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A photovoltaic technology historically goes through two major steps to evolve into a mature technology. The first step involves advances in materials and is usually accompanied by the rapid improvement of power conversion efficiency. The second step focuses on interfaces and is usually accompanied by significant stability improvement. As an emerging generation of photovoltaic technology, perovskite solar cells are transitioning to the second step of their development when a significant focus shifts toward interface studies and engineering. While various interface engineering strategies have been developed, interfacial characterization is crucial to show the effectiveness of interfacial modification. Here, we review the characterization techniques that have been utilized in studying interface properties in perovskite solar cells. We first summarize the main roles of interfaces in perovskite solar cells, and then we discuss some typical characterization methodologies for morphological, optical, and electrical studies of interfaces. Successful experiences and existing problems are analyzed when discussing some commonly used methods. We then analyze the challenges and provide an outlook for further development of interfacial characterizations. This review aims to evoke strengthened research devotion on novel and persuasive interfacial engineering.

The integrated energy system (RIs) is composed of electricity/gas/cold/heat and other energy sources, which can realize the coordinated utilization of different forms of energy. RIs is conducive to promoting the rational utilization of clean energy, and the mutual coupling between different energy sources can effectively improve the overall energy utilization efficiency and operation economy of the system. The diversity of energy supply and demand makes the system operation complex and diverse, so it is indispensable to optimize the system operation scheduling. Demand response is an effective means to optimize the multi-objective scheduling of regional comprehensive energy system. RPS and green certificate trading mechanism are new methods to promote the consumption of renewable energy. In the process of green certificate trading, considering the load side demand response is conducive to realizing the maximum utilization of renewable energy power generation and improving the absorption rate of renewable energy power generation.

The performance of a layered manufacturing (LM) process is determined by the appropriate setting of process parameters. The study of the relationship between performance and process parameters is therefore an important area of LM process planning research. The trend in modern industry is to move from conventional automation to intelligent automation. LM technology is essentially an automated manufacturing technology that is evolving towards an intelligent automation technology. Slicing solid manufacturing (SSM) is a LM technique using paper as the working material and a CO2 laser as the cutting tool. In this manuscript, a back propagation (BP) learning algorithm of an artificial neural network (ANN) is used to determine appropriate process parameters for the SSM method. Key process parameters affecting accuracy are investigated. Quantitative relationships between the input parameters and output accuracy are established by developing the BP neural network.

It is now possible to generate tooling for near net shape manufacturing processes directly from a CAD database by using computer numerical control (CNC) machining or a variety of rapid prototyping (RP) processes. These methods are widely referred to as rapid tooling processes because the tool geometry is created in a relatively short time. In particular, the use of RP processes has proved to be a cost‐effective and time‐efficient approach for producing patterns and core boxes for sand casting. However, the suitability of this approach depends on a variety of geometry and process related considerations. Investigates the use of the laminated object manufacturing (LOM) based rapid tooling process in sand casting. Issues discussed include geometry considerations, error sources and propagation, and shrinkage effects. A case study illustrating time and cost savings using the LOM approach is also presented.

Processing accuracy is an important area studied in rapid prototyping (RP) research. It is mainly dependent on the processing parameters, material characteristics and many other factors. Studies on processing accuracy can be divided into two categories: in-plane processing accuracy and vertical processing accuracy (that determines the staircase on the surfaces of prototypes). This work focuses on the in-plane processing accuracy. Similar to laminated object manufacturing (LOM) process, slicing solid manufacturing (SSM) uses paper and CO2 laser as material and energy source, respectively. This paper introduces an integrated method that combines orthogonal experimental design and analysis, and neural network analysis to determine the optimal processing conditions. The key processing parameters and their degree of influences on the processing accuracy, and the quantitative relations between input parameters and output accuracy will be investigated. This method of experimental design and analysis is not only effective for the SSM process, but also applicable to other RP processes that use the principle of 3D layered manufacture.

