Manufacturing Problems Research Articles

Analysis of challenges of lean six sigma and cyber physical system integration using CODAS

PurposeOrganisations are looking for a concept that can solve traditional as well upgraded production problems with current resources and technology, and this can be addressed by integration of lean six sigma (LSS) with Industry 4.0 (I4.0) technologies. This reduces complexity in the manufacturing process through digital technologies. Cyber physical system (CPS) is considered as primary I4.0 technology with which all other technologies are associated to extend. CPS can integrate with other prevailing manufacturing approaches like lean, LSS and so on. LSS, on the other hand, is a team-focussed performance improvement strategy which is widely used by the industries to identify problems, eliminate waste to meet customer requirements. The study aims at analysis of challenges for LSS and CPS integration.Design/methodology/approachIntegrating LSS and CPS will solve both traditional and modern manufacturing problems. To integrate these technologies, organisational requirements need to be assessed. These requirements are posed as challenges in this study. Their priority weights are analysed, and challenges are prioritised using fuzzy Combinative Distance-based Assessment (CODAS) method. Sensitivity analysis is employed to assess the robustness of the results.FindingsThe result of this study enables top management to integrate LSS and CPS. In this study, 20 challenges were identified, and they are assessed to compute their relative assessment score. Requirement of new tools and methods with 0.6 score ranks first followed by interplay with big data and requirement of new communication protocol. The result highlighted the need for integration of LSS and CPS, proper utilisation of information and communication technologies, and cyber security management as the main impediments that need to be addressed to implement CPS in an LSS environment.Originality/valueThe analysis of challenges of LSS and CPS integration using MCDM tool is the original contribution of the authors.

A submodeling approach for efficient prediction of local temperature profiles in component-scale additive manufacturing

To solve the multiscale problem of additive manufacturing of large components by material extrusion (MEX), this work utilizes the methodology of submodeling and shows the necessary modeling steps to apply the methodology to the material extrusion process. The research enables the coupling of high-resolution models and process simulations at the component level. It allows the investigation of local effects such as crystallization effects or the resulting interface strength and its dependence on process variables in critical zones of a component, considering the component geometry as well as global and component-specific process conditions. The principle is verified numerically and validated experimentally, showing good agreement. In addition, different specifications for boundary conditions and submodel sizes are compared and evaluated. Variable time increments are used to apply the submodels with a computational time independent of the component size. In addition, sensitivity analyses provide information on the necessary temporal and spatial discretization of the submodel for the most accurate prediction of the temperature profile.

Justification for selecting raw materials to minimize the content of sulfur dioxide in confectionery products

Raw materials and semi-fi nished products used in the manufacture of confectionery products may contain food additives, including the preservative sulfur dioxide, ensuring their safety and highquality characteristics throughout the appropriate shelf life. According to current legislation in the fi eld of food safety, including confectionery products, the content of sulfur dioxide should not exceed 10 mg/kg of product. Meanwhile, there is a risk of high sulfur dioxide content in confectionery products, which can negatively aff ect consumers’ health. Therefore, justifi cation for the choice of raw materials and semi-fi nished products with a minimum content of the sulfur dioxide preservative is an urgent problem for manufacturers in the confectionery industry. Research has been carried out on the main raw materials and fruit semi-fi nished products in order to identify the sources of sulfur dioxide in confectionery products. Based on the results obtained, it has been concluded that the content of sulfur dioxide in sugar, fl our, and chocolate confectionery products is in wide ranges and can exceed the concentration of 10 mg/kg of product, which is due, among other things, to the high reactivity of this preservative with main raw materials and semi-fi nished products: starch syrup, oat fl our, fruit fi llings based on sulfated fruit puree, dried fruits, and coconut fl akes.

Research on network interference problems in intelligent manufacturing

Abstract As the smart manufacturing industry continues to grow, ensuring high-quality data transmission has become especially critical. In wireless communication networks, numerous sources of interference exist, especially significant in restricted space environments such as factories. These areas are subject to signal interference from same-frequency devices and interference from multipath propagation due to signal reflection in buildings. In order to guarantee the communication quality of smart manufacturing systems, reducing network interference in such spaces is a prerequisite; therefore, it becomes crucial to reduce various network interferences. In this study, a scheme combining orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) techniques is proposed to cope with the interference problem of the network in the smart manufacturing process.

