Machine vision based statistical process control in fused deposition modeling

The paper describes the application of statistical process control (SPC) methods to supervise the control of a noisy process. SPC charts form a supervisory unit that monitors process outputs and selectively applies terms of a conventional PID controller. The advantages of this method lie in the ability of the control loop to distinguish between process noise and genuine error signals. This stops the controller reacting unnecessarily reducing actuator work. Results are presented for the control of a steam/water heat exchanger rig and show the effectiveness of this supervisory unit to produce quieter control without creating a lag in the control loop. >

Radiation Portal Monitors (RPM) acquire sequential measurements of gamma activity that can be modeled as a sequence of Poisson distributed random numbers. These series can be analyzed with Statistical Process Control (SPC) methods to trigger alarms when the statistical distribution of the ambient dose during a transit changes. Some usual SPC methods have been compared and modified to narrow the time required to bound an alarm. Both the probabilities of detection and the Average Run Length (ARL) are used and fitted to curves to benchmark the SPC methods, which are ranked as: SR, EWMA, CUSUM, SPRT, FLR, and Shewhart.

The unique tool-less and layer-upon-layer capabilities of additive manufacturing (AM) have largely lessened manufacturing constraints and significantly broadened design freedom. AM offers new opportunities for developing customized products by achieving significant improvements in product performances and lower overall costs. The AM-enabled customization opportunities inevitably challenge traditionally design theories and methodologies (DTMs) that lack design knowledge for AM products. Here, to counteract this limitation, this study presents a novel methodology for constructing knowledge supporting DTMs for AM-enabled customization: customized design for AM (CDfAM). The CDfAM supports two main phases of design for additive manufacturing (DfAM): (1) opportunistic DfAM in a conceptual design phase and (2) restrictive DfAM in an embodiment design phase. In terms of the knowledge construction for the opportunistic DfAM, it is essential to construct knowledge that supports to explore a customized design space enabled by AM’s design freedom from the conceptual design phase. To address the challenge, this study provides a novel method for capturing knowledge of customized AM product behaviors. First, this study proposes an affordance-based DfAM framework by integrating the concept of affordance into three link chain models. Second, an affordance structure is proposed to develop a formal method for representing the knowledge of the customized AM product behaviors utilizing finite state automata. In terms of the knowledge construction for the restrictive DfAM, this study presents a formal method for representing and reasoning customizable modular knowledge of design rules for AM. First, a novel structured modular hierarchy and associated formalisms of AM design rule elements are presented. Second, machine learning is employed to automatically learn design rules from AM data, which leverages the proposed design rule formalisms. A knowledge graph is designed and built to demonstrate the proposed methodology. Case studies with AM products such as a transtibial prosthesis leg and AM data such as ex-situ AM measurement data provide illustrations in this study. The case studies show that the proposed methodology explicitly constructs structured and correlated AM design knowledge of customized AM product behaviors, product geometries, and additive manufacturability constraints. It is also shown that qualified design knowledge in the knowledge graph enhances automation of DfAM decision-making and data-driven knowledge constructions. The proposed CDfAM methodology enables incorporating both customized AM product functionalities and additive manufacturability constraints into product designs. This study provides a foundation for AM knowledge construction and data- and knowledge-driven automation of DfAM decision-making. Validated design rules and representations are expected to contribute to developing international standards on AM design rules.

Additive manufacturing (AM) refers to a group of manufacturing techniques that produce components by melting and bonding material powders in a layer-by-layer fashion. By virtue of its capability of producing parts with complex geometry and functionally graded materials, AM is leading the charge of the “third industrial revolution” and has attracted great attention in multiple industrial sectors, such as manufacturing, healthcare, aerospace, and others. Sustainability of AM remains an open question. AM is inherently an energy expensive process and may be energy inefficient as compared to the traditional manufacturing process. Thus, there exists an urgent need to identify the key influence factors and quantify the energy consumption during AM production. The proposed study aims to obtain a preliminary understanding of the impact of part surface geometry on AM energy consumption. The study addresses the effect of part geometry on AM energy consumption through experimental design method. Part geometry consists of two level meanings, part surface area and part surface complexity. The study utilizes a MakerGear M2 fused deposition modeling (FDM) 3D printer to complete the designed experiments. By implementing experimental design and statistical analysis technologies, the study firstly identifies the correlation between part geometry and AM energy consumption. The result shows that part surface area is positively correlated with AM energy consumption and no significant statistical evidence to support that part surface complexity is associated with AM energy consumption. Such findings are of significance to AM energy consumption in terms of both qualitative and quantitative analysis. In addition, the study has significant potentials to guide the future AM energy consumption model development and to be extended to future AM process improvement.

