德国LAP激光模板系统:CONCRETEPRO提升混凝土生产效率与精度

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CONCRETEPRO是一款由德国LAP公司生产的激光投影系统,专为混凝土成品件的生产而设计。该系统的核心特点是利用激光将工程图纸(CAD数据)以1:1的比例准确地投影到工作表面和工件上,如天花板、墙壁、阳台等,显著提升生产效率和精度。相比于传统的机械模板、彩色绘图仪或其他测量工具,CONCRETEPRO提供了显著的优势。 首先,作为一款"3合1"解决方案,CONCRETEPRO包括投影机、软件和专业服务,实现了设计、执行和维护的一体化,简化了操作流程。它的精确度极高,特别适合快速装配货盘,节省了大约25%的工作时间,使得生产过程更为高效。 其次,CONCRETEPRO通过激光投影实现“光速”操作,避免了手动测量和调整的繁琐,极大地提高了生产灵活性。系统允许用户通过远程控制系统逐步调用和处理投影文件,甚至可以用不同颜色区分不同的操作步骤,便于实时监控和检查生产进度。 激光投影的另一个关键特性是视口功能,允许用户对工作区域进行分区控制,通过PC鼠标选择并突出显示特定的截面,增强了关键部位的可见性,有助于质量控制。 此外,CONCRETEPRO的负载平衡功能智能分配投影文件,确保在整个投影区域内的均匀和稳定,即使在区域重叠的情况下也能保持最快的投影速度。这使得整体生产过程更加顺畅,降低了误差发生的可能性。 相较于彩色绘图仪,CONCRETEPRO具有更高的性价比。虽然初始投资可能较低,但考虑到节省的时间和提高的质量,通常几个月就能回收成本,长期来看更具经济性。 CONCRETEPRO激光投影系统是混凝土制品生产中的革命性工具,它以高精度、高效能和易用性引领了行业的发展,为企业和个人带来了显著的生产效益。

生成torch代码:class ConcreteAutoencoderFeatureSelector(): def __init__(self, K, output_function, num_epochs=300, batch_size=None, learning_rate=0.001, start_temp=10.0, min_temp=0.1, tryout_limit=1): self.K = K self.output_function = output_function self.num_epochs = num_epochs self.batch_size = batch_size self.learning_rate = learning_rate self.start_temp = start_temp self.min_temp = min_temp self.tryout_limit = tryout_limit def fit(self, X, Y=None, val_X=None, val_Y=None): if Y is None: Y = X assert len(X) == len(Y) validation_data = None if val_X is not None and val_Y is not None: assert len(val_X) == len(val_Y) validation_data = (val_X, val_Y) if self.batch_size is None: self.batch_size = max(len(X) // 256, 16) num_epochs = self.num_epochs steps_per_epoch = (len(X) + self.batch_size - 1) // self.batch_size for i in range(self.tryout_limit): K.set_learning_phase(1) inputs = Input(shape=X.shape[1:]) alpha = math.exp(math.log(self.min_temp / self.start_temp) / (num_epochs * steps_per_epoch)) self.concrete_select = ConcreteSelect(self.K, self.start_temp, self.min_temp, alpha, name='concrete_select') selected_features = self.concrete_select(inputs) outputs = self.output_function(selected_features) self.model = Model(inputs, outputs) self.model.compile(Adam(self.learning_rate), loss='mean_squared_error') print(self.model.summary()) stopper_callback = StopperCallback() hist = self.model.fit(X, Y, self.batch_size, num_epochs, verbose=1, callbacks=[stopper_callback], validation_data=validation_data) # , validation_freq = 10) if K.get_value(K.mean( K.max(K.softmax(self.concrete_select.logits, axis=-1)))) >= stopper_callback.mean_max_target: break num_epochs *= 2 self.probabilities = K.get_value(K.softmax(self.model.get_layer('concrete_select').logits)) self.indices = K.get_value(K.argmax(self.model.get_layer('concrete_select').logits)) return self def get_indices(self): return K.get_value(K.argmax(self.model.get_layer('concrete_select').logits)) def get_mask(self): return K.get_value(K.sum(K.one_hot(K.argmax(self.model.get_layer('concrete_select').logits), self.model.get_layer('concrete_select').logits.shape[1]), axis=0)) def transform(self, X): return X[self.get_indices()] def fit_transform(self, X, y): self.fit(X, y) return self.transform(X) def get_support(self, indices=False): return self.get_indices() if indices else self.get_mask() def get_params(self): return self.model