(Invited) Physics Informed Data Driven Manufacturing for Li-ion Batteries

The road to carbon neutrality requires a massive scale up of battery manufacturing on a rapid timescale. Li ion batteries are complex systems with many materials and interfaces, and the challenge of controlling microcale material properties of millions of units per year demands a physics and a data driven approach to manufacturing. Conversely, the Toyota Production System (TPS) for manufacturing is a deeply human-led process, thus, we have a need to integrate a variety of models with human expertise and experience. We will present our manufacturing optimization experience and approach with safety and cost as target metrics. Economics incorporates both cost of production (including yield and throughput) as well as future performance (reliability). We will present several case studies of the complexities of implementing ML for complex manufacturing problems, with emphasis on the role of inspection and safety. Finally, we will place advanced manufacturing in the context of the entire battery life cycle from materials to use, and draw on these case studies to present a vision of both data- and human-driven process improvements for battery manufacturing. Key area will focus on defect detection, root cause analysis using production data, and rapid adoption of new chemistries and cell infrastructure.

Automatic optimization combined with genetic algorithm and CFD for temperature uniformity of OLED glass substrate

The heating technology and temperature uniformity of glass substrate (GS) are crucial for the final display quality and efficiency of OLED. In a high-temperature and large heating environment with mutual influences among multiple variables, maintaining temperature difference of the entire GS below 3 °C presents a complex combinatorial optimization problem in industrial manufacturing. To address this challenge, an automatic combined method is established. This method involves parameterized modeling in UG, thermal calculation in CFD, and genetic algorithm optimization in Matlab to determine optimum designs for electric heating tube (EHT) heating temperature and interval with respect to objective functions of temperature uniformity and highest temperature. Through this automatic combined optimized scheme, the optimized heating temperature and EHT interval have been achieved to ensure maintaining a temperature difference of entire GS within 3 °C. All simulations are validated by experiments with an error below 10 %, especially below 1 % under multi EHTs. A heating scheme of 13 EHTs with a radius 6 mm, a distance 5 mm, a temperature 390 °C and an uniform interval 38.5 mm is suggested after final optimization. It is hoped that this study can advance heating technology of OLED GS to achieve the desired practical level.

Higher-Order Iterative Learning Control Algorithms for Linear Systems

Iterative learning control (ILC) algorithms appeared in connection with the problems of increasing the accuracy of performing repetitive operations by robots. They use information from previous repetitions to adjust the control signal on the current repetition. Most often, information from the previous repetition only is used. ILC algorithms that use information from several previous iterations are called higher-order algorithms. Recently, interest in these algorithms has increased in the literature in connection with robotic additive manufacturing problems. However, in addition to the fact that these algorithms have been little studied, there are conflicting estimates regarding their properties. This paper proposes new higher-order ILC algorithms for linear discrete and differential systems. The idea of these algorithms is based on an analogy with multi-step methods in optimization theory, in particular, with the heavy ball method. An example is given that confirms the possibility to accelerate convergence of the learning error when using such algorithms.

Enhancing the productivity of mould shop using continuous improvement tools and simulation

In today’s competitive manufacturing environment, enhancing productivity and operational efficiency is crucial for companies to meet the rising customer demands and expectations driven by recent technological advancements. This study presents a case of productivity enhancement in mould shop manufacturing, which was experiencing bottlenecks and non-value-added (NVA) activities, leading to inefficiencies that limit the throughput. A Discrete Event Simulation (DES) model is developed for the mould shop considering the major components such as Hub, Mainframe, and Planet Carrier. Bottleneck resources are identified from the DES model. The validation of the simulation results is carried out using Pareto analysis. Root Cause Analysis (RCA) and Five Whys (5-Why) analysis are used to brainstorm the causes that lead to lower productivity. Time-reduction strategies using the Theory of Inventive Problem Solving (TRIZ) improved the throughput. The improvements are incorporated into the DES model, and updated results are obtained. As a result, throughput for the hub improved by 60% for the hub, 40% for the mainframe, and 55.88% for the planet carrier. These results show how integrating continuous improvement tools with simulation models enables streamlining operations and substantially increases productivity. The scenario reveals that Lean principles successfully overcome real-world manufacturing problems.