The design methodologies and part shape algorithms for additive manufacturing (AM) are rapidly growing fields, proven to be of critical importance for the uptake of additive manufacturing of parts with enhanced performance in all major industrial sectors. The current trend for part design is a computationally driven approach where the parts are algorithmically morphed to meet the functional requirements with optimized performance in terms of material distribution. However, the manufacturability restrictions of AM processes are not considered at the primary design phases but at a later post-morphed stage of the part’s design. This paper proposes an AM design method to ensure: (1) optimized material distribution based on the load case and (2) the part’s manufacturability. The buildability restrictions from the direct energy deposition (DED) AM technology were used as input to the AM shaping algorithm to grant high AM manufacturability. The first step of this work was to define the term of AM manufacturability, its effect on AM production, and to propose a framework to estimate the quantified value of AM manufacturability for the given part design. Moreover, an AM design method is proposed, based on the developed internal stresses of the build volume for the load case. Stress tensors are used for the determination of the build orientation and as input for the part morphing. A top-down mesoscale geometric optimization is used to realize the AM part design. The DED Design for Additive Manufacturing (DfAM) rules are used to delimitate the morphing of the part, representing at the same time the freeform mindset of the AM technology. The morphed shape of the part is optimized in terms of topology and AM manufacturability. The topology optimization and AM manufacturability indicator (TMI) is introduced to screen the percentage of design elements that serve topology optimization and the ones that serve AM manufacturability. In the end, a case study for proof of concept is realized.

Statistical process control (SPC) methods have been extensively applied to monitor the quality performance of manufacturing processes to quickly detect and correct out-of-control conditions. As sensor and measurement technologies advance, there is a continual need to adapt and refine SPC methods to effectively and efficiently use these new data-sets. One of the most state-of-the-art dimensional measurement technologies currently being implemented in industry is the 3D laser scanner, which rapidly provides millions of data points to represent an entire manufactured part's surface. Consequently, this data has a great potential to detect unexpected faults, i.e., faults that are not captured by measuring a small number of predefined dimensions. However, in order for this potential to be realized, SPC methods capable of handling these large data-sets need to be developed. This paper presents an approach to performing SPC using point clouds obtained through a 3D laser scanner. The proposed approach transforms high-dimensional point clouds into linear profiles through the use of Q---Q plots, which can be monitored by well established profile monitoring techniques. In this paper point clouds are simulated to determine the performance of the proposed approach under varying fault scenarios. In addition, experimental studies were performed to determine the effectiveness of the proposed approach using actual point cloud data. The results of these experiments show that the proposed approach can significantly improve the monitoring capabilities for manufacturing parts that are characterized by complex surface geometries.

Functionally graded materials (FGMs) are advanced materials that are used to prevent the problems that arise when composite materials with sharp interfaces are used in a harsh working environment, which includes stress singularities due to property mismatchs, poor adhesion, and delamination. A number of conventional manufacturing processes have been used to produce functionally graded materials and some of these manufacturing processes were presented in Chap. 3. Additive manufacturing (AM) technology is an advanced manufacturing process that offers many advantages and possibilities for the fabrication of complex three-dimensional products through material addition, as against the material removal in the conventional machining processes. Some of the AM technologies have the capability of fabricating complex parts that are made with the functionally graded material (FGM) in a single manufacturing process. Some of the AM technologies that are used to produce functional FGM parts include: selective-laser sintering, selective-laser melting, the laser-metal deposition process, and fused-deposition modelling. These AM technologies are presented in this chapter; and some of the research work using these technologies for the fabrication of FGMs are also reviewed.

Six sigma process improvement in the emergency department: A pilot study using statistical process control methods to analyze ecg times in the emergency department

Quality is a benchmark to determine the level of good and bad of a product. The level of quality of a product will affect customer satisfaction, hence, to produce high quality products, it is necessary for a company to have a quality control process. Quality control is a process that aims to maintain the quality of products and services that have been promised to consumers. In this study, quality control is carried out in the sugar packaging process. Where in the sugar packaging process found the resulting product has a poor quality, defective and not in accordance with specifications. This study uses the Statistical Process Control (SPC) method which aims to determine the causes of defective products with the intention that the packaging process can further minimize the level of product defects. The SPC method is a statistical analysis technique with seven statistical tools or seven tools. Based on the results of the study it can be seen that the cause of product damage / defects in the product packaging process is caused by three types of damage namely damage due to conveyor (38.17%), damage due to machine pinched (35.82%), and damage due to loose seams (26,00%) This type of damage can be caused by human error and other factors such as engine condition, engine cleanliness and the monitoring process. Proposed improvements recommended for the company are to provide training to employees, make clear work instructions, conduct periodic maintenance for the machines used, supervise all work areas, and carry out quality control for every acceptance of raw materials.