Fast freeform fabrication, also called rapid prototyping, is the automatic manufacturing techniques using slicing and additive processes. The first techniques for fast freeform fabrication became available in the late 1980s and were used to produce models and prototype parts. With the fast freeform fabrication method, the machine reads in data from a CAD drawing, slicing the CAD model into a thin, virtual, horizontal cross-sections and lays down successive layers of liquid, powder, or sheet material, and in this way builds up the model from a series of cross sections. These layers, which correspond to the virtual cross section from the CAD model, are joined together or fused automatically to create the final shape. Figure 3.1 shows the principles of this slicing and additive manufacturing technology. The primary advantage to this slicing and additive fabrication is its ability to create almost any shape or geometric feature.

Dimensional accuracy and variability are critical factors in the casting process that must be considered at each stage of the process. Dimensional accuracy is an indication of how close a casting dimension is to design intent (the actual target value). Dimensional accuracy is often referred as a system error. The main causes for poor dimensional accuracy in sand casting are pattern equipment errors, pattern wear, and casting contraction uncertainty. When properly understood and controlled, system error can often be corrected before production runs. Dimensional variability is the variation of individual casting dimensions about their mean. Dimensional variability is often referred to as random error. Many foundry process variables such as placement of cores and sand consistency contribute to dimensional variability.

Evaluation of tooling alternatives is the comprehensive mental exercise involved in selecting the most efficient way to produce a sand casting pattern. Selection of the proper tooling process can significantly effect the time, cost and accuracy of castings involved. Selection of an appropriate tooling alternative is typically performed by the tool builder. The process requires a sound understanding of the interactions between casting geometry and pattern design, required tooling properties, production volume, available shop capacity, available processes and process capability, and technical capabilities of the foundry involved.

Typically, the sand-casting tool design and fabrication process begins when the tool builder receives the part design from the client or design engineer. In general, the part design can be communicated in variety of ways such as by a physical part, a 2D engineering drawing, a 3D computer generated solid model, or other means that sufficiently conveys the design intent needed for tooling design and fabrication.

Sand casting is one of several near net shape manufacturing processes. In near net shape manufacturing, multiple copies of the product are produced by imprinting the shape of the tool or die on a suitable working material. Typically, the working material starts out as a liquid, powder, or pliable material that is eventually solidified or hardened after being formed by the tool. Net shape manufacturing processes include other casting processes such as die casting and investment casting, polymer molding processes such as injection molding, blow molding, and thermoforming, bulk deformation processes such as extrusion and forging, and sheet metal forming processes such as bending and deep drawing. Design and construction of tooling for near net shape manufacturing processes has traditionally been time consuming and costly. With the advent of modern computer-aided machining and fast freeform fabrication (FFF) technologies, many new and innovative approaches to making near net shape tooling have been developed. This has led to the rapidly expanding new field known as “rapid tooling.”

Sand casting is a mold based net shape manufacturing process in which metal parts are molded by pouring molten metal into a cavity. The mold cavity is created by withdrawing a pattern from sand that has been packed around it. Since the pattern imprint forms the cavity, the pattern creates the external shape of the cast part. If the part has undercuts or hollow internal regions, these can be formed by sand cores that are fabricated separately and then placed in the mold cavity. The cores are supported by core prints, and/or chaplets that allow the molten metal to flow between the core and the mold wall. In addition, cores may be necessary to produce a desired “zero” draft external surface, depending on the parting line selected. The parting line is formed by the interface between the cope (upper portion) and drag (lower portion) of the mold. The separate cope and drag are necessary to allow the pattern to be withdrawn from the sand and to allow the cores to be properly positioned within the mold.

The tool path evaluation form shown in Table 7.1 is a simple form that allows a sand casting tool design professional to quickly estimate the best tooling alternative selection for a given casting geometry as a function of production volume. The form also allows the tooling professional to document the key considerations driving the selection. It should be noted that the “key tool considerations and issues” include the decision factors discussed in Chapter 6.