring printing quality and fine-tuning offset and process parameters for adapting to dynamic conditions[4]. The experimental results show that POM achieves more than 30% production quality improvement in terms of the Cpk by adjusting printing parameters compared to the offline control. PDM provides anomaly detection and diagnosis of the potential printing failure cases to improve process quality and reduce downtime[5]. The experimental results show that the PDM can achieve more than 87% accuracy in predicting different types of defects, improper printer hardware issues in the board support, squeegee, paste conditions, etc. The CPC uses the SPI information to estimate residue buildup level on the stencil undersurface and assess the stencil cleaning profile and cycle control, as illustrated in Figure 4[6]. Upon the implementation of CPC, it is expected that the robustness of the printing quality and the Cpk can be improved by 34% and 10%, respectively, compared to the best-known cleaning parameters using in the production line. Figure 4. Smart residue buildup prediction for stencil cleaning operation control During the P P procedure, the mounter optimization module (MOM) and the mounter diagnosis module (MDM) can be applied as a machine's automation process while optimizing the P P machine's parameters. By utilizing self-alignment effects appropriately, MOM identifies the optimal placement position by predicting the component's post-reflow positions based on the data collected by SPI, Pre-AOI, and Post-AOI machines, as shown in Figure 2. MOM also offers the placement positions adaptively during active production. In the MOM framework, multiple dynamic placement options are first generated based on the solder paste offset information. The components' final offsets in both x and y directions are predicted by a hybrid AI model that stacks on the k-nearest neighbor regression and the gradient boosting regression models. The optimal placement, which has the minimum predicted post-reflow misalignment, can be identified by MOM. The experimental results show that MOM can decrease 18% of the final misalignments compared to a conventional P P placement method (i.e., placing a component on the pad center). MDM is a prescriptive and predictive maintenance method that uses P P machine operational and AOI inspection data to trace back the root causes of P P defects and prevent future failure. MDM can achieve an accuracy of 84.50% in identifying the known root causes of certain defects, such as improper nozzle size, parts' contamination, and feeder problem. It shows that different mounting defects can be detected and classified automatically when the abnormality is detected through AI-based diagnosis algorithms. Figure 5. The illustration of the optimal placement position in the MOM One of the reflow oven issues to be addressed is to find the optimal reflow oven temperature settings, which would affect the final quality of the PCB products. Solder paste manufacturers usually provide a target profile based on the solder paste composition's physical properties, and solder joint temperature is required to meet the given profile. Hence, reflow engineers should fine-tune reflow oven temperature manually to ensure a thermal profile outcome from the reflow oven to correctly meet a target profile, requiring substantial cost and effort. The research team proposes an automated reflow recipe optimization model based on the PCB thermal profile and its recipe. Figure 6. Optimized thermal recipe and thermal profile First, the initial recipe collects the data for the prediction model and identifies the relationship between the thermal profile and the corresponding recipe. Then, an AI-based model is developed to predict the thermal profile based on the input recipe. Compared to traditional methods, the AI-based method generates an optimal reflow oven recipe to minimize the gap between the predicted temperature and the given profile. As a result, the AI-based prediction model allows us to achieve promising results, such as 97% of fitness in the given profile temperature curve within one hour of processing time. The proposed model has other significant advantages, such as saving time, labor, and materials. It enhances the degree of automation of the PCB reflow process. In the future, data from multiple inspection machines will be integrated so that the reflow optimization process is fully automated and generates more reliable results. Summary and Conclusion The small-scale electronics products make the SMA processes much more complicated to maintain high-quality PCB products, and theoretical interpretations of the SMA processes can be challenging due to many uncertain factors. With the help of AI and big data collected from various inspectional operations, SMA processes can be intelligent and flexible in response to dynamic environmental situations. While retaining the optimal control parameters throughout the SMA processes, the final PCB product quality can be enhanced while maintaining the designed throughput. Automated and smart systems bring about the opportunity to next level of electronics manufacturing, which utilizes the data and information from the end-users through edge/cloud computing and fastens the customized product manufacturing with increasing efficiency for high-mix/low-volume manufacturing. Also, it can increase verities of design and fasten the delivery time. About the Author Prof. Sang Won Yoon is a recipient of the SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2019 and a highly successful researcher who leads many productive long-term industry collaborations. Prof. Yoon received his doctoral degree in School of Industrial Engineering at Purdue University, and he joined the faculty of the Watson School in the Department of Systems Science and Industrial Engineering at State University of New York at Binghamton in 2010. Prof. Yoon has been studying how to extract useful insights from expanding data sets to support intelligent decision-making processes. His research not only resides in better understanding large-scale data set by using statistical learning methodologies, but also leverages optimization, soft computing, simulation, and complex theories with conventional machine learning algorithms. As a result, Prof. Yoon has published in over 130 internationally renowned journals and conference proceedings. He was also a member of the Data Science Transdisciplinary Area of Excellence (TAE) initiative and is an active member of the Health Sciences TAE at his institution. The author recognizes the following for their assistance with this article: Daehan Won, [email protected], Assistant professor Jingxi He, [email protected], Ph.D. candidate Shrouq M. Alelaumi, [email protected], Ph.D. candidate Yuanyuan Li, [email protected], Ph.D. candidate Yuqiao Cen, [email protected], Ph.D. candidate References ​​​​​​​[1] Qi, Q., and Tao, F., 2018. Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, pp.3585-3593. [2] 10 Ways machine learning is revolutionizing manufacturing in 2019. https://www.forbes.com/sites/louiscolumbus/2019/08/11/10-ways-machine-learning-is-revolutionizing-manufacturing-in-2019/?sh=7cd2e9e22b40. [3] Khader, N. and Yoon, S.W., 2018. Stencil printing process optimization to control solder paste volume transfer efficiency. IEEE Transactions on Components, Packaging and Manufacturing Technology, 8(9), pp.1686-1694. [4] Lu, H., Wang, H., Yoon, S.W. and Won, D., 2019. Real-Time stencil printing optimization using a hybrid multi-layer online sequential extreme learning and evolutionary search approach. IEEE Transactions on Components, Packaging and Manufacturing Technology, 9(12), pp.2490-2498. [5] Alelaumi, S., Wang, H., Lu, H. and Yoon, S.W., 2020. A Predictive Abnormality Detection Model Using Ensemble Learning in Stencil Printing Process. IEEE Transactions on Components, Packaging and Manufacturing Technology, 10(9), pp.1560-1568. [6] Alelaumi, S., Khader, N., He, J., Lam, S. and Yoon, S.W., 2021. Residue buildup predictive modeling for stencil cleaning profile decision-making using recurrent neural network. Robotics and Computer-Integrated Manufacturing, 68, p.102041.