Biodiesel is a promising alternative fuel that can be obtained from vegetable oils or animal fats through transesterification reactions with alcohol. Biodiesel has numerous advantages compared to diesel oil, namely: it is a renewable energy resource, it is not toxic, and it is environmentally friendly because the raw material does not contain sulfur and low emissions. Biodiesel production is inseparable from the availability of raw materials until now the raw materials that meet the needs of production capacity are palm oil (CPO). To overcome this problem, in this study we want to find out how much biodiesel conversion percentage if we use other raw materials such as: CPO oil, corn oil, VCO oil, and waste cooking oil. The analysis used in this study was the analysis of the use of raw materials to the value of biodiesel conversion using the Statistical Process Control (SPC) method. SPC method was used to analyze, manage, control, and improve a product and process using statistics. The objective of this study was to produce high conversion percentage biodiesel and analyze and control the quality of research products. By having this statistical method, it could be found errors in a study or out of control production so that further action can be taken to overcome them. In this study, it was found that the product which was outside of the control limit was biodiesel made from CPO and waste cooking oil. The results of the analysis using cause and effect diagrams could determine the causes of damage in the production process, which come from the factors of workers/humans, production machines, working methods, materials/raw materials and work environment.

Fundamentals of Mechanical Design and Analysis for AM Fabricated Parts

A void closure model based on hydrostatic integration and the Lode parameter for additive manufacturing AlSi10Mg

Moving towards process-based radiotherapy quality assurance using statistical process control

3D printing, also known as Additive manufacturing (AM), has emerged as a transformative technology with applications across various industries, including the medical sector. This review paper provides an overview of the current status of AM technology, its challenges, and its application in the medical industry. The paper covers the different types of AM technologies, such as fused deposition modeling, stereolithography, selective laser sintering, digital light processing, binder jetting, and electron beam melting, and their suitability for medical applications. The most commonly used biomedical materials in AM, such as plastic, metal, ceramic, composite, and bio-inks, are also viewed. The challenges of AM technology, such as material selection, accuracy, precision, regulatory compliance, cost and quality control, and standardization, are also discussed. The review also highlights the various applications of AM in the medical sector, including the production of patient-specific surgical guides, prosthetics, orthotics, and implants. Finally, the review highlights the Internet of Medical Things (IoMT) and artificial intelligence (AI) for regulatory frameworks and safety standards for 3D-printed biomedical devices. The review concludes that AM technology can transform the healthcare industry by enabling patients to access more personalized and reasonably priced treatment alternatives. Despite the challenges, integrating AI and IoMT with 3D printing technology is expected to play a vital role in the future of biomedical device applications, leading to further advancements and improvements in patient care. More research is needed to address the challenges and optimize its use for medical applications to utilize AM's potential in the medical industry fully.

Over the past few years additive manufacturing (AM) technology has grown in popularity as companies explore its potential. Applying layer upon layer of polymers can create objects of almost any shape and geometry guided by design files, and now, recent developments have made it possible to print metal parts and components, making it a potentially disruptive innovation for the supply chain. AM has already had an impact on other industries such as aviation—Air-bus agreed in October to a deal to manufacture polymer parts for use on its A350 XWB aircraft—and now, as oil and gas companies look to adopt AM into their supply chain management, service companies are breaking through with new machines and processes that may facilitate larger-scale production of parts and components in the future. In addition, a new guideline has been established to help bridge the gap between the quality assurance of parts created by an AM process and those created through traditional manufacturing processes. Additive Manufacturing Qualification In November, DNV GL published its first classification guideline for the use of AM in maritime and oil and gas. The guideline was designed to bridge the gap between the quality assurance of parts and components created by an AM process and those created through traditional manufacturing processes by creating a clear pathway for AM certification. The guideline serves as an umbrella document that establishes the overall framework process for qualification and certification of AM products and further points to other DNV specific services/documents that are relevant in assessing parameters that will impact the final product, from the materials used to the actual printing and post-processing. Brice Le Gallo, DNV’s regional manager for Southeast Asia and Australia, said the lack of a guideline was one of the more significant hurdles for more widespread adoption of AM processes. Because there are currently only a handful of AM standards, companies often conduct their own testing to ensure integrity of the equipment, parts, and components. The cost and time needed to conduct these tests deters wider adoption. In addition, AM processes typically incur higher material and machining costs than conventional manufacturing. He said the new guideline will help provide a blueprint that may encourage further adoption. “Today what we see is that the level of adoption in these industries, maritime and offshore oil and gas, is still quite low. One of the areas where we can really help the industry get the level of adoption increased is on the qualification side,” Le Gallo said. “This is one of the most important hurdles. We think that by bringing appropriate qualification frameworks to the industry, we will see an important increase in the level of adoption. This would then lead to greater volume, which in turn would lead to costs going down.”