Heuristic approaches for a multi-mode resource availability cost problem in aircraft manufacturing

A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.

Introduction of Axisymmetric Grooves as a Tip Seal Treatment for Small-Core Turbines

Abstract The aerodynamic penalties associated with the tip gap flow in axial turbines remains a challenging problem for turbine manufacturers. As modern gas turbines with small-core architectures are brought online, the influence of the tip gap continues to grow. While technologies to reduce tip losses have been implemented into the blades, little attention has been paid to the casing around the rotor. In this study, an introduction of axisymmetric groove enhancements for the casing are examined computationally. These studies use the National Experimental Turbine (NExT) geometry, an engine-representative high-pressure turbine blade. Steady, RANS simulations are used to assess the basic characteristics of grooves, such as depth, location, and arrangement. The objective of this introductory study was to determine the feasibility of impacting the tip leakage vortex formation and the associated losses in rotor efficiency. Furthermore, analyses were done with different tip gap heights along with both flat- and squealer-tipped blades. Tip seals with a single groove are demonstrated to improve rotor aerodynamic efficiency relative to ungrooved seals by 0.4 points when applied to flat-tipped rotor blades and 0.15 points with squealer tips. Arrays of grooves show improvements for flat-tipped blade performance by 0.76 points, while having little additional aerodynamic effect on squealer tip over single-groove designs. Finally, grooved tip seals appear to exert greater influence on turbine aerodynamic performance at larger tip gaps, indicating that grooved tip seals alter the sensitivity of rotor performance to the tip clearance gap.

Deformation behavior and microstructure evolution of one novel superalloy with high rare earth gadolinium content: Multi-scale modeling and experimental study

It has been proved that rare earth elements have a positive effect on the performance of superalloys and steels at only trace levels, but rare earth phases are always tricky in the manufacturing process. In this work, one Ni–Mo–Cr-Gd alloy with ultrahigh rare earth Gd content (1.75 wt %) was designed to reveal the influence of hard and brittle Ni5Gd particles on hot deformation behavior and microstructure evolution. The Ni–Mo–Cr-Gd alloy exhibits higher deformation activation energy and smaller processable regions in the processing maps as compared with the Ni–Mo–Cr alloy. Combining electron back scatter diffraction and crystal plastic finite element method, it was found that strain concentrates around Ni5Gd particles, especially at grain boundaries, and rapidly reaches the critical value for recrystallization, which verifies particle stimulated nucleation. The recrystallization fraction decreases with strain rates from 0.01 to 1 s−1 but increases from 1 to 10 s−1, which is because meta-dynamic recrystallization occurs faster when the strain rate is greater than 1 s−1 and Ni5Gd particles promote the process indirectly by introducing more severely deformed matrix. Furthermore, the finite element modeling shows that plastic strain and temperature rise at the central region of samples increase with strain rate increasing, which can explain the unusual fragmentation of Ni5Gd at 1200 °C with the highest strain rate. Our results firstly reveal the evolution mechanism of Gd-containing superalloy during hot deformation, which can help to solve the manufacturing problems of similar alloys.

Pickup and Delivery Vehicle Routing Problem with Hybrid Missions and Third-Party Vehicle Rentals: A Case Study of Animal Feed Industry

The vehicle routing problem for small and medium-sized animal feed manufacturers is a complex challenge because each transport route can vary significantly between delivering products to customers and picking up raw materials to return to the factory. Currently, businesses lack well-planned management for utilizing a limited number of trucks. As a result, they often incur additional costs by relying on third-party vehicle rentals. This research aims to reduce transportation costs for small and medium-sized animal feed manufacturers by solving a formulated mixed-integer linear programming (MILP) model, using experimental data that accurately reflects the actual problems faced. The MILP model results help reduce transportation costs by up to 18.5% compared to current practices by optimizing route sequences and incorporating the use of third-party vehicle rentals. This also serves as a guideline for reducing transportation costs in other similar industries.