2023-04-21 上传

创建混凝土材料 uniaxialMaterial Concrete01 1 -4 -0.002 0.0 -0.005 0.0 -0.1 -30.0 -0.15 -60.0 -0.3 -120.0 ;# 混凝土材料标签1,使用 Concrete01 材料模型 创建钢筋材料 uniaxialMaterial Steel02 2 420000 9 0.01 0.925 ;# 钢筋材料标签2,使用 Steel02 材料模型 整体截面标签 set sectionTag 1 定义墩高和直径 set h 39.5 ;# 墩高 set diaTop 1.7 ;# 上部直径 set diaBottom 1.9 ;# 下部直径 set cover 0.1 ;# 保护层厚度 计算上下部分长度 set lenTop [expr {($h-2*$cover)/2}] set lenBottom 设置纤维区域参数 set nf 32 ;# 纤维数量 set startAng 0.0 ;# 纤维区域开始的角度 set endAng 360.0 ;# 纤维区域结束的角度 创建上部圆形纤维区域 section Fiber $sectionTag ;# 创建纤维截面 patch circ 1 $nf $cover $startAng $endAng 0.0 0.0 $lenTop ;# 创建圆形纤维区域 创建下部圆形纤维区域 set nfBottom [expr {int($nf*$lenBottom/$lenTop)}] ;# 根据长度比例计算下部纤维数量 patch circ 1 $nfBottom $cover $startAng $endAng 0.0 0.0 $lenBottom ;# 创建圆形纤维区域 创建钢筋 set numSteel 32 ;# 钢筋数量 set steelSize 0.01 ;# 钢筋直径 layer straight 2 $numSteel $steelSize 0.0 0.0 $lenTop 0.0 ;# 上部钢筋 layer straight 2 $numSteel $steelSize 0.0 0.0 $lenBottom 0.0 ;# 下部钢筋 创建节点 set startNodeId 1 set endNodeId [expr {$startNodeId + 15}] set nodeId $startNodeId 创建墩节点 node $nodeId 29.980000 1.500000 -44.050000 incr nodeId node $nodeId 29.980000 8.100000 -44.050000 incr nodeId node $nodeId 29.980000 1.500000 -40.050000 incr nodeId node $nodeId 29.980000 8.100000 -40.050000 incr nodeId node $nodeId 29.980000 1.500000 -34.050000 incr nodeId node $nodeId 29.980000 8.100000 -34.050000 incr nodeId node $nodeId 29.980000 1.500000 -28.050000 incr nodeId node $nodeId 29.980000 8.100000 -28.050000 incr nodeId node $nodeId 29.980000 1.500000 -22.050000 incr nodeId node $nodeId 29.980000 8.100000 -22.050000 incr nodeId node $nodeId 29.980000 1.500000 -16.050000 incr nodeId node $nodeId 29.980000 8.100000 -16.050000 incr nodeId node $nodeId 29.980000 1.500000 -10.050000 incr nodeId node $nodeId 29.980000 8.100000 -10.050000 incr nodeId node $nodeId 29.980000 1.500000 -4.550000 incr nodeId node $nodeId 29.980000 8.100000 -4.550000 创建墩的单元 set startElemId 1 set endElemId [expr {$startElemId + 7}] set elemId $startElemId 创建坐标转换 geomTransf Linear 1 ;# 创建线性坐标转换,标签1 创建墩的单元 element nonlinearBeamColumn $elemId 1 2 5 $sectionTag 1 element nonlinearBeamColumn [expr {$elemId+1}] 2 3 5 $sectionTag 1 element nonlinearBeamColumn [expr {$elemId+2}] 3 4 5 $sectionTag 1 element nonlinearBeamColumn [expr {$elemId+3}] 4 5 5 $sectionTag 1 element nonlinearBeamColumn [expr {$elemId+4}] 5 6 5 $sectionTag 1 element nonlinearBeamColumn [expr {$elemId+5}] 6 7 5 $sectionTag 1 element nonlinearBeamColumn [expr {$elemId+6}] 7 8 5 $sectionTag 1 以上代码中缺少截面组装的部分,请你补充完整