Virtual screening of drug materials for pharmaceutical tablet manufacturability with reference to sticking

The manufacturing of pharmaceutical solid dosage forms, such as tablets involves a large number of successive processing operations including crystallisation of the drug substance, granulation, drying, milling, mixing of the formulation, and compaction. Each step is fraught with manufacturing problems. Undesired adhesion of powders to the surface of the compaction tooling, known as sticking, is a frequent and highly disruptive problem that occurs at the very end of the process chain when the tablet is formed. As alternatives to the mechanistic approaches to address sticking, we introduce two different machine learning strategies to predict sticking directly from the chemical formula of the drug substance, represented by molecular descriptors. An empirical database for sticking behaviour was developed and used to train the machine learning (ML) algorithms to predict sticking characteristics from molecular descriptors. The ML model has successfully classified sticking/non-sticking behaviour of powders with 100% separation. Predictions were made for materials in the Handbook of Pharmaceutical Excipients and a subset of molecules included in the ChemBL database, demonstrating the potential use of machine learning approaches to screen for sticking propensity early during drug discovery and development. This is the first time molecular descriptors and machine learning are used to predict and screen for sticking behaviour. The method has potential to transform the development of medicines by providing manufacturability information at the drug screening stage and is potentially applicable to other manufacturing problems controlled by the chemistry of the drug substance.

A Chimera method for thermal part-scale metal additive manufacturing simulation

This paper presents a Chimera approach for the thermal problems in welding and metallic Additive Manufacturing (AM). In particular, a moving mesh is attached to the moving heat source while a fixed background mesh covers the rest of the computational domain. The thermal field of the moving mesh is solved in the heat source reference frame. The chosen framework to couple the solutions on both meshes is a non-overlapping Domain Decomposition (DD) with Neumann–Dirichlet transmission conditions.Increased steadiness and accuracy within the vicinity of the Heat Affected Zone (HAZ) are the main advantages of this approach. The steadiness gain allows for the use of larger time steps, which is vital in AM applications and, in particular, Laser Powder Bed Fusion (LPBF), where the disparity of time scales represents a major hurdle. Moreover, enhanced accuracy can be observed in the resulting morphology of the melt pool. It will be shown that the method addresses classical shortcomings pointed out by Goldak without requiring the use of an asymmetrical heat source profile.

A comparison of recent optimization algorithms for build orientation problems in additive manufacturing

Abstract Build orientation in additive manufacturing technology is a pre-process application that affects many parameters, such as the volume of the support structure, part quality, build time, and cost. Determining the optimum build orientation for one or more objectives for complex parts is an error-prone puzzle. This study evaluates the behavior of cuckoo search algorithm, differential evolution, firefly algorithm, genetic algorithm, gray wolf optimizer, Harris hawks optimization, jaya algorithm, moth flame optimizer, multi-verse optimizer, particle swarm optimization, A Sine cosine algorithm, salp swarm algorithm, and whale optimization algorithm to determine the optimum build orientation of the component to be manufactured additively. The efficiency of these algorithms is evaluated on the build orientation problem of two complex components considering undercut area and build height as objective functions. Thus, the feasibility of these algorithms for real-world additive manufacturing problems is revealed. According to results obtained from the extensive analysis, the cuckoo search algorithm is the best alternative for minimizing undercut area, considering its robustness. However, the required time to solve the problem is as much as almost twice that of other algorithms. The firefly algorithm and particle swarm optimization algorithm are the best alternatives for minimizing build height.

Computer Numerical Control Machining Simulations and Experimental Analysis of a Novel C-Gear

C-gear is a novel gear transmission system, with no existing special machining and cutting tools to realize its construction. In this study, we employed a 5-axis computer numerical control (CNC) machining center to construct a C-gear, while solving the prototype manufacturing problem at the experimental research stage. We deduced the C-gear mathematical model and studied the three-dimensional (3D) modeling methods based on the analysis of its generation principle. The C-gear processing technique using the 5-axis CNC machining center and the tool path designs were established using the VERICUT and MASTERCAM softwares, and the C-gears were experimentally constructed. The maximum overcut and residual of the gear positioning hole surface were both found to be 0.094 mm. Moreover, the maximum overcut on the tooth surface and root were 0.02 and 0.03 mm, respectively, and the maximum residual on both the tooth surface and root was 0.03 mm. The machining overcuts and residual amounts of the proposed method were better than those of reference [14]. The existing overcut and residual errors included the errors generated by surface fitting during the 3D modeling of the gears. Therefore, the machining errors of the physically constructed tooth surface are expected to be smaller than the theoretical value, suggesting a high feasibility of the proposed method to realize the machining of C-gears.

The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems

The Use of Computational Fluid Dynamics (CFD) within the Agricultural Industry to Address General and Manufacturing Problems

A Mathematical Programming Model for Minimizing Energy Consumption on a Selective Laser Melting Machine

The scheduling problem in additive manufacturing is receiving increasing attention; however, few have considered the effect of scheduling decisions on machine energy consumption. This research focuses on the nesting and scheduling problem of a single selective laser melting (SLM) machine to reduce total energy consumption. Based on an energy consumption model, a nesting and scheduling problem is formulated, and a mixed integer linear programming model is proposed. This model simultaneously determines part-to-batch assignments, part placement in the batch, and the choice of build orientation to reduce the total energy consumption of the SLM machine. The energy-saving potential of the model is validated through numerical experiments. Additionally, the effect of the number of alternative build orientations on energy consumption is explored.

Collaborative optimization for scheduling manufacturing tasks and transport vehicles considering manufacturer’s time availability in cloud manufacturing

ABSTRACT As an advanced networked intelligent manufacturing model, cloud manufacturing (CMfg) effectively integrates available resources by strengthening the connection among various manufacturers to solve complex manufacturing problems. Due to the geographical dispersion of manufacturers and the cooperativity nature of the manufacturing process, logistic impacts are non-negligible in scheduling. This paper focuses on the collaborative scheduling of manufacturing and transportation services. To make full use of the manufacturer’s capacity without affecting his benefit, local tasks are scheduled with higher priority on a manufacturer while the service time for CMfg tasks is assumed to be fragmented and represented as a set of available time windows. Based on these considerations, a collaborative optimization model for CMfg on task and vehicle scheduling with manufacturing service windows is established, in which the completion time of all CMfg orders and the total idle traveling time of vehicles are minimized to ensure the efficiency of both manufacturing and logistic service. A dual-loop variable neighborhood search algorithm is designed to solve the problem. Comprehensive experiments are conducted, showing improvements of 2.41% to 29.57% in objective and fewer vehicles needed compared with some existing methods, which validate the efficacy of the proposed model and method.

Overview and Potential Solutions of Current and Future Challenges in CMP

This talk is dedicated to the special session in honor of Prof. Babu Shrink of critical dimensions and stringent technology specifications are posing several planarization challenges in technology development (TD) and high-volume manufacturing (HVM) phases of current and advanced technology nodes. In this talk, we will focus on process challenges related to achieving (a) minimum defects (b) high planarization efficiency with less than 1nm dishing (c) tighter WIW and WID uniformities (d) ultra-smooth post-CMP surface roughness in the order of sub-nanometer. In addition, identifying appropriate endpoint control techniques combined with advanced process control strategies to provide good process margin will also be discussed. Addressing the above challenges in a cost-effective manner is vital for HVM. Root cause analysis and probable working models will be discussed for high-risk process challenges. Based on our understanding, a jist shall be provided about challenges associated with future technology nodes in logic and memory industries, with focus on how (a) technical understanding, (b) change in CMP ecosystem, and (c) AI can help solving technology development and high-volume manufacturing problems.