2023-07-11 上传

The programme should have the following features: ● A menu including Open and Exit where Open starts a JFileChooser to select the file with the questions inside and Exit ends the programme. ● Once a file is loaded, the GUI should display one question and its answers at a time. ● The user should be able to select an answer and they should be informed if they were correct or not. ● The user should be made aware of the number of correctly answered and the total number of questions answered. ● The user should only be able to proceed to the next question once they answered the current one. ● Once all questions have been answered, the user should be informed of their overall score and that the game has finished. The Open menu item should now be enabled to start a new quiz. Optionally, you can add a restart menu item to redo the current quiz. Concrete sub-tasks: a) define a class called Question to hold a single question, i.e. the text, the possible answers, and the correct answer index; (0.25P) b) write a method to select a file via a JFileChooser and to read all the questions from that file into an array/list of Question objects (assume that file has the structure mentioned above); (0.25P) c) design and implement a GUI with the components mentioned above: A menu, ability to display the question and answers, ability to select an answer, show the outcome and score, and proceed to the next question. (Appropriate layout: 1P, Class extends JFrame: 0.25P, Class follows OOP principles: 0.25P, Global set-up in main method: 0.25P)1 d) write a method to display a question on the GUI you designed; (0.25P) e) implement an actionPerformed method to respond to user interactions with the GUI. Make sure to enable and disable interactive components as required, e.g. the user should not be able to skip to the next question without selecting an answer first and they should not be able to load a new quiz before finishing the current one;

2023-05-29 上传

File "D:\03lyf\代码\seq2point_train.py", line 188, in <module> history = model.fit(train_datagen,epochs=n_epoch, File "D:\py\Anaconda3\envs\代码\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler return fn(*args, **kwargs) File "D:\py\Anaconda3\envs\代码\lib\site-packages\keras\engine\training.py", line 1685, in fit tmp_logs = self.train_function(iterator) File "D:\py\Anaconda3\envs\代码\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 150, in error_handler return fn(*args, **kwargs) File "D:\py\Anaconda3\envs\代码\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 894, in __call__ result = self._call(*args, **kwds) File "D:\py\Anaconda3\envs\代码\lib\site-packages\tensorflow\python\eager\polymorphic_function\polymorphic_function.py", line 926, in _call return self._no_variable_creation_fn(*args, **kwds) # pylint: disable=not-callable File "D:\py\Anaconda3\envs\代码\lib\site-packages\tensorflow\python\eager\polymorphic_function\tracing_compiler.py", line 143, in __call__ return concrete_function._call_flat( File "D:\py\Anaconda3\envs\代码\lib\site-packages\tensorflow\python\eager\polymorphic_function\monomorphic_function.py", line 1757, in _call_flat return self._build_call_outputs(self._inference_function.call( File "D:\py\Anaconda3\envs\代码\lib\site-packages\tensorflow\python\eager\polymorphic_function\monomorphic_function.py", line 381, in call outputs = execute.execute( File "D:\py\Anaconda3\envs\代码\lib\site-packages\tensorflow\python\eager\execute.py", line 52, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, KeyboardInterrupt

2023-06-13 上传